diff --git a/.github/workflows/DCE.yml b/.github/workflows/DCE.yml
index 54f4499..4da723d 100644
--- a/.github/workflows/DCE.yml
+++ b/.github/workflows/DCE.yml
@@ -1,34 +1,40 @@
-name: Bash script
+name: Run DCE then build docker
 
 on:
   push:
-    branches: [ "freesurfer", "main" ]
+    branches: [ "dev", "main" ]
   pull_request:
-    branches: [ "freesurfer", "main" ]
+    branches: [ "dev", "main" ]
 
 permissions:
   contents: read
 
 jobs:
-  build:
+  run:
 
     runs-on: self-hosted
 
     steps:
-    - uses: actions/checkout@v3
-    - uses: actions/checkout@v3
+    - name: Checkout
+      uses: actions/checkout@v5
+
+    - name: Checkout ROCKETSHIP repo
+      uses: actions/checkout@v5
       with:
         repository: petmri/ROCKETSHIP
         ref: dev
         path: ROCKETSHIP
+
     #- name: Set up Python 3.10
     #  uses: actions/setup-python@v3
     #  with:
     #    python-version: "3.10"
+
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        pip install --upgrade hd-bet
     #    pip install flake8 pytest
         
         
@@ -38,29 +44,71 @@ jobs:
    #     flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
    #     # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
    #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
     #- name: Test with pytest
     #  run: |
     #    pytest
+
     #- name: Set up MATLAB
     #  uses: matlab-actions/setup-matlab@v1
-    - name: Clean the runner subject data
-      run: |
-        cd /media/network_mriphysics/USC-PPG/RUNNER_DATA/1101743_runner/1st_timepoint
-        shopt -s extglob
-        rm -f !(2.nii|5.nii|10.nii|12.nii|15.nii|DCE.nii|aif.nii)
-        cd /home/mrispec/actions-runner/_work/in-house_toolbox/in-house_toolbox
         
     - name: Preprocess the data
-      run:  ./preprocess_all.sh -d /media/network_mriphysics/USC-PPG/RUNNER_DATA -b -Z
+      run:  ./preprocess_all.sh -d /media/network_mriphysics/USC-PPG/RUNNER_DATA/rawdata -b -Z -m -c
       
     - name: Run DCE
-      run:  ./DCE_all.sh -d /media/network_mriphysics/USC-PPG/RUNNER_DATA -b -Z
+      run:  ./DCE_all.sh -d /media/network_mriphysics/USC-PPG/RUNNER_DATA/rawdata -f
     
+    - name: Output logs
+      run:  |
+        date=$(date +"%Y-%m-%d")
+        cat /media/network_mriphysics/USC-PPG/RUNNER_DATA/derivatives/logs/preprocessing_log_${date}.txt
+        cat /media/network_mriphysics/USC-PPG/RUNNER_DATA/derivatives/logs/dce_log_${date}.txt
+
+    - name: Convert HTML to PDF
+      run:  wkhtmltopdf --enable-local-file-access /media/network_mriphysics/USC-PPG/RUNNER_DATA/derivatives/sub-1101743/ses-01/reports/sub-1101743_ses-01_desc-casereport.html /media/network_mriphysics/USC-PPG/RUNNER_DATA/sub-1101743/ses-01/reports/sub-1101743_ses-01_desc-casereport.pdf
+      continue-on-error: true
+
     - name: Upload analysis results
-      uses: actions/upload-artifact@v3.1.0
+      uses: actions/upload-artifact@v4
       with:
-        name: T1_Ktrans_graphs
-        path: /media/network_mriphysics/USC-PPG/RUNNER_DATA/1101743_runner/1st_timepoint/report.png
-        retention-days: 5
+        name: HTML_report
+        path: |
+          /media/network_mriphysics/USC-PPG/RUNNER_DATA/derivatives/dceprep/sub-1101743/ses-01/reports/sub-1101743_ses-01_desc-casereport.html
+          /media/network_mriphysics/USC-PPG/RUNNER_DATA/derivatives/dceprep/sub-1101743/ses-01/reports/sub-1101743_ses-01_desc-casereport.pdf
+          /media/network_mriphysics/USC-PPG/RUNNER_DATA/derivatives/dceprep/sub-1101743/ses-01/figures/
+        retention-days: 7
         if-no-files-found: error
 
+  docker:
+    needs: run
+    strategy:
+      fail-fast: false
+      matrix:
+        release: [R2020a, R2020b, R2021a, R2021b, R2022a, R2022b, R2023a]
+
+    runs-on: self-hosted
+
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Extract branch name
+      id: extract_branch
+      shell: bash
+      run: echo "branch=$(echo ${GITHUB_REF#refs/heads/})" >> "$GITHUB_ENV"
+
+    - name: Build Dockerfile
+      id: docker_build
+      run: |
+        docker image prune -f
+        docker build --build-arg MATLAB_RELEASE=${{ matrix.release }} . -t lsaca05/dce:${{ matrix.release }}-$branch
+
+    # - name: Run Docker
+    #   if: ${{ matrix.release }} == "R2020a"
+    #   run: |
+    #     ./run_docker.sh
+    #     exit
+
+    # - name: Push to Docker Hub
+    #   if: steps.docker_build.outcome == 'success'
+    #   run: |
+    #     docker push lsaca05/dce:${{ matrix.release }}-$branch
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index a9af213..e74e283 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,4 @@
 .project
 .pydevproject
+docker/files/license.txt
+__pycache__
\ No newline at end of file
diff --git a/DCE_MRI_process_all.sh b/DCE_MRI_process_all.sh
deleted file mode 100755
index 367011e..0000000
--- a/DCE_MRI_process_all.sh
+++ /dev/null
@@ -1,508 +0,0 @@
-#!/bin/bash
-# FSL, AFNI, Matlab, ROCKETSHIP + parametric_scripts, and Python are required
-# Within parametric_scripts should be a custom scripts folder with T1mapping_fit.m
-# control variables
-EN_Z_NORM=0
-EN_BIAS1=0
-EN_BIAS2=0
-ff=0
-#EN_MOTION_CORR=1
-
-# make this your main data directory or pass it as an option to -d
-#DATA_DIR=/media/network_mriphysics/USC-PPG/data
-
-# options
-while getopts ":d:bBZFh" options; do
-	case "${options}" in
-		b)
-			EN_BIAS1=1
-			;;
-		B)	EN_BIAS2=1
-			;;
-		d)
-			DATA_DIR=${OPTARG}
-			;;
-		F)
-			ff=1
-			;;
-		h)
-			echo "This script runs through all subject folders of a specified main data directory, processing every folder ending in '_timepoint'."
-			echo "-b: enable first round of bias field corrections"
-			echo "-B: enable second round of bias field corrections, post-Z-norm if enabled"
-			echo "-Z: enable Z-slice normalization"
-			echo "-d: specify main data directory containing all subject folders"
-			echo "-F: fail fast, any command failures will end the script"
-			echo "-h: display this message"
-			exit 0
-			;;
-		Z)
-			EN_Z_NORM=1
-			;;
-	esac
-done
-
-if [ -z "$DATA_DIR" ]
-	then
-		echo "ERROR: Please use '-d [dir_path]' to pass the path to your main data directory to this script."
-		exit 1
-fi
-cd $DATA_DIR
-ROCKETSHIP_PATH=$(find $HOME -name '*run_dce_auto.m' -printf '%h\n' -quit)
-GPUFIT_PATH=$(find $HOME -type d -name Gpufit-build)
-SCRIPT_PATH=$(find $HOME -name '*auto_analysis.py' -printf '%h\n' -quit)
-#SCRIPT_PATH=/home/mrispec/Code/in-house_toolbox
-
-# Run bias correction on VFA data 
-# ------------------------------
-for dir in */*_timepoint/; do
-	date
-	echo Processing ${dir}...
-	 # /home/raghav/data/203/1st_timepoint
-	SUBJECT_TP_PATH=$(realpath $dir)
-	
-	cd $dir
-	# FSL brain mask extraction from VFA 2 image
-	bet 2.nii brain.nii -R -m -f 0.45 -g 0 -Z
-	fslcpgeom 2.nii brain_mask.nii
-			
-	# FAST documentation recommends brain masking first
-	fslmaths 2.nii -mas brain_mask.nii.gz 2_masked.nii 
-	fslmaths 5.nii -mas brain_mask.nii.gz 5_masked.nii
-	fslmaths 10.nii -mas brain_mask.nii.gz 10_masked.nii
-	fslmaths 12.nii -mas brain_mask.nii.gz 12_masked.nii
-	fslmaths 15.nii -mas brain_mask.nii.gz 15_masked.nii
-	
-	if [ $EN_BIAS1 -eq 1 ]
-		then
-		
-		echo Bias field correction with FAST
-		# don't forget to remove all unnecessary images 
-		fast -t 3 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 2_masked.nii
-		rm 2_masked_mixeltype.nii.gz
-		rm 2_masked_pve_0.nii.gz
-		rm 2_masked_pve_1.nii.gz
-		rm 2_masked_pve_2.nii.gz
-		rm 2_masked_pveseg.nii.gz
-		rm 2_masked_seg.nii.gz
-		3dcalc -a 2_masked.nii -b 2_masked_bias.nii.gz -expr a/b -prefix 2_bfc.nii
-		
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 5_masked.nii
-		rm 5_masked_mixeltype.nii.gz
-		rm 5_masked_pve_0.nii.gz
-		rm 5_masked_pve_1.nii.gz
-		rm 5_masked_pve_2.nii.gz
-		rm 5_masked_pveseg.nii.gz
-		rm 5_masked_seg.nii.gz
-		3dcalc -a 5_masked.nii -b 5_masked_bias.nii.gz -expr a/b -prefix 5_bfc.nii
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 10_masked.nii
-		rm 10_masked_mixeltype.nii.gz
-		rm 10_masked_pve_0.nii.gz
-		rm 10_masked_pve_1.nii.gz
-		rm 10_masked_pve_2.nii.gz
-		rm 10_masked_pveseg.nii.gz
-		rm 10_masked_seg.nii.gz
-		3dcalc -a 10_masked.nii -b 10_masked_bias.nii.gz -expr a/b -prefix 10_bfc.nii
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 12_masked.nii
-		rm 12_masked_mixeltype.nii.gz
-		rm 12_masked_pve_0.nii.gz
-		rm 12_masked_pve_1.nii.gz
-		rm 12_masked_pve_2.nii.gz
-		rm 12_masked_pveseg.nii.gz
-		rm 12_masked_seg.nii.gz
-		3dcalc -a 12_masked.nii -b 12_masked_bias.nii.gz -expr a/b -prefix 12_bfc.nii
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 15_masked.nii
-		rm 15_masked_mixeltype.nii.gz
-		rm 15_masked_pve_0.nii.gz
-		rm 15_masked_pve_1.nii.gz
-		rm 15_masked_pve_2.nii.gz
-		rm 15_masked_pveseg.nii.gz
-		#rm 15_masked_seg.nii.gz
-		3dcalc -a 15_masked.nii -b 15_masked_bias.nii.gz -expr a/b -prefix 15_bfc.nii
-			
-		# threshold and binarize wm mask
-		fslmaths 15_masked_seg.nii.gz -thr 3 -uthr 3 15_wm.nii
-		
-		# apply wm mask to all VFAs
-		fslmaths 2_bfc.nii -mas 15_wm.nii.gz 2_bfc_wm.nii 
-		fslmaths 5_bfc.nii -mas 15_wm.nii.gz 5_bfc_wm.nii
-		fslmaths 10_bfc.nii -mas 15_wm.nii.gz 10_bfc_wm.nii
-		fslmaths 12_bfc.nii -mas 15_wm.nii.gz 12_bfc_wm.nii
-		fslmaths 15_bfc.nii -mas 15_wm.nii.gz 15_bfc_wm.nii
-	else
-		# dumb file name management for norm only runs
-		fslmaths 2.nii -mas brain_mask.nii.gz 2_bfc.nii 
-		fslmaths 5.nii -mas brain_mask.nii.gz 5_bfc.nii
-		fslmaths 10.nii -mas brain_mask.nii.gz 10_bfc.nii
-		fslmaths 12.nii -mas brain_mask.nii.gz 12_bfc.nii
-		fslmaths 15.nii -mas brain_mask.nii.gz 15_bfc.nii
-		
-		echo Skipping BFC... but still segmenting one VFA for matter masks
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 15_bfc.nii
-		rm 15_bfc_mixeltype.nii.gz
-		rm 15_bfc_pve_0.nii.gz
-		rm 15_bfc_pve_1.nii.gz
-		rm 15_bfc_pve_2.nii.gz
-		rm 15_bfc_pveseg.nii.gz
-		#rm 15_bfc_seg.nii.gz
-		
-		# threshold and binarize wm mask
-		fslmaths 15_bfc_seg.nii.gz -thr 3 -uthr 3 15_wm.nii
-		
-		# apply wm mask to all VFAs
-		fslmaths 2_bfc.nii -mas 15_wm.nii.gz 2_bfc_wm.nii 
-		fslmaths 5_bfc.nii -mas 15_wm.nii.gz 5_bfc_wm.nii
-		fslmaths 10_bfc.nii -mas 15_wm.nii.gz 10_bfc_wm.nii
-		fslmaths 12_bfc.nii -mas 15_wm.nii.gz 12_bfc_wm.nii
-		fslmaths 15_bfc.nii -mas 15_wm.nii.gz 15_bfc_wm.nii
-	fi
-
-	# Run Z-axis normalization VFA data
-	# ------------------------------
-	if [ $EN_Z_NORM -eq 1 ] 
-		then
-		echo begin slice normalization
-		python3 $SCRIPT_PATH/VFA_norm.py $SUBJECT_TP_PATH
-	fi
-
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "15_BFC_Z.nii" ]
-				then
-					echo "Missing Z-normalized files. Z-norm likely failed due to non-existent inputs."
-					exit 1
-			fi
-	fi
-
-	if [ $EN_BIAS2 -eq 1 ]
-		then
-		# 2nd Bias correction VFA data
-		# ------------------------------
-		echo Begin second round of BFC
-		# Bias field correction with FAST
-		# don't forget to remove all unnecessary images 
-		fast -t 3 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 2_BFC_Z.nii
-		rm 2_BFC_Z_mixeltype.nii.gz
-		rm 2_BFC_Z_pve_0.nii.gz
-		rm 2_BFC_Z_pve_1.nii.gz
-		rm 2_BFC_Z_pve_2.nii.gz
-		rm 2_BFC_Z_pveseg.nii.gz
-		rm 2_BFC_Z_seg.nii.gz
-		3dcalc -a 2_BFC_Z.nii -b 2_BFC_Z_bias.nii.gz -expr a/b -prefix 2_b2corr.nii
-		
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 5_BFC_Z.nii
-		rm 5_BFC_Z_mixeltype.nii.gz
-		rm 5_BFC_Z_pve_0.nii.gz
-		rm 5_BFC_Z_pve_1.nii.gz
-		rm 5_BFC_Z_pve_2.nii.gz
-		rm 5_BFC_Z_pveseg.nii.gz
-		rm 5_BFC_Z_seg.nii.gz
-		3dcalc -a 5_BFC_Z.nii -b 5_BFC_Z_bias.nii.gz -expr a/b -prefix 5_b2corr.nii
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 10_BFC_Z.nii
-		rm 10_BFC_Z_mixeltype.nii.gz
-		rm 10_BFC_Z_pve_0.nii.gz
-		rm 10_BFC_Z_pve_1.nii.gz
-		rm 10_BFC_Z_pve_2.nii.gz
-		rm 10_BFC_Z_pveseg.nii.gz
-		rm 10_BFC_Z_seg.nii.gz
-		3dcalc -a 10_BFC_Z.nii -b 10_BFC_Z_bias.nii.gz -expr a/b -prefix 10_b2corr.nii
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 12_BFC_Z.nii
-		rm 12_BFC_Z_mixeltype.nii.gz
-		rm 12_BFC_Z_pve_0.nii.gz
-		rm 12_BFC_Z_pve_1.nii.gz
-		rm 12_BFC_Z_pve_2.nii.gz
-		rm 12_BFC_Z_pveseg.nii.gz
-		rm 12_BFC_Z_seg.nii.gz
-		3dcalc -a 12_BFC_Z.nii -b 12_BFC_Z_bias.nii.gz -expr a/b -prefix 12_b2corr.nii
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 15_BFC_Z.nii
-		rm 15_BFC_Z_mixeltype.nii.gz
-		rm 15_BFC_Z_pve_0.nii.gz
-		rm 15_BFC_Z_pve_1.nii.gz
-		rm 15_BFC_Z_pve_2.nii.gz
-		rm 15_BFC_Z_pveseg.nii.gz
-		rm 15_BFC_Z_seg.nii.gz
-		3dcalc -a 15_BFC_Z.nii -b 15_BFC_Z_bias.nii.gz -expr a/b -prefix 15_b2corr.nii
-		
-		# concatenates 5 images in one VFA.nii image  
-		3dTcat -prefix VFA.nii 2_b2corr.nii 5_b2corr.nii 10_b2corr.nii 12_b2corr.nii 15_b2corr.nii
-		
-	elif [ $EN_Z_NORM -eq 1 ] 
-		then
-		echo Concatenating Z-norm\'d images
-		# concatenates 5 images in one VFA.nii image
-		3dTcat -prefix VFA.nii 2_BFC_Z.nii 5_BFC_Z.nii 10_BFC_Z.nii 12_BFC_Z.nii 15_BFC_Z.nii
-
-	elif [ $EN_BIAS1 -eq 1 ]
-		then
-		echo Concatenating non Z\'d images
-		3dTcat -prefix VFA.nii 2_bfc.nii 5_bfc.nii 10_bfc.nii 12_bfc.nii 15_bfc.nii
-	else
-		echo Concatenating raw images
-		3dTcat -prefix VFA.nii 2_masked.nii 5_masked.nii 10_masked.nii 12_masked.nii 15_masked.nii
-	fi
-	
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "VFA.nii" ]
-				then
-					echo "Missing VFA file. Component files may have failed."
-					exit 1
-			fi
-	fi
-	
-	# motion correction of VFA
-	# ------------------------------
-	mcflirt -in VFA.nii -refvol 'VFA.nii[0]' -cost mutualinfo -report -verbose -plots -o VFA_mc.nii
-	gunzip VFA_mc.nii.gz
-	
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "VFA_mc.nii" ]
-				then
-					echo "Missing VFA_mc file. Motion correction may have failed."
-					exit 1
-			fi
-	fi
-	# smooth
-	#3dBlurToFWHM -input VFA_mc.nii -FWHM 5 -prefix VFA_mc_blurred.nii
-	
-	# T1 mapping where the input image is 'VFA.motioncorrected.nii'
-	# ------------------------------
-	matlab -nodisplay -r "cd('$ROCKETSHIP_PATH/parametric_scripts/custom_scripts'); addpath '$ROCKETSHIP_PATH'; addpath '$ROCKETSHIP_PATH/dce'; addpath '$ROCKETSHIP_PATH/external_programs'; addpath '$ROCKETSHIP_PATH/external_programs/niftitools'; addpath '$ROCKETSHIP_PATH/parametric_scripts'; T1mapping_fit('$SUBJECT_TP_PATH/'); exit;"
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "T1_map_t1_fa_fit_VFA_mc.nii" ]
-				then
-					echo "Missing T1 map file. T1 mapping may have failed."
-					exit 1
-			fi
-	fi
-	# Motion correction of dynamic images using AFNI
-	# ------------------------------
-	echo Motion correcting dynamic images...
-	mcflirt -in DCE.nii -refvol 'DCE.nii[1]' -cost mutualinfo -report -plots -o DCE_mc.nii
-	python3 $SCRIPT_PATH/max_disp.py $SUBJECT_TP_PATH
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "DCE_mc.nii.gz" ]
-				then
-					echo "Missing motion corrected DCE file."
-					exit 1
-			fi
-	fi
-	# Align T1 map with Dynamic data
-	# ------------------------------
-	# MC or no?
-	3dTcat -prefix ref_rep.nii DCE_mc.nii'[1]'
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii T1_map_t1_fa_fit_VFA_mc.nii t1_map_fixed_use_me.nii.gz
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "t1_map_fixed_use_me.nii.gz" ]
-				then
-					echo "Missing registered T1 map."
-					exit 1
-			fi
-	fi
-	# align and apply brain mask
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii brain_mask.nii.gz brain_mask_dyn.nii.gz
-	
-	# ensure AIF is included in mask
-	fslcpgeom 2.nii brain_mask_dyn.nii
-	cp aif.nii aif_aligned.nii
-	fslcpgeom brain_mask_dyn.nii aif_aligned.nii
-	fslmaths aif_aligned.nii -thr 0 aif_pos.nii
-	rm aif_aligned.nii
-	fslmaths brain_mask_dyn.nii -add aif_pos.nii -thr 1 -bin brain_mask_dyn_aif.nii
-	fslmaths DCE_mc.nii -mas brain_mask_dyn_aif.nii.gz DCE_mc_masked.nii
-		
-	if [ $EN_BIAS1 -eq 1 ]
-		then
-
-		# Applying bias field correction on dynamic images
-		# ------------------------------
-		echo Applying BFC to dynamic images...
-		3dTcat -prefix 1st_rep.nii DCE_mc_masked.nii'[0]' # extract images from different DCE repetitions
-		3dTcat -prefix 5th_rep.nii DCE_mc_masked.nii'[4]'
-		3dTcat -prefix 10th_rep.nii DCE_mc_masked.nii'[9]'
-		3dTcat -prefix 20th_rep.nii DCE_mc_masked.nii'[19]'
-		3dTcat -prefix 30th_rep.nii DCE_mc_masked.nii'[29]'
-		3dTcat -prefix 40th_rep.nii DCE_mc_masked.nii'[39]'
-		3dTcat -prefix 50th_rep.nii DCE_mc_masked.nii'[49]'
-		3dTcat -prefix 60th_rep.nii DCE_mc_masked.nii'[59]'
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 1st_rep.nii
-		rm 1st_rep_mixeltype.nii.gz
-		rm 1st_rep_pve_0.nii.gz
-		rm 1st_rep_pve_1.nii.gz
-		rm 1st_rep_pve_2.nii.gz
-		rm 1st_rep_pveseg.nii.gz
-		rm 1st_rep_seg.nii.gz
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 5th_rep.nii
-		rm 5th_rep_mixeltype.nii.gz
-		rm 5th_rep_pve_0.nii.gz
-		rm 5th_rep_pve_1.nii.gz
-		rm 5th_rep_pve_2.nii.gz
-		rm 5th_rep_pveseg.nii.gz
-		rm 5th_rep_seg.nii.gz
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 10th_rep.nii
-		rm 10th_rep_mixeltype.nii.gz
-		rm 10th_rep_pve_0.nii.gz
-		rm 10th_rep_pve_1.nii.gz
-		rm 10th_rep_pve_2.nii.gz
-		rm 10th_rep_pveseg.nii.gz
-		rm 10th_rep_seg.nii.gz
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 20th_rep.nii
-		rm 20th_rep_mixeltype.nii.gz
-		rm 20th_rep_pve_0.nii.gz
-		rm 20th_rep_pve_1.nii.gz
-		rm 20th_rep_pve_2.nii.gz
-		rm 20th_rep_pveseg.nii.gz
-		rm 20th_rep_seg.nii.gz
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 30th_rep.nii
-		rm 30th_rep_mixeltype.nii.gz
-		rm 30th_rep_pve_0.nii.gz
-		rm 30th_rep_pve_1.nii.gz
-		rm 30th_rep_pve_2.nii.gz
-		rm 30th_rep_pveseg.nii.gz
-		rm 30th_rep_seg.nii.gz
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 40th_rep.nii
-		rm 40th_rep_mixeltype.nii.gz
-		rm 40th_rep_pve_0.nii.gz
-		rm 40th_rep_pve_1.nii.gz
-		rm 40th_rep_pve_2.nii.gz
-		rm 40th_rep_pveseg.nii.gz
-		rm 40th_rep_seg.nii.gz
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 50th_rep.nii
-		rm 50th_rep_mixeltype.nii.gz
-		rm 50th_rep_pve_0.nii.gz
-		rm 50th_rep_pve_1.nii.gz
-		rm 50th_rep_pve_2.nii.gz
-		rm 50th_rep_pveseg.nii.gz
-		rm 50th_rep_seg.nii.gz
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 60th_rep.nii
-		rm 60th_rep_mixeltype.nii.gz
-		rm 60th_rep_pve_0.nii.gz
-		rm 60th_rep_pve_1.nii.gz
-		rm 60th_rep_pve_2.nii.gz
-		rm 60th_rep_pveseg.nii.gz
-		rm 60th_rep_seg.nii.gz
-
-		# Concatenation1
-		3dTcat -prefix dyn_bias.nii 1st_rep_bias.nii.gz 5th_rep_bias.nii.gz 10th_rep_bias.nii.gz 20th_rep_bias.nii.gz 30th_rep_bias.nii.gz 40th_rep_bias.nii.gz 50th_rep_bias.nii.gz 60th_rep_bias.nii.gz 
-
-		# Computing average across 8 bias field that have been sampled
-		3dTstat -mean -prefix mean_dyn_bias_map.nii dyn_bias.nii'[0..7]'
-
-		# Normalizing motion corrected DCE image with mean bias field 
-		3dcalc -a DCE_mc_masked.nii -b mean_dyn_bias_map.nii -expr a/b -prefix DCE_mc_bfc.nii
-
-		# don't forget to remove all unnecessary images 
-		rm 1st_rep.nii
-		rm 1st_rep_bias.nii.gz
-		rm 5th_rep.nii
-		rm 5th_rep_bias.nii.gz
-		rm 10th_rep.nii
-		rm 10th_rep_bias.nii.gz
-		rm 20th_rep.nii
-		rm 20th_rep_bias.nii.gz
-		rm 30th_rep.nii
-		rm 30th_rep_bias.nii.gz
-		rm 40th_rep.nii
-		rm 40th_rep_bias.nii.gz
-		rm 50th_rep.nii
-		rm 50th_rep_bias.nii.gz
-		rm 60th_rep.nii
-		rm 60th_rep_bias.nii.gz
-	else
-		#echo Motion correcting dynamic set
-		#3dvolreg -heptic -verbose -base 'DCE.nii[1]' -dfile DCE_motion.txt -prefix DCE_mc_bfc.nii DCE.nii
-		#3dTcat -prefix ref_rep.nii dce_mc_bfc'[1]'
-		mv DCE_mc.nii DCE_mc_bfc.nii
-	fi
-	
-	# align existing white matter mask to dynamic images and re-binarize
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii 15_wm.nii.gz 15_wm_mask_dyn.nii.gz
-	fslmaths 15_wm_mask_dyn.nii.gz -thr 1.7 -bin 15_wm_mask_dyn.nii
-	
-	# apply wm mask to all DCE images
-	fslmaths DCE_mc_bfc.nii -mas 15_wm_mask_dyn.nii.gz DCE_mc_bfc_wm.nii.gz
-
-	# normalize dynamic images
-	# ------------------------------
-	echo Normalizing dynamic images...
-	python $SCRIPT_PATH/DCE_norm.py $SUBJECT_TP_PATH
-	
-	# smooth dynamic set
-	#3dBlurToFWHM -input DCE_mc_bfc_norm.nii -FWHM 4 -prefix DCE_mc_bfc_norm_blurred.nii
-
-	# DCE
-	# ------------------------------
-	echo Begin DCE processing...
-	matlab -nodisplay -r "cd('$ROCKETSHIP_PATH'); addpath '$GPUFIT_PATH/matlab'; run_dce_auto('$SUBJECT_TP_PATH/'); exit;"
-
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "dce_patlak_fit_Ktrans.nii" ]
-				then
-					echo "Missing Ktrans maps. Check terminal--DCE failed or inputs were not generated."
-					exit 1
-			fi
-	fi
-	
-	# Analyze results (scouting)
-	# ------------------------------
-	# Make gm mask
-	if [ $EN_BIAS1 -eq 1 ]
-		then
-		fslmaths 15_masked_seg.nii.gz -thr 2 -uthr 2 -bin 15_gm_mask.nii
-		fslmaths 15_bfc.nii -mas 15_gm_mask.nii.gz 15_gm.nii
-	else
-		fslmaths 15_bfc_seg.nii.gz -thr 2 -uthr 2 -bin 15_gm_mask.nii
-		fslmaths 15_bfc.nii -mas 15_gm_mask.nii.gz 15_gm.nii
-	fi
-	
-	# Align then re-binarize gm mask
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii 15_gm.nii.gz 15_gm_mask_dyn.nii.gz
-	fslmaths 15_gm_mask_dyn.nii.gz -thr 20 -bin 15_gm_mask_dyn.nii
-	
-	# Make CSF mask
-	if [ $EN_BIAS1 -eq 1 ]
-		then
-		fslmaths 15_masked_seg.nii.gz -thr 1 -uthr 1 -bin 15_csf_mask.nii
-		fslmaths 15_bfc.nii -mas 15_csf_mask.nii.gz 15_csf.nii
-	else
-		fslmaths 15_bfc_seg.nii.gz -thr 1 -uthr 1 -bin 15_csf_mask.nii
-		fslmaths 15_bfc.nii -mas 15_csf_mask.nii.gz 15_csf.nii
-	fi
-	
-	# Align CSF mask
-	flirt -in 15_csf.nii.gz -ref ref_rep.nii -out 15_csf_mask_dyn.nii.gz -init t12dcevol.mat -applyxfm
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii 15_csf.nii.gz 15_csf_mask_dyn.nii.gz
-	fslmaths 15_csf_mask_dyn.nii.gz -thr 20 -bin 15_csf_mask_dyn.nii
-	
-	# Apply masks to T1 map
-	fslmaths t1_map_fixed_use_me.nii.gz -mas 15_wm_mask_dyn.nii T1_wm.nii
-	fslmaths t1_map_fixed_use_me.nii.gz -mas 15_gm_mask_dyn.nii T1_gm.nii
-	fslmaths t1_map_fixed_use_me.nii.gz -mas 15_csf_mask_dyn.nii T1_csf.nii
-	
-	# Apply masks to Ktrans map
-	fslmaths dce_patlak_fit_Ktrans.nii -mas 15_wm_mask_dyn.nii Ktrans_wm.nii
-	fslmaths dce_patlak_fit_Ktrans.nii -mas 15_gm_mask_dyn.nii Ktrans_gm.nii
-	fslmaths dce_patlak_fit_Ktrans.nii -mas 15_csf_mask_dyn.nii Ktrans_csf.nii
-	
-	python $SCRIPT_PATH/auto_analysis.py $SUBJECT_TP_PATH
-	cd ../../	
-	echo $dir processing complete!
-done
diff --git a/DCE_all.sh b/DCE_all.sh
index 16736c5..aa540bf 100755
--- a/DCE_all.sh
+++ b/DCE_all.sh
@@ -1,44 +1,89 @@
 #!/bin/bash
-# Oct 13, 2022
-# FSL, AFNI, Matlab, ROCKETSHIP + parametric_scripts, and Python are required
-# Within parametric_scripts should be a custom scripts folder with T1mapping_fit.m
+# FSL, Matlab, ROCKETSHIP + parametric_scripts, ANTS, and Python are required
+# Within ROCKETSHIP/parametric_scripts should be a custom scripts folder with T1mapping_fit.m
+shopt -s extglob
 # control variables
-EN_Z_NORM=0
-EN_BIAS1=1
-EN_BIAS2=0
-ff=0
-#EN_MOTION_CORR=1
-
-# make this your main data directory or pass it as an option to -d
-#DATA_DIR=/media/network_mriphysics/USC-PPG/data
+COMPARISON_MODE=0
+EN_BIAS1=0
+EN_SMOOTHING=0
+fail=0
+count=0
+total=0
+successes=0
+SKIP_IF_SUCCESS=0
+USE_FREESURFER=0
+PURGE_INTERMEDIATES=0
+GIGA_PURGE=0
+TARGET_FLAG=0
+SCRIPT_LOOP_DIR=dceprep/sub-*/ses-*
 
 # options
-while getopts ":d:bBZFh" options; do
+while getopts ":d:C:fhl:sST:" options; do
 	case "${options}" in
 		b)
 			EN_BIAS1=1
 			;;
-		B)	EN_BIAS2=1
+		C)
+			COMPARISON_MODE=1
+			OUTPUT_DIR=${OPTARG}
+			SCRIPT_LOOP_DIR=dceprep-${OPTARG}/sub-*/ses-*
 			;;
 		d)
 			DATA_DIR=${OPTARG}
+			if [ ${DATA_DIR::-1} == "/" ]
+				then
+				DATA_DIR=${DATA_DIR::-1}
+			fi
+			date=$(date +%Y-%m-%d)
+			DERIV_DIR=$(dirname $DATA_DIR)/derivatives
+			LOG_FILE=$DERIV_DIR/logs/dce_log_$date.txt
+			if [ ! -d "$DATA_DIR/logs" ]
+				then
+				mkdir $DATA_DIR/logs
+			fi
+			# write command to log file
+			echo "Command: $0 $@" > $LOG_FILE
 			;;
-		F)
-			ff=1
+		f)
+			USE_FREESURFER=1
 			;;
 		h)
 			echo "This script runs through all subject folders of a specified main data directory, processing every folder ending in '_timepoint'."
-			echo "The output is mainly the DCE outputs (Ktrans maps) and QC graphs."
-			echo "-b: enable first round of bias field corrections"
-			echo "-B: enable second round of bias field corrections, post-Z-norm if enabled"
-			echo "-Z: enable Z-slice normalization"
+			echo "The data must be preprocessed with \`preprocess_all.sh\` before running this script."
+			echo "The input is \`DCE_bfc_norm.nii.gz\`. The output is mainly the DCE outputs (Ktrans maps) and QC reports."
+			echo "If everything goes well, the script will output a case report for each subject and a population report for the entire dataset."
+			echo "-C: enable comparison mode. Specify output directory."
 			echo "-d: specify main data directory containing all subject folders"
-			echo "-F: fail fast, any command failures will end the script"
+			echo "-f: enable Freesurfer wm parcellation for subregion analysis"
 			echo "-h: display this message"
+			# echo "-l: specify a list of subjects to process (filename only, place in code folder)"
+			echo "-s: skip subjects that have already been processed"
+			echo "-S: enable smoothing of DCE input"
+			echo "-T [dir_path]: specify the subject(s)/session(s) to run (default is 'sub-*/ses-*/')"
 			exit 0
 			;;
-		Z)
-			EN_Z_NORM=1
+		l)
+			INPUT_LIST=$DATA_DIR/../code/${OPTARG}
+			;;
+		s)
+			SKIP_IF_SUCCESS=1
+			;;
+		S)
+			EN_SMOOTHING=1
+			;;
+		T)
+			TARGET_FLAG=1
+			if [ $COMPARISON_MODE -eq 1 ]
+				then
+				SCRIPT_LOOP_DIR=dceprep-$OUTPUT_DIR/$OPTARG
+			else
+				SCRIPT_LOOP_DIR=dceprep/$OPTARG
+			fi
+			echo "Processing target directory: $SCRIPT_LOOP_DIR"
+			;;
+		*)
+			echo "Invalid option ${OPTARG}. Please use -h for a list of valid options."
+			exit 1
 			;;
 	esac
 done
@@ -48,81 +93,295 @@ if [ -z "$DATA_DIR" ]
 		echo "ERROR: Please use '-d [dir_path]' to pass the path to your main data directory to this script."
 		exit 1
 fi
-cd $DATA_DIR
-ROCKETSHIP_PATH=$(find $HOME -type d -name ROCKETSHIP)
-GPUFIT_PATH=$(find $HOME -type d -name Gpufit-build)
-SCRIPT_PATH=$(find $HOME -type d -name in-house_toolbox)
 if [[ "$OSTYPE" == "linux-gnu" ]]; then
-	ROCKETSHIP_PATH=$(find $HOME -name '*run_dce_auto.m' -printf '%h\n' -quit)
-	SCRIPT_PATH=$(find $HOME -name '*auto_analysis.py' -printf '%h\n' -quit)
+	ROCKETSHIP_PATH=$(find $HOME -name '*run_dce_cli.m' -printf '%h\n' -quit || find / -name '*run_dce_cli.m' -printf '%h\n' -quit) &> /dev/null
+	SCRIPT_PATH=$(dirname "$(realpath $0)")
+	GPUFIT_PATH=$(find $HOME -name 'GpufitConstrainedMex.mexa64' -printf '%h\n' -quit || find / -name 'GpufitConstrainedMex.mexa64' -printf '%h\n' -quit)
+	GPUFIT_M_PATH=$(find $HOME -name 'ModelID.m' -printf '%h\n' -quit || find / -name 'ModelID.m' -printf '%h\n' -quit)
+else
+	ROCKETSHIP_PATH=$(find $HOME -type d -name ROCKETSHIP)
+	SCRIPT_PATH=$(find $HOME -type d -name in-house_toolbox)
+	GPUFIT_PATH=$(find $HOME -type d -name Gpufit-build)
 fi
+cd $DERIV_DIR || exit 1
 
-# Run bias correction on VFA data 
-# ------------------------------
-for dir in */*_timepoint/; do
-	date
-	echo DCE processing ${dir}...
-	SUBJECT_TP_PATH=$(realpath $dir)
-	cd $dir
+# rm dce_log.txt
+# read list of subjects
+# while IFS= read -r line; do
+# 	SUBJECT=sub-$(echo $line | awk '{print $1}')
+# 	echo $SUBJECT
+# 	SESSION=$(echo $line | awk '{print $3}')
+# Function to calculate and display progress and estimated remaining time
+function show_progress {
+    local current_iteration=$1
+    local start_time=$2
+    local total_iterations=$3
+	local runtime=$4
 
-	# DCE
-	# ------------------------------
-	echo Begin DCE processing...
-	matlab -nodisplay -r "cd('$ROCKETSHIP_PATH'); addpath '$GPUFIT_PATH/matlab'; run_dce_auto('$SUBJECT_TP_PATH/'); exit;"
+    # Calculate elapsed time
+    current_time=$(date +%s)
+    elapsed_time=$((current_time - start_time))
+
+    # Calculate estimated total time
+    estimated_total_time=$((runtime * total_iterations))
 
-	if [ $ff -eq 1 ]
+    # Calculate remaining time
+    remaining_time=$((estimated_total_time - elapsed_time))
+
+    # Display progress and estimated remaining time
+    echo -ne "Progress: $((elapsed_time * 100 / estimated_total_time))% - Elapsed time: $(($elapsed_time / 60))m $(($elapsed_time % 60))s - "
+	if [ $remaining_time -lt 0 ]
+		then
+		echo -ne "Estimated remaining time: calculating...                    \r"
+	else
+    	echo -ne "Estimated remaining time: $(($remaining_time / 60))m $(($remaining_time % 60))s                     \r"
+	fi
+}
+# if [ $COMPARISON_MODE -eq 1 ] && [ -d dceprep-$OUTPUT_DIR ] && [ $TARGET_FLAG -eq 0 ];
+# 	then
+# 	SCRIPT_LOOP_DIR=dceprep-$OUTPUT_DIR/sub-*/ses-*
+# elif [ $COMPARISON_MODE -eq 1 ] && [ -d dceprep-$OUTPUT_DIR ] && [ $TARGET_FLAG -eq 0 ];
+# 	then
+# 	SCRIPT_LOOP_DIR=dceprep/sub-*/ses-*
+# fi
+# if [ -z "$(ls -A $SCRIPT_LOOP_DIR 2>/dev/null)" ]; then
+# 		SCRIPT_LOOP_DIR=dceprep-multihance_fix/sub-*/ses-*
+# fi
+for der_dir in $SCRIPT_LOOP_DIR; do
+	((total++))
+done
+runtime=70
+start_time=$(date +%s)
+for der_dir in $SCRIPT_LOOP_DIR; do
+	dir=$der_dir
+	date >> $LOG_FILE
+
+	((count++))
+	show_progress $count $start_time $total $runtime
+
+	# get subject ID and session
+	SUBJECT=$(echo $der_dir | grep -o 'sub-[^/]*')
+	SESSION=$(echo $der_dir | grep -o 'ses-[0-9]*')
+	PREFIX=${SUBJECT}_${SESSION}
+	if [ $COMPARISON_MODE -eq 1 ]
+		then
+		echo "Comparison mode enabled. Processing in $OUTPUT_DIR..." >> $LOG_FILE
+		if [ ! -d $DERIV_DIR/dceprep-"$OUTPUT_DIR"/$SUBJECT/$SESSION ]
+			then
+			mkdir -p $DERIV_DIR/dceprep-"$OUTPUT_DIR"/$SUBJECT/$SESSION
+			mkdir -p $DERIV_DIR/dceprep-"$OUTPUT_DIR"/$SUBJECT/$SESSION/dce
+			mkdir -p $DERIV_DIR/dceprep-"$OUTPUT_DIR"/$SUBJECT/$SESSION/anat
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/dce/*bfcz* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/dce/*desc-AIF*_T1map* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/dce/*hmc* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/dce/*.txt $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/anat/*_T1map* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/anat
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/anat/*mask* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/anat
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/anat/*.mat $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/anat
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/anat/*wmparc* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/anat
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/anat/*DCEref_VFA* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/anat
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/anat/*DCEref_T1w* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/anat
+			cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/figures $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/figures
+		fi
+		cd $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION || echo "ERROR: $OUTPUT_DIR does not exist. Preprocess first or check the name and try again." >> $LOG_FILE
+		if [ ! -f dce/${PREFIX}_desc-hmc_DCEref.nii.gz ]
+			then
+			# probably a no motion correction run, get 2nd t-slice of DCE
+			# cp dce/${PREFIX}_desc-bfc_DCE.nii.gz dce/${PREFIX}_desc-hmc_DCE.nii.gz
+			fslmerge -n 1 dce/${PREFIX}_desc-hmc_DCEref.nii.gz dce/${PREFIX}_desc-bfc_DCE.nii.gz
+		fi
+	else
+		cd $der_dir || echo "ERROR: $der_dir does not exist. Preprocess first or check the name and try again." >> $LOG_FILE
+	fi
+	SUBJECT_TP_PATH=$(pwd)
+	if [ ! -f anat/${PREFIX}_desc-brain_T1w.nii.gz ]
+		then
+		cp -r $DERIV_DIR/dceprep-multihance_fix/$SUBJECT/$SESSION/anat/${PREFIX}_desc-brain_T1w.nii.gz $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/anat
+	fi
+	# Locking mechanism to prevent concurrent processing from other machines
+	LOCKFILE="lock.txt"
+	LOCKDIR=$(pwd)
+	LOCKPATH="$LOCKDIR/$LOCKFILE"
+	LOCKHOST=$(hostname)
+	LOCKPID=$$
+	LOCKLIST="$DERIV_DIR/locks_${LOCKHOST}.txt"
+	echo "Locking $LOCKPATH on $LOCKHOST with PID $LOCKPID"
+
+	# Try to create lock file atomically
+	if ( set -o noclobber; echo "$LOCKHOST:$LOCKPID" > "$LOCKPATH" ) 2> /dev/null; then
+		echo "$LOCKPATH" >> "$LOCKLIST"
+		trap 'for f in $(cat "$LOCKLIST" 2>/dev/null); do rm -f "$f"; done; rm -f "$LOCKLIST"; exit $?' INT TERM EXIT
+	else
+		echo "Skipping $dir because it is currently being processed by $(cat $LOCKPATH)." >> $LOG_FILE
+		cd $DERIV_DIR
+		continue
+	fi
+	if [ $SKIP_IF_SUCCESS -eq 1 ]
 		then
-			if [ ! -f "dce_patlak_fit_Ktrans.nii" ]
+		if [ -f "reports/${PREFIX}_desc-casereport.html" ] && [ -f "anat/${PREFIX}_space-DCEref_desc-wmparc.nii.gz" ]
+			then
+			echo "Skipping $dir because it has already been processed." >> $LOG_FILE
+			((successes++))
+			if [ $PURGE_INTERMEDIATES -eq 1 ] # && [ $COMPARISON_MODE -eq 1 ]
 				then
-					echo "Missing Ktrans maps. Check terminal--DCE failed or inputs were not generated."
-					fail=1
-					continue
+				cd anat
+				rm -f !(${PREFIX}*bfczunified_VFA.nii|${PREFIX}_space-DCEref_T1map*|${PREFIX}*label-*_T1map*|*.mat|*wmparc.nii.gz|*space-DCEref_desc-brain_mask.nii.gz)
+				cd ../dce
+				rm -f !(${PREFIX}*Ktrans*|${PREFIX}*bfcz_DCE*|${PREFIX}*AIFincluded*|${PREFIX}*AIF_T1map*|figures|*.log|*.txt|*.par)
 			fi
+			cd $DERIV_DIR
+			continue
+		fi
+	fi
+	if [ ! -f "dce/${PREFIX}_desc-bfcz_DCE.nii.gz" ] && [ ! -f "dce/${PREFIX}_desc-bfcz_DCE.nii" ]
+		then
+		echo Missing input file dce/${PREFIX}_desc-bfcz_DCE. Make sure the data has been preprocessed. Skipping $dir... >> $LOG_FILE
+		cd $DERIV_DIR
+		fail=1
+		continue
+	fi
+
+	# iNESMA smooth DCE input (exclude AIF roi)
+	if [ $EN_SMOOTHING -eq 1 ]
+		then
+		python3 $SCRIPT_PATH/iNESMA_GPU.py $PREFIX dce/${PREFIX}_desc-bfcz_DCE.nii.gz dce/${PREFIX}_desc-AIF_T1map.nii.gz
 	fi
 	
-	# Analyze results (scouting)
+	# DCE
 	# ------------------------------
-	# Make gm mask
-	if [ $EN_BIAS1 -eq 1 ]
+	echo Begin DCE processing...
+	matlab -nodisplay -r "cd('$ROCKETSHIP_PATH'); addpath '$GPUFIT_PATH'; addpath '$GPUFIT_M_PATH'; run_dce_cli('$DATA_DIR/$SUBJECT/$SESSION/', '$SUBJECT_TP_PATH/'); exit;"
+	mv dce/dce_*_fit_Ktrans.nii dce/${PREFIX}_Ktrans.nii
+	mv dce/dce_*_fit_ktrans_ci_low.nii dce/${PREFIX}_Ktrans_ci_low.nii
+	mv dce/dce_*_fit_ktrans_ci_high.nii dce/${PREFIX}_Ktrans_ci_high.nii
+	mv dce/dce_*_fit_vp.nii dce/${PREFIX}_vp.nii
+	mv dce/dce_*_fit_vp_ci_low.nii dce/${PREFIX}_vp_ci_low.nii
+	mv dce/dce_*_fit_vp_ci_high.nii dce/${PREFIX}_vp_ci_high.nii
+	mv dce/dce_*_fit_sse.nii dce/${PREFIX}_sse.nii
+	# move images into figures folder
+	mv dce/dce*.png figures/
+	rm -f dce/*.fig
+	if [ ! -f "dce/${PREFIX}_Ktrans.nii" ]
 		then
-		fslmaths 15_masked_seg.nii.gz -thr 2 -uthr 2 -bin 15_gm_mask.nii
-		fslmaths 15_bfc.nii -mas 15_gm_mask.nii.gz 15_gm.nii
-	else
-		fslmaths 15_bfc_seg.nii.gz -thr 2 -uthr 2 -bin 15_gm_mask.nii
-		fslmaths 15_bfc.nii -mas 15_gm_mask.nii.gz 15_gm.nii
+			echo $dir "Missing Ktrans maps. DCE failed or inputs were not generated. Hopefully message below is relevant." >> $LOG_FILE
+			tail -1 dce/A_dceR1info.log >> $LOG_FILE
+			cd $DATA_DIR/../derivatives
+			fail=1
+			continue
 	fi
 	
-	# Align then re-binarize gm mask
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii 15_gm.nii.gz 15_gm_mask_dyn.nii.gz
-	fslmaths 15_gm_mask_dyn.nii.gz -thr 20 -bin 15_gm_mask_dyn.nii
+	# Analyze results
+	# ------------------------------
 	
-	# Make CSF mask
-	if [ $EN_BIAS1 -eq 1 ]
-		then
-		fslmaths 15_masked_seg.nii.gz -thr 1 -uthr 1 -bin 15_csf_mask.nii
-		fslmaths 15_bfc.nii -mas 15_csf_mask.nii.gz 15_csf.nii
-	else
-		fslmaths 15_bfc_seg.nii.gz -thr 1 -uthr 1 -bin 15_csf_mask.nii
-		fslmaths 15_bfc.nii -mas 15_csf_mask.nii.gz 15_csf.nii
-	fi
+	# Align then re-binarize gm mask
+	DCEREF_FILE=$(ls dce/${PREFIX}*DCEref.nii.gz | head -n 1)
+	antsApplyTransforms -i anat/${PREFIX}_label-GM_mask.nii.gz -r $DCEREF_FILE -t anat/${PREFIX}_from-T1w_to-DCEref.mat -o anat/${PREFIX}_space-DCEref_label-GM_mask_pv.nii.gz &> /dev/null
+	fslmaths anat/${PREFIX}_space-DCEref_label-GM_mask_pv.nii.gz -thr 0.9 -bin anat/${PREFIX}_space-DCEref_label-GM_mask.nii.gz
+	rm anat/${PREFIX}_space-DCEref_label-GM_mask_pv.nii.gz
 	
 	# Align CSF mask
-	flirt -in 15_csf.nii.gz -ref ref_rep.nii -out 15_csf_mask_dyn.nii.gz -init t12dcevol.mat -applyxfm
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii 15_csf.nii.gz 15_csf_mask_dyn.nii.gz
-	fslmaths 15_csf_mask_dyn.nii.gz -thr 20 -bin 15_csf_mask_dyn.nii
+	antsApplyTransforms -i anat/${PREFIX}_label-CSF_mask.nii.gz -r $DCEREF_FILE -t anat/${PREFIX}_from-T1w_to-DCEref.mat -o anat/${PREFIX}_space-DCEref_label-CSF_mask_pv.nii.gz &> /dev/null
+	fslmaths anat/${PREFIX}_space-DCEref_label-CSF_mask_pv.nii.gz -thr 0.9 -bin anat/${PREFIX}_space-DCEref_label-CSF_mask.nii.gz
+	rm anat/${PREFIX}_space-DCEref_label-CSF_mask_pv.nii.gz
 	
 	# Apply masks to T1 map
-	fslmaths t1_map_fixed_use_me.nii.gz -mas 15_wm_mask_dyn.nii T1_wm.nii
-	fslmaths t1_map_fixed_use_me.nii.gz -mas 15_gm_mask_dyn.nii T1_gm.nii
-	fslmaths t1_map_fixed_use_me.nii.gz -mas 15_csf_mask_dyn.nii T1_csf.nii
+	fslmaths anat/${PREFIX}_space-DCEref_T1map.nii  -mas anat/${PREFIX}_space-DCEref_label-WM_mask.nii.gz anat/${PREFIX}_space-DCEref_label-WM_T1map.nii
+	fslmaths anat/${PREFIX}_space-DCEref_T1map.nii -mas anat/${PREFIX}_space-DCEref_label-GM_mask.nii.gz anat/${PREFIX}_space-DCEref_label-GM_T1map.nii
+	fslmaths anat/${PREFIX}_space-DCEref_T1map.nii -mas anat/${PREFIX}_space-DCEref_label-CSF_mask.nii.gz anat/${PREFIX}_space-DCEref_label-CSF_T1map.nii
 	
 	# Apply masks to Ktrans map
-	fslmaths dce_patlak_fit_Ktrans.nii -mas 15_wm_mask_dyn.nii Ktrans_wm.nii
-	fslmaths dce_patlak_fit_Ktrans.nii -mas 15_gm_mask_dyn.nii Ktrans_gm.nii
-	fslmaths dce_patlak_fit_Ktrans.nii -mas 15_csf_mask_dyn.nii Ktrans_csf.nii
+	fslmaths dce/${PREFIX}_Ktrans.nii -mas anat/${PREFIX}_space-DCEref_label-WM_mask.nii.gz dce/${PREFIX}_seg-WM_Ktrans.nii
+	fslmaths dce/${PREFIX}_Ktrans.nii -mas anat/${PREFIX}_space-DCEref_label-GM_mask.nii.gz dce/${PREFIX}_seg-GM_Ktrans.nii
+	fslmaths dce/${PREFIX}_Ktrans.nii -mas anat/${PREFIX}_space-DCEref_label-CSF_mask.nii.gz dce/${PREFIX}_seg-CSF_Ktrans.nii
 	
-	python3 $SCRIPT_PATH/auto_analysis.py $SUBJECT_TP_PATH
-	python3 $SCRIPT_PATH/report.py $SUBJECT_TP_PATH
-	cd ../../	
-	echo $dir processing complete!
-done
+	# registration QC
+	python3 $SCRIPT_PATH/ktrans_analysis.py $dir $PREFIX
+	
+	fslmaths anat/${PREFIX}_space-DCEref_label-WM_mask.nii.gz -add 2000 huh.nii
+	fslmaths huh.nii.gz -thr 2001 huh.nii
+	fslmaths $DCEREF_FILE -sub huh.nii.gz bozo.nii
+	fslmaths bozo.nii -thr 0 anat/${PREFIX}_space-DCEref_label-WMQC.nii.gz
+	
+	fslmaths anat/${PREFIX}_space-DCEref_label-GM_mask.nii.gz -add 2000 huh2.nii
+	fslmaths huh2.nii.gz -thr 2001 huh2.nii
+	fslmaths $DCEREF_FILE -sub huh2.nii.gz bozo2.nii
+	fslmaths bozo2.nii -thr 0 anat/${PREFIX}_space-DCEref_label-GMQC.nii.gz
+	rm huh.nii.gz huh2.nii.gz bozo.nii.gz bozo2.nii.gz
+		
+	# wm parcellation with Freesurfer
+	if [ $USE_FREESURFER -eq 1 ]
+		then
+		# get subject ID
+		subj=$(echo $dir | rev | cut -d'/' -f2 | rev)
+
+		# run Freesurfer
+		# if [ ! -f $dir/freesurfer/$SUBJECT/$SESSION/mri/wmparc.mgz ]
+		# 	then
+		# 	mkdir -p $dir/freesurfer/$SUBJECT/$SESSION
+		# 	recon-all -s $DATA_DIR -i anat/${PREFIX}_T1w.nii.gz -sd $dir/freesurfer/$SUBJECT/$SESSION -all -parallel -openmp 8
+		# fi
+
+		# get wm parcellation
+		mri_label2vol --seg $DERIV_DIR/freesurfer/$SUBJECT/$SESSION/mri/wmparc.mgz \
+			--temp $DERIV_DIR/freesurfer/$SUBJECT/$SESSION/mri/rawavg.mgz \
+			--o $DERIV_DIR/freesurfer/$SUBJECT/$SESSION/mri/wmparc-in-rawavg.mgz \
+			--regheader $DERIV_DIR/freesurfer/$SUBJECT/$SESSION/mri/wmparc.mgz &> /dev/null
+        mri_convert $DERIV_DIR/freesurfer/$SUBJECT/$SESSION/mri/wmparc-in-rawavg.mgz wmparc.nii.gz &> /dev/null
+        antsApplyTransforms -i wmparc.nii.gz -r dce/${PREFIX}_desc-hmc_DCEref.nii.gz -t anat/${PREFIX}_from-t1w_to-DCEref.mat -n NearestNeighbor -o anat/${PREFIX}_space-DCEref_desc-wmparc.nii &> /dev/null
+        gzip -f anat/${PREFIX}_space-DCEref_desc-wmparc.nii &> /dev/null
+		rm wmparc.nii.gz
+	elif [ ! -f dce/${PREFIX}_space-MNI_Ktrans.nii.gz ]
+		then
+		# flirt -in DCE_mc.nii.gz -ref $FSLDIR/data/standard/MNI152_T1_1mm.nii.gz -omat DCE2MNI.mat -out DCE_MNI_FSL.nii.gz
+		# flirt -in $dir/T1.nii -ref $FSLDIR/data/standard/MNI152_T1_1mm.nii.gz -out t1w_MNI.nii.gz
+		# antsRegistration --verbose 0 --dimensionality 3 --float 0 --collapse-output-transforms 1 \
+		# 	--output [ DCE_MPRAGE,DCE_MPRAGE.nii.gz ] --interpolation Linear --use-histogram-matching 0 \
+		# 	--winsorize-image-intensities [ 0.005,0.995 ] --transform Affine[ 0.1 ] \
+		# 	--metric MI[ T1_bet.nii.gz,DCE_mc.nii.gz,1,32,Regular,0.25 ] \
+		# 	--convergence [ 1000x500x250x100,1e-6,10 ] --shrink-factors 12x8x4x2 --smoothing-sigmas 4x3x2x1vox
+		# antsRegistrationSyNQuick.sh -d 3 -f anat/${PREFIX}_desc-brain_T1w.nii.gz -m dce/${PREFIX}_desc-hmc_DCEref.nii.gz -o dce/${PREFIX}_space-MNI_DCEref -n 8
+		antsRegistrationSyNQuick.sh -d 3 -f $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz -m anat/${PREFIX}_desc-brain_T1w.nii.gz -o anat/${PREFIX}_space-MNI_T1w -n 8
+		# antsApplyTransforms -i dce/${PREFIX}_desc-hmc_DCEref.nii.gz -r $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz -t dce/${PREFIX}_space-MNI_DCEref1Warp.nii.gz -t anat/${PREFIX}_space-MNI_T1w0GenericAffine.mat -t [ anat/${PREFIX}_from-T1w_to-DCEref.mat, 1] -o DCE_mc_MNI.nii.gz
+		antsApplyTransforms -i dce/${PREFIX}_desc-hmc_DCEref.nii.gz -r $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz -t anat/${PREFIX}_space-MNI_T1w0GenericAffine.mat -t [ anat/${PREFIX}_from-T1w_to-DCEref.mat, 1] -o dce/${PREFIX}_space-MNI_DCEref.nii.gz
+		# antsRegistration --verbose 1 --dimensionality 3 --float 0 --collapse-output-transforms 1 \
+		# 	--output [ anat/${PREFIX}_space-MNI,anat/${PREFIX}_space-MNI_T1w.nii.gz ] --interpolation Linear --use-histogram-matching 0 \
+		# 	--winsorize-image-intensities [ 0.005,0.995 ] --transform SyN[ 0.1 ] \
+		# 	--metric MI[ $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz,anat/${PREFIX}_space-DCEref_T1w.nii.gz,1,32,Regular,0.25 ] \
+		# 	--convergence [ 1000x500x250x100,1e-6,10 ] --shrink-factors 12x8x4x2 --smoothing-sigmas 4x3x2x1vox
+		# antsApplyTransforms -i dce/${PREFIX}_Ktrans.nii -r $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz -t ${PREFIX}_space-MNI_T1w0Warp.nii.gz -t t1w_MNI0GenericAffine.mat -t [T1_dyn0GenericAffine.mat, 1] -o Ktrans_MNI.nii.gz
+		antsApplyTransforms -i dce/${PREFIX}_Ktrans.nii -r $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz -t anat/${PREFIX}_space-MNI_T1w0GenericAffine.mat -t [ anat/${PREFIX}_from-T1w_to-DCEref.mat, 1] -o dce/${PREFIX}_space-MNI_Ktrans.nii.gz
+		# antsApplyTransforms -i dce_patlak_fit_vp.nii -r $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz -t t1w_MNI1Warp.nii.gz -t t1w_MNI0GenericAffine.mat -t [T1_dyn0GenericAffine.mat, 1] -o vp_MNI.nii.gz
+		# flirt -in dce_patlak_fit_Ktrans.nii -ref $FSLDIR/data/standard/MNI152_T1_1mm.nii.gz -out ktrans_2_MNI.nii.gz -init DCE2MNI.mat -applyxfm
+	else
+		echo SKIP
+	fi
+	mkdir reports &> /dev/null
+	python3 $SCRIPT_PATH/case_report.py $DATA_DIR/$SUBJECT/$SESSION $PREFIX $USE_FREESURFER
+	python3 $SCRIPT_PATH/ktrans_report.py $DATA_DIR/$SUBJECT/$SESSION $PREFIX
+	if [ $PURGE_INTERMEDIATES -eq 1 ]
+		then
+		# rm -f !(Ktrans_*|T1_gm*|T1_wm*|T1_csf*|*_patlak_fit*.nii|case_report.html|*_MNI.nii.gz|*fit_VFA.nii|figures|dce*.png|*.log)
+		cd anat
+		rm -f !(${PREFIX}*bfczunified_VFA.nii|${PREFIX}_space-DCEref_T1map*|${PREFIX}*label-*_T1map*|*.mat|*wmparc.nii.gz)
+		cd ../dce
+		rm -f !(${PREFIX}*Ktrans*|${PREFIX}*bfcz_DCE*|${PREFIX}*AIFincluded*|${PREFIX}*AIF_T1map*|figures|*.log|*.txt|*.par)
+	elif [ $GIGA_PURGE -eq 1 ]
+		then
+		rm -f !(case_report.html|figures|*.log)
+	fi
+	cd $DERIV_DIR
+	echo $dir processing complete! >> $LOG_FILE
+	((successes++))
+done # < $INPUT_LIST
+
+mkdir -p $DERIV_DIR/reports
+python3 $SCRIPT_PATH/population_report.py $DERIV_DIR $OUTPUT_DIR $ROCKETSHIP_PATH
+((failures=count-successes))
+echo "Completed DCE processing for $count subjects." >> $LOG_FILE
+echo $successes subjects succeeded >> $LOG_FILE
+echo $failures subjects failed >> $LOG_FILE
+
+if [ $fail -eq 1 ]
+	then
+	exit 1
+fi
diff --git a/DCE_norm.py b/DCE_norm.py
index 43cc8c9..1bc22cd 100644
--- a/DCE_norm.py
+++ b/DCE_norm.py
@@ -1,15 +1,18 @@
 import sys
 from pathlib import Path
-from statistics import mean, pstdev
+from statistics import mean, pstdev, median
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
 from numpy.polynomial import Polynomial
 from numpy.polynomial.polynomial import polyval
 import nibabel as nib
+from lmfit import Model
+
 matplotlib.use('Agg')
 
 # add as arg? add mask arg?
+GAUSSFIT = True
 POLYFIT = True
 
 def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE NORMALIZATION
@@ -20,10 +23,16 @@ def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE N
     wm_data = wm_mask.get_fdata()
     mri_shape = mri_data.shape
     wm_shape = wm_data.shape
-    slice_num = min(mri_shape[0], mri_shape[1], mri_shape[2], mri_shape[3])
-    slice_loc = mri_shape.index(slice_num)
-    mri_data = np.reshape(mri_data, (mri_shape[min(dim-set([slice_loc]))], mri_shape[max(dim-set([slice_loc]))], slice_num, 64))
-    wm_data = np.reshape(wm_data, (wm_shape[min(dim-set([slice_loc]))], wm_shape[max(dim-set([slice_loc]))], slice_num, 64))
+    slice_num = min(mri_shape[0], mri_shape[1], mri_shape[2])
+    axis_order = np.argsort([mri_shape[0], mri_shape[1], mri_shape[2]])
+    # Unpack the axis order
+    # usual shape should be x >= y > z
+    z_index, y_index, x_index = axis_order
+    # if x and y are the same, swap them
+    if mri_shape[x_index] == mri_shape[y_index]:
+        x_index, y_index = y_index, x_index
+    mri_data = np.transpose(mri_data, (x_index, y_index, z_index, 3))
+    wm_data = np.transpose(wm_data, (x_index, y_index, z_index, 3))
     wm_mean = []
     orig_img = []
 
@@ -49,8 +58,100 @@ def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE N
     mri_final = mri_data
     wm_final = wm_data
 
-    # apply normalizations
-    if POLYFIT is True:
+    gaussian_params = [0 for i in range(slice_num)]
+    if GAUSSFIT:
+        startat = 0
+        endat = slice_num
+        hadSuccess = False
+        # get histogram of each wm slice
+        for i in range(slice_num):
+            a = np.where(wm_data[:, :, i, :] > 0)
+            hist, bins = np.histogram(wm_data[:,:,i,:][a].flatten(), bins=100)
+
+            def double_gaussian(x, A1, mu1, sigma1, A2, mu2, sigma2):
+                return (
+                    A1 * np.exp(-0.5 * ((x - mu1) / sigma1) ** 2) +
+                    A2 * np.exp(-0.5 * ((x - mu2) / sigma2) ** 2)
+                )
+
+            # Initial guess for the parameters
+            # count voxels within 1 std of mean
+            area = np.count_nonzero(wm_data[:,:,i,:][a])
+
+            # prepare for gaussian fitting
+            bin_width = bins[1] - bins[0]
+            model = Model(double_gaussian)
+            amp_guess = 0
+            params = None
+            # check if slice is empty
+            if area == 0:
+                # just get from next slice
+                # if i == slice_num - 1:
+                #     a = np.where(wm_data[:,:,i-1,:] > 0)
+                #     area = np.count_nonzero(wm_data[:,:,i-1,:][a])
+                #     amp_guess = area / pstdev(wm_data[:,:,i-1,:][a]) * 0.3989 * bin_width
+                #     params = model.make_params(A1=amp_guess*0.1, mu1=wm_mean[i-1], sigma1=pstdev(wm_data[:,:,i-1,:][a]), A2=amp_guess, mu2=median(wm_data[:,:,i-1,:][a]), sigma2=pstdev(wm_data[:,:,i-1,:][a]))
+                # else:
+                #     a = np.where(wm_data[:,:,i+1,:] > 0)
+                #     area = np.count_nonzero(wm_data[:,:,i+1,:][a])
+                #     amp_guess = area / pstdev(wm_data[:,:,i+1,:][a]) * 0.3989 * bin_width
+                #     params = model.make_params(A1=amp_guess*0.1, mu1=wm_mean[i+1], sigma1=pstdev(wm_data[:,:,i+1,:][a]), A2=amp_guess, mu2=median(wm_data[:,:,i+1,:][a]), sigma2=pstdev(wm_data[:,:,i+1,:][a]))
+                if hadSuccess and i < endat:
+                    endat = i
+                elif not hadSuccess:
+                    startat = i + 1
+                continue
+            else:
+                hadSuccess = True
+                amp_guess = area / pstdev(wm_data[:,:,i,:][a]) * 0.3989 * bin_width
+                params = model.make_params(A1=amp_guess*0.1, mu1=wm_mean[i], sigma1=pstdev(wm_data[:,:,i,:][a]), A2=amp_guess, mu2=median(wm_data[:,:,i,:][a]), sigma2=pstdev(wm_data[:,:,i,:][a]))
+            result = model.fit(hist, params, x=bins[:-1])
+            gaussian_params[i] = result.best_values
+
+            # Plot the histogram
+            plt.figure()
+            plt.bar(bins[:-1], hist, width=np.diff(bins), align='edge', alpha=0.5)
+
+            # Plot the fitted curve
+            plt.plot(bins[:-1], result.best_fit, color='red', linewidth=2)
+
+            # Add labels and title
+            plt.title(f"Histogram and Fitted Curve - Slice {i+1}")
+            plt.xlabel("Pixel Value")
+            plt.ylabel("Frequency")
+
+            # make hist directory if it doesn't exist
+            hist_dir = file_dir + '/../figures/hist'
+            Path(hist_dir).mkdir(parents=True, exist_ok=True)
+
+            # Save the figure
+            path1 = file_dir + '/../figures/hist/DCE_' + str(i+1) + '_hist.png'
+            plt.savefig(path1)
+            plt.close()
+
+        # apply normalizations
+        print("Using Gaussian fitting to normalize DCE")
+        mu = [0 for i in range(slice_num)]
+        for i in range(startat, endat):
+            if gaussian_params[i]['A1'] > gaussian_params[i]['A2'] and gaussian_params[i]['mu1'] > 0 and gaussian_params[i]['mu1'] > gaussian_params[i]['mu2'] and gaussian_params[i]['mu1'] < 1500:
+                mu[i] = gaussian_params[i]['mu1']
+            else:
+                mu[i] = gaussian_params[i]['mu2']
+            # print("mu " + str(mu[i]))
+            # print("SLICE: " + str(i + 1))
+            # print("mu: " + str(gaussian_params[i]['mu1']) + " " + str(gaussian_params[i]['mu2']))
+            # print("A: " + str(gaussian_params[i]['A1']) + " " + str(gaussian_params[i]['A2']))
+            # print("sigma: " + str(gaussian_params[i]['sigma1']) + " " + str(gaussian_params[i]['sigma2']))
+        median_mu = median([m for m in mu if m != 0])
+        for i in range(slice_num):
+            if mu[i] > 0:
+                scale_factor = median_mu / mu[i]
+            else:
+                scale_factor = 1  # or some other default value
+            mri_final[:, :, i, :] *= scale_factor
+            wm_final[:, :, i, :] *= scale_factor
+
+    elif POLYFIT is True:
         print("Using Polynomial fitting to normalize " + mri_file1)
         poly_norm_curve = Polynomial.fit(list(range(slice_num)), wm_mean, 4, w=polyfit_slice_weights)
         norm_slices = polyval(list(range(slice_num)), poly_norm_curve.convert().coef)
@@ -64,9 +165,9 @@ def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE N
     else:
         print("Using Z-normalization")
         # calc stats of all slices
-        data_mean = mean(wm_mean[1:13])
-        std_dev = pstdev(wm_mean[1:13])
-        err = 1*std_dev
+        data_mean = mean(wm_mean[0:slice_num-1])
+        std_dev = pstdev(wm_mean[0:slice_num-1])
+        err = .5*std_dev
         # find slices out of range
         min_val = data_mean - err
         max_val = data_mean + err
@@ -96,36 +197,60 @@ def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE N
         else:
             norm_wm.append(0)
 
-    data_mean1 = mean(norm_img)
-
     fig, ax = plt.subplots(figsize=(20, 6))
     ax.plot(range(slice_num), wm_mean, '-ok', label='original')
-    if POLYFIT is True:
+    if POLYFIT is True and GAUSSFIT is False:
         ax.plot(range(slice_num), norm_slices, ':ob', label='fit')
-    ax.plot(range(slice_num), norm_wm, '--og', label='corrected')
+    if GAUSSFIT:
+        ax.plot(range(slice_num), mu, 'o', label='mu', color='grey')
+        ax.plot(range(slice_num), np.ones(slice_num)*median_mu, ':x', label='median_mu', color='lightgreen')
+    ax.plot(range(slice_num), norm_wm, '--xg', label='corrected')
     ax.set_xlabel('Slice #')
     ax.set_ylabel('White Matter Mean')
     ax.set_title("DCE Slice Normalization")
     ax.legend()
 
-    path2 = file_dir +'/DCE_mc_bfc_norm.png'   #THE STRING IN THE END CONTAINS THE FILE NAME OF THE GRAPHS GENERATED
-    plt.savefig(path2)
-    # print(file_dir)
-    mri_final = np.reshape(mri_final, mri_shape)
+    path2 = 'figures/' + mri_file1.split('desc-bfc_DCE')[0].split('/')[-1] + 'desc-bfcz_DCE.svg'   #THE STRING IN THE END CONTAINS THE FILE NAME OF THE GRAPHS GENERATED
+    plt.savefig(path2, bbox_inches='tight')
+    plt.close()
+
+    # transpose mri_final back to mri_shape order
+    x_index = mri_final.shape.index(mri_shape[0])
+    y_index = mri_final.shape.index(mri_shape[1])
+    z_index = mri_final.shape.index(mri_shape[2])
+    if x_index == y_index:
+        y_index = set(dim) - {x_index, z_index}
+        y_index = list(y_index)[0]
+    elif x_index == z_index:
+        z_index = set(dim) - {x_index, y_index}
+        z_index = list(z_index)[0]
+    elif y_index == z_index:
+        z_index = set(dim) - {x_index, y_index}
+        z_index = list(z_index)[0]
+
+    mri_final = np.transpose(mri_final, (x_index, y_index, z_index, 3))
+    mri_final = mri_final.astype(np.float32)
     final_img = nib.Nifti1Image(mri_final, mri.affine)
-    path3 = file_dir + '/DCE_mc_bfc_norm.nii'  #THE STRING IN THE END CONTAINS THE FILE NAME OF THE NORMALIZED NIFTI IMAGE GENERATED
+    path3 = mri_file1.split('desc-bfc_DCE')[0] + 'desc-bfcz_DCE.nii'  #THE STRING IN THE END CONTAINS THE FILE NAME OF THE NORMALIZED NIFTI IMAGE GENERATED
     nib.save(final_img, path3)
 
 
 dir = Path(sys.argv[1])     # takes timepoint directory as argument
 files_in_dir = dir.iterdir()
 for file in files_in_dir:
-    if str(file).endswith('mc_bfc.nii') or str(file).endswith('mc_bfc.nii.gz'):
+    if str(file).endswith('desc-bfc_DCE.nii') or str(file).endswith('desc-bfc_DCE.nii.gz'):
         file1 = str(file)
-        mask_file = file1.split('.', 1)
-        mask_file = mask_file[0] + '_wm.nii'
+        mask_file = file1.split('desc-bfc_DCE', 1)[0]
+        mask_file = mask_file + 'seg-WM_DCE.nii'
+
+        if Path(mask_file + ".gz").exists():
+            mask_file += ".gz"
+        elif not Path(mask_file).exists():
+            print(f"Mask file not found for {file1}")
+            continue
 
         try:
             normalize(file1, mask_file, str(dir))
-        except FileNotFoundError:
-            normalize(file1, mask_file + ".gz", str(dir))
+        except Exception as e:
+            print(e)
+            print("Error in normalizing " + file1)
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..d513a55
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,253 @@
+# syntax=docker/dockerfile:1
+
+# cannot do 24.04 because of freesurfer (libc6 << 2.36)
+FROM nvidia/cuda:13.0.0-cudnn-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND="noninteractive" \
+    LANG="en_US.UTF-8" \
+    LC_ALL="en_US.UTF-8"
+
+# # modified from fmriprep (https://github.com/nipreps/fmriprep/blob/master/Dockerfile)
+# # Prepare environment
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+                    apt-utils \
+                    autoconf \
+                    build-essential \
+                    bzip2 \
+                    ca-certificates \
+                    curl \
+                    wget \
+                    sudo \
+                    git \
+                    libasound2 \
+                    libcairo-gobject2 libcairo2 libcap2 libcrypt1 libcrypt-dev \
+                    libcups2 libdbus-1-3 libdrm2 libfontconfig1 libgbm1 libgdk-pixbuf2.0-0 libgl1 libglib2.0-0 \
+                    libgomp1 libgstreamer-plugins-base1.0-0 libgstreamer1.0-0 libgtk-3-0 libnspr4 libnss3 libodbc1 \
+                    libpam0g libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 \
+                    libsm6 libsndfile1 \
+                    libuuid1 libx11-6 libx11-xcb1 libxcb-dri3-0 libxcb1 libxcomposite1 libxcursor1 libxdamage1 \
+                    libxext6 libxfixes3 libxft2 libxi6 libxinerama1 libxrandr2 libxrender1 libxt6 libxtst6 libxxf86vm1 \
+                    linux-libc-dev \
+                    make net-tools procps zlib1g \
+                    bc \
+                    dc \
+                    file \
+                    libfontconfig1 \
+                    libfreetype6 \
+                    libgl1-mesa-dev \
+                    libgl1-mesa-dri \
+                    libglu1-mesa-dev \
+                    libgomp1 \
+                    libice6 \
+                    libxcursor1 \
+                    libxft2 \
+                    libxinerama1 \
+                    libxrandr2 \
+                    libxrender1 \
+                    libxt6 \
+                    libtool \
+                    lsb-release \
+                    netbase \
+                    pkg-config \
+                    python3-pip \
+                    unzip \
+                    xvfb \
+                    language-pack-en \
+                    libx11-dev \
+                    gettext \
+                    xterm \
+                    x11-apps \
+                    libncurses5 \
+                    libegl1 \
+                    csh \
+                    tcsh \
+                    xorg \
+                    xorg-dev \
+                    xserver-xorg-dev \
+                    xserver-xorg-video-intel \
+                    libjpeg62 \
+                    libpcre2-16-0 \
+                    libxcb-icccm4 \
+                    libxcb-image0 \
+                    libxcb-keysyms1 \
+                    libxcb-render-util0 \
+                    libxcb-shape0 \
+                    libxcb-util1 \
+                    libxcb-xinerama0 \
+                    libxcb-xinput0 \
+                    libxcb-xkb1 \
+                    libxkbcommon-x11-0 \
+                    libxss1 && \
+    apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+# Simulate SetUpFreeSurfer.sh
+ENV FSL_DIR="/opt/fsl" \
+    OS="Linux" \
+    FS_OVERRIDE=0 \
+    FIX_VERTEX_AREA="" \
+    FSF_OUTPUT_FORMAT="nii.gz" \
+    FREESURFER_HOME="/usr/local/freesurfer/8.1.0"
+ENV SUBJECTS_DIR="$FREESURFER_HOME/subjects" \
+    FUNCTIONALS_DIR="$FREESURFER_HOME/sessions" \
+    MNI_DIR="$FREESURFER_HOME/mni" \
+    LOCAL_DIR="$FREESURFER_HOME/local" \
+    MINC_BIN_DIR="$FREESURFER_HOME/mni/bin" \
+    MINC_LIB_DIR="$FREESURFER_HOME/mni/lib" \
+    MNI_DATAPATH="$FREESURFER_HOME/mni/data"
+ENV PERL5LIB="$MINC_LIB_DIR/perl5/5.8.5" \
+    MNI_PERL5LIB="$MINC_LIB_DIR/perl5/5.8.5" \
+    PATH="$FREESURFER_HOME/bin:$FREESURFER_HOME/tktools:$MINC_BIN_DIR:$PATH"
+
+# Installing freesurfer
+COPY docker/files/freesurfer-exclude.txt /usr/local/etc/freesurfer-exclude.txt
+RUN curl -sSL https://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/8.1.0/freesurfer_ubuntu22-8.1.0_amd64.deb -o /tmp/freesurfer.deb \
+    && dpkg -i /tmp/freesurfer.deb \
+    && rm -f /tmp/freesurfer.deb \
+    && ./${FREESURFER_HOME}/SetUpFreeSurfer.sh \
+    && rm -rf /usr/local/freesurfer/8.1.0/trctrain \
+    && rm -rf /usr/local/freesurfer/8.1.0/python \
+    && rm -rf /usr/local/freesurfer/8.1.0/subjects \
+    && rm -rf /usr/local/freesurfer/8.1.0/average \
+    && rm -rf /usr/local/freesurfer/8.1.0/models \
+    && if [ -f /usr/local/etc/freesurfer-exclude.txt ]; then \
+        cd /usr/local/freesurfer/8.1.0 && \
+        grep -v '^#' /usr/local/etc/freesurfer-exclude.txt | xargs -r rm -rf; \
+    fi
+
+# FSL
+ENV FSLDIR="/opt/fsl" \
+    PATH="/opt/fsl/bin:$PATH" \
+    FSLOUTPUTTYPE="NIFTI_GZ" \
+    FSLMULTIFILEQUIT="TRUE" \
+    FSLLOCKDIR="" \
+    FSLMACHINELIST="" \
+    FSLREMOTECALL="" \
+    FSLGECUDAQ="cuda.q" \
+    LD_LIBRARY_PATH="/opt/fsl/lib:$LD_LIBRARY_PATH"
+
+RUN apt-get update -qq \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/* \
+    && echo "Downloading FSL ..." \
+    && curl -Ls https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/releases/getfsl.sh | sh -s \
+    && rm -rf /opt/fsl/data/atlases \
+        /opt/fsl/data/first \
+        /opt/fsl/data/mist \
+        /opt/fsl/data/possum \
+        /opt/fsl/data/standard/bianca \
+        /opt/fsl/data/standard/tissuepriors \
+        /opt/fsl/doc \
+        /opt/fsl/etc/default_flobs.flobs \
+        /opt/fsl/etc/fslconf \
+        /opt/fsl/etc/js \
+        /opt/fsl/etc/luts \
+        /opt/fsl/etc/matlab \
+        /opt/fsl/extras \
+        /opt/fsl/include \
+        /opt/fsl/python \
+        /opt/fsl/refdoc \
+        /opt/fsl/tcl \
+        /opt/fsl/bin/FSLeyes
+
+# Installing ANTs 2.3.3 (NeuroDocker build)
+# Note: the URL says 2.3.4 but it is actually 2.3.3
+ENV ANTSPATH="/opt/ants/bin"
+ENV PATH="${ANTSPATH}:$PATH"
+WORKDIR /opt/ants
+RUN curl -sSL "https://github.com/ANTsX/ANTs/releases/download/v2.6.2/ants-2.6.2-ubuntu-22.04-X64-gcc.zip" -o ants.zip \
+    && unzip ants.zip \
+    && mv ants-2.6.2/* . \
+    && rmdir ants-2.6.2 \
+    && rm ants.zip
+
+WORKDIR /
+# GPUFIT
+RUN curl -sSLO "https://github.com/ironictoo/Gpufit/releases/download/1.3/Gpufit_1.3.0_linux.zip" \
+    && unzip -d /opt/Gpufit Gpufit*.zip && rm /Gpufit*.zip && mv /opt/Gpufit/Gpufit_1.2.0/* /opt/Gpufit/
+    # && rmdir /opt/Gpufit/Gpufit_1.3.0_linux
+ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/Gpufit/matlab64/matlab
+ENV GPUFIT_PATH="/opt/Gpufit/matlab64"
+
+# HD-BET
+RUN curl -sSLO "https://github.com/MIC-DKFZ/HD-BET/archive/refs/heads/master.zip" \
+    && unzip -d /opt/HD-BET *master.zip && mv /opt/HD-BET/HD-BET-master/* /opt/HD-BET && rm -rf /opt/HD-BET/HD-BET-master && cd /opt/HD-BET && pip install --no-cache-dir -e . \
+    && sed -i 's/~/\//' /opt/HD-BET/HD_BET/paths.py \
+    && mkdir -p /hd-bet_params/release_2.0.0 \
+    && chmod -R 777 /hd-bet_params/release_2.0.0 \
+    && cd /hd-bet_params/release_2.0.0 \
+    && curl -sSLo /hd-bet_params/release_v1.5.0.zip "https://zenodo.org/records/14445620/files/release_v1.5.0.zip?download=1" \
+    && unzip /hd-bet_params/release_v1.5.0.zip -d /hd-bet_params/release_2.0.0 \
+    && rm /hd-bet_params/release_v1.5.0.zip && chmod -R 777 /hd-bet_params/release_2.0.0
+
+# AUTO AIF
+RUN git clone --depth 1 https://github.com/petmri/vascular_function.git /opt/vascular_function \
+    && cd /opt/vascular_function \
+    && grep -vE '^(cupy|tensorrt_cu12)' requirements.txt > filtered-requirements.txt \
+    && pip install --no-cache-dir -r filtered-requirements.txt \
+    && rm filtered-requirements.txt
+
+# ROCKETSHIP
+RUN curl -sSLO "https://github.com/petmri/ROCKETSHIP/archive/refs/heads/dev.zip" \
+    && unzip -d /opt/ROCKETSHIP *dev.zip && rm /dev.zip
+
+# MATLAB
+# Install patched glibc - See https://github.com/mathworks/build-glibc-bz-19329-patch
+# Note: base-dependencies.txt includes libcrypt-dev and linux-libc-dev to enable installation of patched -dev packages
+# WORKDIR /packages
+# RUN apt-get update && apt-get clean && apt-get autoremove && \
+#     wget -q https://github.com/mathworks/build-glibc-bz-19329-patch/releases/download/ubuntu-focal/all-packages.tar.gz && \
+#     tar -x -f all-packages.tar.gz \
+#     --exclude glibc-*.deb \
+#     --exclude libc6-dbg*.deb
+# RUN apt-get install ./*.deb && \
+#     rm -fr /packages
+# WORKDIR /
+
+# Copyright 2019 - 2022 The MathWorks, Inc.
+
+# To specify which MATLAB release to install in the container, edit the value of the MATLAB_RELEASE argument.
+# Use lower case to specify the release, for example: ARG MATLAB_RELEASE=r2021b
+ARG MATLAB_RELEASE=r2023a
+
+# When you start the build stage, this Dockerfile by default uses the Ubuntu-based matlab-deps image.
+# To check the available matlab-deps images, see: https://hub.docker.com/r/mathworks/matlab-deps
+# FROM mathworks/matlab-deps:${MATLAB_RELEASE}
+
+# Declare the global argument to use at the current build stage
+ARG MATLAB_RELEASE
+
+# Install mpm dependencies & tini
+RUN apt-get update \
+    && apt-get install --no-install-recommends --yes \
+    tini \
+    && apt-get clean \
+    && apt-get autoremove \
+    && rm -rf /var/lib/apt/lists/*
+
+# Run mpm to install MATLAB in the target location and delete the mpm installation afterwards.
+# If mpm fails to install successfully then output the logfile to the terminal, otherwise cleanup.
+RUN wget -q https://www.mathworks.com/mpm/glnxa64/mpm \
+    && chmod +x mpm \
+    && ./mpm install \
+    --release=${MATLAB_RELEASE} \
+    --destination=/opt/matlab \
+    --products MATLAB Curve_Fitting_Toolbox Parallel_Computing_Toolbox Statistics_and_Machine_Learning_Toolbox \
+        Image_Processing_Toolbox Optimization_Toolbox \
+    || (echo "MPM Installation Failure. See below for more information:" && cat /tmp/mathworks_root.log && false) \
+    && rm -f mpm /tmp/mathworks_root.log \
+    && ln -s /opt/matlab/bin/matlab /usr/local/bin/matlab \
+    && rm /opt/matlab/sys/os/glnxa64/libstdc++*
+ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/opt/matlab/extern/bin/glnxa64:/opt/matlab/sys/os/glnxa64
+
+# Add "matlab" user and grant sudo permission.
+RUN adduser --shell /bin/bash --disabled-password --gecos "" matlab \
+    && echo "matlab ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/matlab \
+    && chmod 0440 /etc/sudoers.d/matlab
+
+# REST OF THIS
+COPY . .
+ENV MPLCONFIGDIR="/matplotlib"
+
+RUN python3 -m pip install --no-cache-dir -r requirements.txt && python3 -m pip cache purge && \
+    rm -f *.zip
diff --git a/README.md b/README.md
index 05e213b..d087ec2 100644
--- a/README.md
+++ b/README.md
@@ -1,47 +1,238 @@
-# in-house_toolbox
-The `main` branch is stable. Checkout a tag if you want something super stable.
-# Requires FSL, AFNI, Matlab, ROCKETSHIP + parametric_scripts, Freesurfer, and Python.
-Used FSL 6.0, AFNI_20.0.09 'Galba', freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c, Python 3.8.10
+# DCEprep
+
+A preprocessing and analysis pipeline for Dynamic Contrast-Enhanced (DCE) MRI data. Handles VFA-based T1 mapping, bias field correction, z-axis normalization, motion correction, AIF selection, and Ktrans parameter mapping, with automated QC reporting.
+
+![Overview of DCEprep processing steps](overview.png)
+
+#### If you use this software in your research, please cite:
+
+> Barnes S, et al. Automated DCE-MRI processing with DCEPrep for Blood-Brain Barrier permeability in a multi-site aging study. *Pending*. 2026.
+
+---
+
+## Table of Contents
+
+- [Installation](#installation)
+  - [Docker (Recommended)](#docker-recommended-easy-consistent-18-gb)
+  - [Without Docker](#without-docker)
+    - [Dependencies](#dependencies-without-docker)
+- [Data Organization](#data-organization)
+- [Pipeline Structure](#pipeline-structure)
+  - [Preprocessing: preprocess_all.sh](#preprocessing-preprocess_allsh)
+  - [Analysis: DCE_all.sh](#analysis-dce_allsh)
+
+---
+
+## Installation
+
+Clone the repository. The `main` branch is stable. Check out a specific tag for a pinned release.
+
+```bash
+git clone https://github.com/petmri/DCEPrep.git
+```
+
+### Docker (Recommended, easy, consistent, ~18 GB)
+
+The easiest way to get started is with the included `run_docker.sh` script, which will automatically pull the Docker image and launch a container.
+
+> **Important:** Make sure the following are shared (mounted) with the Docker container:
+> - MATLAB license file
+> - FreeSurfer license file
+> - Your data directory
+> - Script preference folder (`docker/files/`)
+> - `/etc/`
+>
+> Edit line 24 of `run_docker.sh` to set your data directory before running.
+
+```bash
+./run_docker.sh
+```
+
+To pull the image manually:
+
+```bash
+docker pull lsaca05/dce:<MATLAB_release>-<branch>
+# Example:
+docker pull lsaca05/dce:R2022a-dev
+```
+
+See [Docker Hub tags](https://hub.docker.com/repository/docker/lsaca05/dce/tags) for available releases.
+
+### Without Docker
+
+Setup a Python virtual environment and install dependencies from `venv_requirements.txt`:
+
+```bash
+cd DCEPrep
+python3 -m venv tf
+source tf/bin/activate
+pip install -r venv_requirements.txt
+```
+
+`conda` is also supported — install packages from `venv_requirements.txt` into your conda environment.
+
+#### Dependencies (Without Docker)
+If running without Docker, in addition to the Python packages in `venv_requirements.txt`, the following software must be installed and accessible in your system's PATH:
+| Dependency | Version / Notes |
+|---|---|
+| FSL | 6.0 |
+| ANTs | 2.6.2 |
+| FreeSurfer | Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c (wm parcellation) |
+| MATLAB | R2023a |
+| Python | 3.8.10 or 3.10 |
+| ROCKETSHIP + parametric_scripts | 1.2 |
+
+---
+
+## Data Organization
+
+All data is assumed to be [BIDS](https://bids-specification.readthedocs.io/) compliant.
+
+---
+
 ## Pipeline Structure
-### `preprocess_all`
-Inputs: `2.nii` `5.nii` `10.nii` `12.nii` `15.nii` `DCE.nii` `aif.nii`
 
-Outputs: `DCE_mc_bfc_norm.nii`, a bunch of others
+### Preprocessing: `preprocess_all.sh`
+
+#### Inputs
+
+| File | Pattern |
+|---|---|
+| VFA images (any number of flip angles) | `sub-##_ses-##_flip-##_VFA.nii.gz` |
+| DCE image | `sub-##_ses-##_DCE.nii.gz` |
+| T1-weighted image | `sub-##_ses-##_T1w.nii.gz` |
+
+#### Main Outputs
+
+| File |
+|---|
+| `dce/sub-##_ses-##_desc-bfcz_DCE.nii.gz` |
+| `anat/sub-##_ses-##_space-DCEref_T1map.nii` |
+| `anat/sub-##_ses-##_space-DCEref_VFA.nii.gz` |
+| `dce/sub-##_ses-##_desc-AIF_T1map.nii.gz` |
+| `anat/sub-##_ses-##_space-DCEref_desc-brain_mask.nii.gz` |
+
+#### Options
+
+| Flag | Description |
+|---|---|
+| `-d [rawdata_path]` | **Required.** Path to your BIDS raw data folder. |
+| `-a [suffix]` | AIF suffix (default: `desc-AIF_mask`). `.nii.gz` is appended automatically. |
+| `-A [mode]` | Enable AutoAIF: `A` (fully automatic), `M` (manual if available), or `T` (manual + training if available). Requires the [vascular_function repo and weights](https://github.com/petmri/vascular_function). |
+| `-b` | Enable first round of bias field correction. |
+| `-B` | Enable second round of bias field correction (post-Z-norm, if enabled). |
+| `-c` | Clean the case's derivative folder before processing. Ensures fresh runs but disables skips. |
+| `-C [name]` | Enable comparison mode. Outputs all files to a named directory within each timepoint. Useful for comparing runs (e.g., no corrections vs. corrections). |
+| `-m` | Enable motion correction. |
+| `-s` | Skip preprocessing if DCE input file already exists. |
+| `-t` | Only run up to T1 mapping. |
+| `-T [dir_path]` | Target specific subject(s)/session(s) (default: `sub-*/ses-*/`). |
+| `-w [path]` | Path to AutoAIF weights file. |
+| `-Z` | Enable z-slice normalization. |
+
+#### Example
+
+```bash
+./preprocess_all.sh -d /media/network_mriphysics/USC-PPG/bids_test/rawdata -b -c -Z -A -C noMC
+```
+
+#### Step Summary
+
+1. **Brain Extraction** of T1w MPRAGE using `HD-BET` with default weights (does not brain mask VFAs).
+2. <details>
+    <summary><b>DCE Motion Correction</b> — FSL <code>mcflirt</code>, targeting 2nd frame with mutual info cost function</summary>
+    <code>mcflirt -in $source_dir/dce/${PREFIX}_DCE.nii.gz -refvol 1 -cost mutualinfo -report -plots -o dce/${PREFIX}_desc-hmc_DCE.nii</code>
+   </details>
+3. <details>
+    <summary><b>MPRAGE → DCE Registration</b> — ANTs <code>antsRegistration</code></summary>
+    <code>
+    antsRegistration --verbose 0 --dimensionality 3 --float 0
+        --collapse-output-transforms 1 --output [ anat/${PREFIX}_${REF_SPACE}_T1w,anat/${PREFIX}_${REF_SPACE}_T1w.nii.gz ]
+        --interpolation Linear --use-histogram-matching 0 --winsorize-image-intensities [ 0.005,0.995 ]
+        --transform Rigid[ 0.1 ] --metric MI[ $DCE_REF_VOL,${source_dir}/anat/${PREFIX}_T1w.nii.gz,1,32,Regular,0.25 ]
+        --convergence [ 1000x500x250x100,1e-6,10 ] --shrink-factors 12x8x4x2 --smoothing-sigmas 4x3x2x1vox
+    </code>
+   </details>
+4. <details>
+    <summary><b>VFA → DCE Registration</b> — ANTs <code>antsRegistration</code></summary>
+    <code>
+    antsRegistration --verbose 0 --dimensionality 3 --float 0
+        --collapse-output-transforms 1 --output [ anat/${PREFIX}_flip-${VFA}_${REF_SPACE},anat/${PREFIX}_flip-${VFA}_${REF_SPACE}_VFA.nii.gz ]
+        --interpolation Linear --use-histogram-matching 0 --winsorize-image-intensities [ 0.005,0.995 ]
+        --transform Rigid[ 0.1 ] --metric MI[ $DCE_REF_VOL,$source_dir/anat/${PREFIX}_flip-${VFA}_VFA.nii.gz,1,32,Regular,0.25 ]
+        --convergence [ 1000x500x250x100,1e-6,10 ] --shrink-factors 12x8x4x2 --smoothing-sigmas 4x3x2x1vox
+    </code>
+   </details>
+5. <details>
+    <summary><b>MPRAGE White Matter Segmentation</b> — FSL <code>fast</code></summary>
+    <code>
+    fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b --nopve -g -o anat/${PREFIX}_label- anat/${PREFIX}_desc-brain_T1w.nii.gz
+    </code>
+   </details>
+6. **Apply MPRAGE → DCE transform to WM mask** — ANTs `antsApplyTransforms`
+7. <details>
+    <summary><b>VFA Bias Field Correction</b> — FSL <code>fast</code></summary>
+    <code>
+    fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -B --nopve -o anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-brain_VFA.nii.gz
+    </code>
+   </details>
+8. **VFA Z-axis Normalization** — double Gaussian fitting (`VFA_norm.py`)
+9. **Second VFA Bias Field Correction** — FSL `fast`
+10. **T1 Map Generation** — ROCKETSHIP
+11. **Apply MPRAGE → DCE transform to brain mask** — ANTs `antsApplyTransforms`
+12. **AIF Drawing via Neural Network** — ensures AIF is included within the brain mask
+13. <details>
+    <summary><b>DCE Bias Field Correction</b> — averages bias fields from the 1st + 8 evenly spaced temporal samples via FSL <code>fast</code></summary>
+    <code>
+    fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b --nopve -o rep_$((rep_interval*i-1)).nii
+    </code>
+    </details>
+14. **DCE Z-axis Normalization** — double Gaussian fitting (`DCE_norm.py`)
+
+---
 
-Options: `-d: specify main data directory (required)`
+### Analysis: `DCE_all.sh`
 
-`-b: enable first round of bias field correction`
+#### Inputs
 
-`-B: enable second round of bias field correction`
+| File |
+|---|
+| `dce/sub-##_ses-##_desc-bfcz_DCE.nii.gz` |
+| `anat/sub-##_ses-##_space-DCEref_T1map.nii` |
+| `anat/sub-##_ses-##_space-DCEref_VFA.nii.gz` |
+| `dce/sub-##_ses-##_desc-AIF_T1map.nii.gz` |
+| `anat/sub-##_ses-##_space-DCEref_desc-brain_mask.nii.gz` |
 
-`-Z: enable z-slice normalization`
+#### Main Outputs
 
-`-c: clean timepoint directory prior to preprocessing (rm all except inputs)`
+| File |
+|---|
+| `sub-##_ses-##_Ktrans.nii` |
+| `sub-##_ses-##_vp.nii` |
+| `case_report.html` |
+| `population_report.html` |
 
-Example call: `./preprocess_all -d /media/network_mriphysics/USC-PPG/data -b -Z -F -c`
-1. **Brain Extraction** using FSL `bet` (does not brain mask VFAs).
-2. **Bias Field Correction** using FSL `FAST`
-3. **VFA Z-axis Normalization** using polynomial fitting `VFA_norm.py`
-4. Second round of **Bias Field Correction** using FSL `FAST`
-5. **VFA Motion Correction** using FSL `mcflirt`
-6. **Generate T1 maps** with ROCKETSHIP
-7. **DCE Motion Correction** using FSL `mcflirt`
-8. Register T1 map with motion corrected DCE series using Freesurfer
-9. Register brain mask to DCE space using Freesurfer, include AIF region, and use to mask motion corrected DCE series
-10. **Bias Field Correction** of DCE series by taking 8 z-slice samples and averaging their bias fields generated by FSL `fast`
-11. Register white matter mask to dynamic space and apply to bias field corrected DCE series
-12. **DCE Z-axis Normalization** using polynomial fitting `DCE_norm.py`
+#### Options
 
-### `DCE_all.sh`
-Inputs: `DCE_bfc_norm.nii` `aif.nii` `t1_map_fixed_use_me.nii.gz`
+| Flag | Description |
+|---|---|
+| `-d [path]` | **Required.** Path to raw BIDS data directory. |
+| `-C [name]` | Enable comparison mode. Copies essential files from a standard run if a preprocessed run of the same name does not exist. |
+| `-f` | Enable FreeSurfer WM parcellation for subregion analysis. |
+| `-s` | Skip cases already processed. |
+| `-S` | Enable smoothing of DCE input. |
+| `-T [dir_path]` | Target specific subject(s)/session(s) (default: `sub-*/ses-*/`). |
 
-Outputs: Ktrans maps, QC reports (overview being `report.png`)
+#### Example
 
-Options: `-d: specify main data directory (required)`
+```bash
+./DCE_all.sh -d /media/network_mriphysics/USC-PPG/bids_test/rawdata -s -C noMC
+```
 
-`-b: enable first round of bias field correction` (do this if you did it before)
+#### Step Summary
 
-Example call: `./DCE_all -d /media/network_mriphysics/USC-PPG/data -b`
-1. Run DCE (ROCKETSHIP)
-2. Create, align, and apply gray matter and CSF masks 
-3. Run QC scripts `auto_analysis.py` and `report.py`
+1. **Ktrans Mapping** — ROCKETSHIP
+2. **Gray Matter & CSF Masking** — create, align, and apply masks with `antsApplyTransforms` and `fslmaths`
+3. **QC Analysis** — run `ktrans_analysis.py` and `ktrans_report.py`
+4. **Case Report** — generate `case_report.html` for each case via `case_report.py`
+5. **Population Report** — after all cases finish, generate `population_report.html` via `population_report.py`
diff --git a/VFA_norm.py b/VFA_norm.py
index 1c66007..6896816 100644
--- a/VFA_norm.py
+++ b/VFA_norm.py
@@ -1,32 +1,43 @@
 import sys
 from pathlib import Path
 import re
-from statistics import mean, pstdev
+from statistics import mean, pstdev, median
 import numpy as np
 # import numpy.polynomial.polynomial as poly
 import matplotlib
 import matplotlib.pyplot as plt
+import multiprocessing
 from numpy.polynomial import Polynomial
 from numpy.polynomial.polynomial import polyval
 import nibabel as nib
+from scipy.optimize import curve_fit
+from lmfit import Model
 matplotlib.use('Agg')
 
 # add as arg? add mask arg?
+GAUSSFIT = True
 POLYFIT = True
 
 def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE NORMALIZATION
     dim = {0, 1, 2}
     mri = nib.load(mri_file1)
     white_matter = nib.load(wm_masked)
-    num = int(re.search(r'\d+', mri_file1.split('/')[-1]).group())
+    num = int(re.search(r'flip-(\d+)', mri_file1.split('/')[-1]).group(1))
+    num = f"{num:02d}"
     mri_data = mri.get_fdata()
     wm_data = white_matter.get_fdata()
     mri_shape = mri_data.shape
     wm_shape = wm_data.shape
     slice_num = min(mri_shape[0], mri_shape[1], mri_shape[2])
-    slice_loc = mri_shape.index(slice_num)
-    mri_data = np.reshape(mri_data, (mri_shape[min(dim-set([slice_loc]))], mri_shape[max(dim-set([slice_loc]))], slice_num))
-    wm_data = np.reshape(wm_data, (wm_shape[min(dim-set([slice_loc]))], wm_shape[max(dim-set([slice_loc]))], slice_num))
+    axis_order = np.argsort([mri_shape[0], mri_shape[1], mri_shape[2]])
+    # Unpack the axis order
+    # usual shape should be x >= y > z
+    z_index, y_index, x_index = axis_order
+    # if x and y are the same, swap them
+    if mri_shape[x_index] == mri_shape[y_index]:
+        x_index, y_index = y_index, x_index
+    mri_data = np.transpose(mri_data, (x_index, y_index, z_index))
+    wm_data = np.transpose(wm_data, (x_index, y_index, z_index))
     wm_mean = []
     orig_img = []
 
@@ -51,8 +62,121 @@ def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE N
     mri_final = mri_data
     wm_final = wm_data
 
-    # apply normalizations
-    if POLYFIT is True:
+    gaussian_params = [0 for i in range(slice_num)]
+    if GAUSSFIT:
+        startat = 0
+        endat = slice_num
+        hadSuccess = False
+        # get histogram of each wm slice
+        for i in range(slice_num):
+            # print("SLICE: " + str(i + 1))
+            a = np.where(wm_data[:, :, i] > 0)
+            hist, bins = np.histogram(wm_data[:,:,i][a].flatten(), bins=100)
+
+            def double_gaussian(x, A1, mu1, sigma1, A2, mu2, sigma2):
+                return (
+                    A1 * np.exp(-0.5 * ((x - mu1) / sigma1) ** 2) +
+                    A2 * np.exp(-0.5 * ((x - mu2) / sigma2) ** 2)
+                )
+
+            # Initial guess for the parameters
+            # count voxels within 1 std of mean
+            area = np.count_nonzero(wm_data[:,:,i][a])
+
+            # prepare for gaussian fitting
+            bin_width = bins[1] - bins[0]
+            model = Model(double_gaussian)
+            amp_guess = 0
+            params = 0
+            # check if slice is empty
+            if area == 0 or pstdev(wm_data[:,:,i][a]) == 0:
+                # just get from next slice
+                # if i == slice_num - 1:
+                #     a = np.where(wm_data[:,:,i-1] > 0)
+                #     area = np.count_nonzero(wm_data[:,:,i-1][a])
+                #     amp_guess = area / pstdev(wm_data[:,:,i-1][a]) * 0.3989 * bin_width
+                #     params = model.make_params(A1=amp_guess*0.1, mu1=wm_mean[i-1], sigma1=pstdev(wm_data[:,:,i-1][a]), A2=amp_guess, mu2=median(wm_data[:,:,i-1][a]), sigma2=pstdev(wm_data[:,:,i-1][a]))
+                # else:
+                #     a = np.where(wm_data[:,:,i+1] > 0)
+                #     area = np.count_nonzero(wm_data[:,:,i+1][a])
+                #     amp_guess = area / pstdev(wm_data[:,:,i+1][a]) * 0.3989 * bin_width
+                #     params = model.make_params(A1=amp_guess*0.1, mu1=wm_mean[i+1], sigma1=pstdev(wm_data[:,:,i+1][a]), A2=amp_guess, mu2=median(wm_data[:,:,i+1][a]), sigma2=pstdev(wm_data[:,:,i+1][a]))
+                # omit slice if empty
+                # if i == 0:
+                #     startat = 1
+                # elif i == slice_num - 1:
+                #     endat = slice_num - 2
+                # continue
+                if hadSuccess and i < endat:
+                    endat = i
+                elif not hadSuccess:
+                    startat = i + 1
+                continue
+            else:
+                hadSuccess = True
+                amp_guess = area / pstdev(wm_data[:,:,i][a]) * 0.3989 * bin_width
+                params = model.make_params(A1=amp_guess*0.1, mu1=wm_mean[i], sigma1=pstdev(wm_data[:,:,i][a]), A2=amp_guess, mu2=median(wm_data[:,:,i][a]), sigma2=pstdev(wm_data[:,:,i][a]))
+
+            result = model.fit(hist, params, x=bins[:-1])
+            # print(result.fit_report())
+            # print(result.best_values)
+            gaussian_params[i] = result.best_values
+
+            # Plot the histogram
+            plt.figure()
+            plt.bar(bins[:-1], hist, width=np.diff(bins), align='edge', alpha=0.5)
+
+            # Plot the fitted curve
+            plt.plot(bins[:-1], result.best_fit, color='red', linewidth=2)
+
+            # Add labels and title
+            plt.title(f"Histogram and Fitted Curve - Slice {i+1}")
+            plt.xlabel("Pixel Value")
+            plt.ylabel("Frequency")
+
+            # make hist directory if it doesn't exist
+            hist_dir = file_dir + '/../figures/hist'
+            Path(hist_dir).mkdir(parents=True, exist_ok=True)
+
+            # Save the figure
+            path1 = file_dir + '/../figures/hist/' + str(num) + '_' + str(i+1) + '_hist.png'
+            plt.savefig(path1)
+            plt.close()
+
+
+        # apply normalizations
+        print("Using Gaussian fitting to normalize FA " + str(num))
+        mu = [0 for i in range(slice_num)]
+        # print(startat, endat)
+        # endat = len(gaussian_params)
+        for i in range(startat, endat):
+            # max(gaussian_params[i][0], gaussian_params[i][3])
+            # if gaussian_params[i][0] > gaussian_params[i][3] and gaussian_params[i][1] > 0 and gaussian_params[i][1] < 1000:# and abs(gaussian_params[i][1] - wm_mean[i]) < abs(gaussian_params[i][4] - wm_mean[i]):
+            #     mu.append(gaussian_params[i][1])
+            # else:
+            #     mu.append(gaussian_params[i][4])
+            # print(i, slice_num)
+            if gaussian_params[i]['A1'] > gaussian_params[i]['A2'] and gaussian_params[i]['mu1'] > 0 and gaussian_params[i]['mu1'] > gaussian_params[i]['mu2'] and gaussian_params[i]['mu1'] < 1500:
+                mu[i] = gaussian_params[i]['mu1']
+            else:
+                mu[i] = gaussian_params[i]['mu2']
+
+            # print("mu " + str(mu[i]))
+            # print("SLICE: " + str(i + 1))
+            # print("mu1 and mu2: " + str(gaussian_params[i]['mu1']) + " " + str(gaussian_params[i]['mu2']))
+            # print("A: " + str(gaussian_params[i]['A1']) + " " + str(gaussian_params[i]['A2']))
+            # print("sigma: " + str(gaussian_params[i]['sigma1']) + " " + str(gaussian_params[i]['sigma2']))
+
+        median_mu = median([m for m in mu if m != 0])
+        # print("median_mu: " + str(median_mu))
+        for i in range(slice_num):
+            if mu[i] == 0:
+                continue
+            scale_factor = median_mu / mu[i]
+            mri_final[:, :, i] *= scale_factor
+            wm_final[:, :, i] *= scale_factor
+
+    elif POLYFIT is True:
         print("Using Polynomial fitting to normalize")
         poly_norm_curve = Polynomial.fit(list(range(slice_num)), wm_mean, 4, w=polyfit_slice_weights)
         norm_slices = polyval(list(range(slice_num)), poly_norm_curve.convert().coef)
@@ -66,9 +190,9 @@ def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE N
     else:
         print("Using Z-normalization")
         # calc stats of all slices
-        data_mean = mean(wm_mean[0:13])
-        std_dev = pstdev(wm_mean[0:13])
-        err = 0.1*std_dev
+        data_mean = mean(wm_mean[0:slice_num-1])
+        std_dev = pstdev(wm_mean[0:slice_num-1])
+        err = 1*std_dev
         # find slices out of range
         min_val = data_mean - err
         max_val = data_mean + err
@@ -97,35 +221,56 @@ def normalize(mri_file1, wm_masked, file_dir):   # THE FUNCTION PERFORMING THE N
             norm_wm.append(0)
 
     # data_mean1 = mean(norm_img)
-
     fig, ax = plt.subplots(figsize=(20, 6))
     ax.plot(range(slice_num), wm_mean, '-ok', label='original')
-    if POLYFIT is True:
+    if POLYFIT is True and GAUSSFIT is False:
         ax.plot(range(slice_num), norm_slices, ':ob', label='fit')
-    ax.plot(range(slice_num), norm_wm, '--og', label='corrected')
+    ax.plot(range(slice_num), norm_wm, '--xg', label='corrected', mfc='none')
+    if GAUSSFIT:
+        ax.plot(range(slice_num), mu, 'o', label='mu', color='grey')
+        ax.plot(range(slice_num), np.ones(slice_num)*median_mu, ':x', label='median_mu', color='lightgreen')
     ax.set_xlabel('Slice #')
     ax.set_ylabel('White Matter Mean')
     ax.set_title("VFA Slice Normalization")
     ax.legend()
 
-    path2 = file_dir + '/' + str(num) +'_BFC_Z.png'
-    plt.savefig(path2)
+    # if figures directory doesn't exist, create it
+    Path('figures').mkdir(parents=True, exist_ok=True)
+
+    path2 = 'figures/' + str(prefix) + '_flip-' + str(num) + '_space-DCEref_desc-bfcz_VFA.svg'
+    plt.savefig(path2, bbox_inches='tight')
+    plt.close()
 
-    mri_final = np.reshape(mri_final, mri_shape)
+    # transpose mri_final back to mri_shape order
+    x_index = mri_final.shape.index(mri_shape[0])
+    y_index = mri_final.shape.index(mri_shape[1])
+    z_index = mri_final.shape.index(mri_shape[2])
+    if x_index == y_index:
+        y_index = set(dim) - {x_index, z_index}
+        y_index = list(y_index)[0]
+    elif x_index == z_index:
+        z_index = set(dim) - {x_index, y_index}
+        z_index = list(z_index)[0]
+    elif y_index == z_index:
+        z_index = set(dim) - {x_index, y_index}
+        z_index = list(z_index)[0]
+
+    mri_final = np.transpose(mri_final, (x_index, y_index, z_index))
+    mri_final = mri_final.astype(np.float32)
+    # cut off z-slices where mu is 0
+    # mri_final = mri_final[:, :, startat:endat]
     final_img = nib.Nifti1Image(mri_final, mri.affine)
-    path3 = file_dir + '/' + str(num) + '_BFC_Z.nii'
+    path3 = file_dir + '/' + str(prefix) + '_flip-' + str(num) + '_space-DCEref_desc-bfcz_VFA.nii.gz'
     nib.save(final_img, path3)
 
+if __name__ == "__main__":
+    dir = Path(sys.argv[1])     # takes timepoint directory as argument
+    prefix = sys.argv[2]
+    BFC = sys.argv[3]
+    file_pattern = r'.*flip-\d+_space-DCEref_desc-bfc_VFA.nii.*' if BFC else r'.*flip-\d+_space-DCEref_desc-brain_VFA.nii.*'
+    files_in_dir = dir.iterdir()
+    file_list = [file for file in files_in_dir if re.search(r'.*flip-\d+_space-DCEref_desc-brain_VFA.nii.*', str(file))]
+    num_processes = len(file_list)
 
-dir = Path(sys.argv[1])     # takes timepoint directory as argument
-files_in_dir = dir.iterdir()
-for file in files_in_dir:
-    if re.search(r'\d+_bfc.nii.*', str(file)):
-        file1 = str(file)
-        mask_file = file1.split('.', 1)
-        mask_file = mask_file[0] + '_wm.nii'
-
-        try:
-            normalize(file1, mask_file, str(dir))   #CALLING THE 'normalize()' FUNCTION TO PERFORM THE NORMALIZATION
-        except FileNotFoundError:
-            normalize(file1, mask_file + ".gz", str(dir))
+    with multiprocessing.Pool(processes=num_processes) as pool:
+        pool.starmap(normalize, [(str(file), str(file).split('_desc-brain', 1)[0] + '_seg-WM_VFA.nii.gz', str(dir)) for file in file_list])
diff --git a/aif_metric.py b/aif_metric.py
new file mode 100644
index 0000000..89006ee
--- /dev/null
+++ b/aif_metric.py
@@ -0,0 +1,72 @@
+import numpy as np
+
+# get metric of AIF
+def quality_peak(aif_curve):
+    peak_ratio = max(aif_curve) / aif_curve[0]
+    # peak_ratio = tf.cast(peak_ratio, tf.float32)
+    return peak_ratio*(100/7.546971123202499)
+
+def quality_tail(aif_curve):
+    # end is mean of last 20% of curve
+    end_ratio = np.mean(aif_curve[-int(float(int(len(aif_curve)))*0.2):]) / aif_curve[0]
+    # end_ratio = tf.cast(end_ratio, tf.float32)
+
+    quality = (1 / (end_ratio + 1)) * (100/0.24035631585328981)
+    # if quality > 200:
+    #     quality = 200
+    return quality
+
+def quality_peak_to_end(aif_curve):
+    peak_ratio = quality_peak(aif_curve)/(100/7.546971123202499)
+    end_ratio = np.mean(aif_curve[-int(float(int(len(aif_curve)))*0.2):]) / aif_curve[0]
+    # end_ratio = tf.cast(end_ratio, tf.float32)
+    
+    return (peak_ratio / end_ratio)*(100/2.4085609761976534)
+
+def quality_peak_time(aif_curve):
+    peak_time = np.argmax(aif_curve)
+    # peak_time = tf.cast(peak_time, tf.float32)
+    num_timeslices = len(aif_curve)
+    qpt = (num_timeslices - peak_time) / num_timeslices
+    return qpt*(100/0.9157142857142857)
+
+def quality_ultimate(aif_curve):
+    peak_ratio = quality_peak(aif_curve)
+    end_ratio = quality_tail(aif_curve)
+    peak_to_end = quality_peak_to_end(aif_curve)
+    peak_time = quality_peak_time(aif_curve)
+
+    # take weighted average
+    return peak_ratio*0.3 + end_ratio*0.3 + peak_to_end*0.3 + peak_time*0.1
+    # return peak_ratio + 0.3*end_ratio + 0.3*peak_to_end + 0.1*peak_time
+
+def quality_peak_new(aif_curve):
+    peak_ratio = max(aif_curve) / np.mean(aif_curve)
+    return (1 / (1 + np.exp(-3.5 * peak_ratio + 7.5))) * (100 / 0.4499714351078607)
+
+def quality_tail_new(aif_curve):
+    end_ratio = np.mean(aif_curve[-int(len(aif_curve) * 0.2):])
+    quality = (1 - (end_ratio / (1.1 * np.mean(aif_curve))) ** 2)
+    return quality * (100 / 0.33436023529043213)
+
+def quality_base_to_mean_new(aif_curve):
+    return (1 - (get_baseline_from_curve(aif_curve) / np.mean(aif_curve)) ** 2) * (100 / 0.8831850876454762)
+
+def quality_peak_time_new(aif_curve):
+    peak_time = np.argmax(aif_curve)
+    num_timeslices = len(aif_curve)
+    qpt = (num_timeslices - peak_time) / num_timeslices
+    return qpt * (100 / 0.9081383928571428)
+
+def quality_ultimate_new(aif_curve):
+    peak_ratio = quality_peak_new(aif_curve)
+    end_ratio = quality_tail_new(aif_curve)
+    base_to_mean = quality_base_to_mean_new(aif_curve)
+    peak_time = quality_peak_time_new(aif_curve)
+
+    # take weighted average
+    return peak_ratio * 0.3 + end_ratio * 0.3 + base_to_mean * 0.3 + peak_time * 0.1
+
+def get_baseline_from_curve(curve):
+    peak_index = np.argmax(curve)
+    return np.mean(curve[:peak_index-1][np.where(curve[:peak_index-1] < curve[0] * 1.75)])
diff --git a/case_report.py b/case_report.py
new file mode 100644
index 0000000..d6badfe
--- /dev/null
+++ b/case_report.py
@@ -0,0 +1,655 @@
+import datetime
+import sys
+import json
+import os
+import subprocess
+import re
+
+import jinja2
+import nibabel as nib
+import numpy as np
+import matplotlib.pyplot as plt
+from nilearn import plotting
+from matplotlib import colors as mcolors
+
+from aif_metric import *
+import glob
+from utils.constants import KTRANS_MIN_THRESHOLD
+
+source_dir = sys.argv[1]
+# source_dir = sys.argv[2]
+prefix = sys.argv[2]
+freesurfer = bool(int(sys.argv[3]))
+# if source_dir[-1] == '/':
+#     source_dir = source_dir[:-1]
+
+files_to_reorient = [f'anat/{prefix}_flip-01_space-DCEref_VFA.nii.gz', f'dce/{prefix}_Ktrans.nii',
+                     f'anat/{prefix}_space-DCEref_T1w.nii.gz', f'anat/{prefix}_space-DCEref_label-WM_mask.nii.gz',
+                     f'anat/{prefix}_space-DCEref_T1map.nii', f'anat/{prefix}_space-DCEref_desc-brain_mask.nii.gz',
+                     f'anat/{prefix}_space-DCEref_label-GM_mask.nii.gz', f'anat/{prefix}_space-DCEref_desc-wmparc.nii.gz',
+                     f'dce/{prefix}_desc-hmc_DCEref.nii.gz', f'dce/{prefix}_DCEref.nii.gz']
+# if c3d exists, reorient files to RAS
+dimensions = 0
+voxel_size = 0
+mean_wm = 0
+mean_gm = 0
+expected_ktrans_vmax = 0.005
+if subprocess.run(['which', 'c3d'], stdout=subprocess.PIPE).returncode == 0:
+    for file in files_to_reorient:
+        if not os.path.exists(file):
+            print(f"File does not exist, skipping: {file}")
+            continue
+        file_no_extension = file.split('.')[0]
+        command = ['c3d', file, '-orient', 'RAS', '-o', file_no_extension + '_RAS.nii.gz']
+        try:
+            subprocess.run(command, check=True)
+            if file == (f'dce/{prefix}_Ktrans.nii'):
+                ktrans = nib.load(f'dce/{prefix}_Ktrans_RAS.nii.gz')
+                ktrans_data = ktrans.get_fdata()
+                ktrans_flipped = np.flip(ktrans_data, axis=1)
+                ktrans_flipped = nib.Nifti1Image(ktrans_flipped, ktrans.affine, ktrans.header)
+                dimensions = ktrans.header.get_data_shape()
+                voxel_size = ktrans.header.get_zooms()
+
+                # plot ktrans
+                fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(15, 5), gridspec_kw={'hspace': -.1, 'wspace': -.1}, dpi=300)
+                #read ktrans coordinates
+                ktrans_coords = int(ktrans.header['qoffset_z'])
+                ktrans_z_slices = min(dimensions)
+                midpt = int(ktrans_coords-5*ktrans_z_slices/2)
+                max_coord = int(ktrans_coords-5*ktrans_z_slices)
+                plotting.plot_anat(ktrans_flipped, display_mode='z', cut_coords=range(ktrans_coords, midpt, -5), axes=axes[0], vmin=0, vmax=expected_ktrans_vmax, cmap='gnuplot', annotate=False, colorbar=True)
+                plotting.plot_anat(ktrans_flipped, display_mode='z', cut_coords=range(midpt, max_coord, -5), axes=axes[1], vmin=0, vmax=expected_ktrans_vmax, cmap='gnuplot', annotate=False)
+                plt.savefig('figures/ktrans.svg', bbox_inches='tight', pad_inches = 0)
+                plt.close()
+        except Exception as e:
+            print("Error running c3d command: " + ' '.join(command))
+            print(e)
+else:
+    # use freesurfer's mri_convert to reorient files to RAS
+    for file in files_to_reorient:
+        if not os.path.exists(file):
+            print(f"File does not exist, skipping: {file}")
+            continue
+        file_no_extension = file.split('.')[0]
+        command = ['mri_convert', '--in_orientation', 'LPI', file, file_no_extension + '_RAS.nii.gz']
+        try:
+            subprocess.run(command, check=True)
+            if file == (f'dce/{prefix}_Ktrans.nii'):
+                ktrans = nib.load(f'dce/{prefix}_Ktrans_RAS.nii.gz')
+                ktrans_data = ktrans.get_fdata()
+                ktrans_flipped = np.flip(ktrans_data, axis=1)
+                ktrans_flipped = nib.Nifti1Image(ktrans_flipped, ktrans.affine, ktrans.header)
+                dimensions = ktrans.header.get_data_shape()
+                voxel_size = ktrans.header.get_zooms()
+
+                # plot Ktrans, different coords
+                fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(15, 5), gridspec_kw={'hspace': -.1, 'wspace': -.1}, dpi=300)
+                plotting.plot_anat(ktrans_flipped, display_mode='z', cut_coords=range(-56, -21, 5), axes=axes[0], vmin=0, vmax=expected_ktrans_vmax, cmap='gnuplot', annotate=False, colorbar=True)
+                plotting.plot_anat(ktrans_flipped, display_mode='z', cut_coords=range(-21, 13, 5), axes=axes[1], vmin=0, vmax=expected_ktrans_vmax, cmap='gnuplot', annotate=False)
+                plt.savefig('figures/ktrans.svg', bbox_inches='tight', pad_inches = 0)
+                plt.close()
+        except Exception as e:
+            print("Error running freesurfer mri_convert (reorient)")
+            dimensions = 'ktrans failed to load'
+            voxel_size = 'ktrans failed to load'
+            print(e)
+
+# use jinja2 to generate html
+env = jinja2.Environment(loader=jinja2.FileSystemLoader(os.path.dirname(os.path.realpath(__file__))))
+template = env.get_template('template.html')
+
+# get date
+date = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+# get commit hash
+try:
+    commit_hash = subprocess.check_output(
+        ['git', 'rev-parse', 'HEAD'],
+        cwd=os.path.dirname(os.path.realpath(__file__))
+    ).decode('ascii').strip()
+except Exception as e:
+    print("Git didn't work correctly. Trying a different way of getting latest dev branch commit hash...")
+    command = ['cat', '.git/refs/heads/dev']
+    commit_hash = subprocess.check_output(
+        command,
+        cwd=os.path.dirname(os.path.realpath(__file__))
+    ).decode('ascii').strip()
+
+# Get ROCKETSHIP repo commit hash
+try:
+    # Assume ROCKETSHIP is in ../ROCKETSHIP relative to this script
+    rocketship_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'ROCKETSHIP'))
+    rocketship_commit_hash = subprocess.check_output(
+        ['git', 'rev-parse', 'HEAD'],
+        cwd=rocketship_dir
+    ).decode('ascii').strip()
+except Exception as e:
+    print("Could not get ROCKETSHIP commit hash.")
+    rocketship_commit_hash = 'unknown'
+
+# get subject id
+subject_id = source_dir.split('/')[-2]
+timepoint = source_dir.split('/')[-1]
+if subject_id.endswith('_timepoint'):
+    subject_id = source_dir.split('/')[-3]
+    # get timepoint
+    timepoint = source_dir.split('/')[-2]
+
+# get institute from DCE.json
+try:
+    with open(os.path.join(source_dir, 'dce', f'{prefix}_DCE.json')) as f:
+        dce = json.load(f)
+    institute = dce['InstitutionName']
+    manufacturer = dce['Manufacturer']
+    MR_machine_model = dce['ManufacturersModelName']
+    field_strength = dce['MagneticFieldStrength']
+except Exception as e:
+    dce = {}
+    institute = 'no json'
+    manufacturer = 'no json'
+    MR_machine_model = 'no json'
+    field_strength = 'no json'
+    print("Error loading DCE.json")
+    print(e)
+
+try:
+    # brain mask
+    plotting.plot_roi(f'anat/{prefix}_desc-brain_mask.nii.gz', bg_img=f'{source_dir}/anat/{prefix}_T1w.nii.gz', cut_coords=(-20, 0, -15), vmin=0, vmax=1, dim=-1, cmap='gray', output_file='figures/t1w_mask.svg', colorbar=False, draw_cross=False, title='mask')
+
+    # T1w segmentation
+    plotting.plot_roi(f'{source_dir}/anat/{prefix}_T1w.nii.gz', bg_img=f'{source_dir}/anat/{prefix}_T1w.nii.gz', cmap='gray', output_file='figures/t1w.svg', cut_coords=(-20, 0, -15), dim=-1, colorbar=False, draw_cross=False)
+    plotting.plot_roi(f'anat/{prefix}_label-WM_mask.nii.gz', bg_img=f'{source_dir}/anat/{prefix}_T1w.nii.gz', cut_coords=(-20, 0, -15), vmin=0, vmax=1, dim=-1, cmap='gray', output_file='figures/t1w_wm.svg', colorbar=False, draw_cross=False, title='wm')
+    plotting.plot_roi(f'anat/{prefix}_label-GM_mask.nii.gz', bg_img=f'{source_dir}/anat/{prefix}_T1w.nii.gz', cut_coords=(-20, 0, -15), vmin=0, vmax=1, dim=-1, cmap='gray', output_file='figures/t1w_gm.svg', colorbar=False, draw_cross=False, title='gm')
+except Exception as e:
+    print("Error plotting T1w segmentation")
+    print(e)
+
+try:
+    # T1w to VFA
+    # plotting.plot_anat(f'anat/{prefix}_label-WM_mask_RAS.nii.gz', cmap='gray', output_file='figures/t1w_to_dceref.svg', cut_coords=7, display_mode='z', annotate=False, colorbar=False, draw_cross=False, title='T1w to DCEref')
+
+    # T1w to dyn
+    t1w_dceref = nib.load(f'anat/{prefix}_space-DCEref_T1w_RAS.nii.gz')
+    t1w_dceref_data = t1w_dceref.get_fdata()
+    t1w_dceref_flipped = np.flip(t1w_dceref_data, axis=1)
+    t1w_dceref_flipped = nib.Nifti1Image(t1w_dceref_flipped, t1w_dceref.affine, t1w_dceref.header)
+    plotting.plot_anat(t1w_dceref_flipped, cmap='gray', output_file=f'figures/{prefix}_space-DCEref_T1w.svg', cut_coords=7, display_mode='z', annotate=False, colorbar=False, draw_cross=False, title='T1w to dyn')
+
+    # flip T1w masks
+    t1w_mask = nib.load(f'anat/{prefix}_space-DCEref_desc-brain_mask_RAS.nii.gz')
+    t1w_mask_data = t1w_mask.get_fdata()
+    t1w_mask_flipped = np.flip(t1w_mask_data, axis=1)
+    t1w_mask_flipped = nib.Nifti1Image(t1w_mask_flipped, t1w_mask.affine, t1w_mask.header)
+    # nib.save(t1w_mask_flipped, str(tp_dir) + '/T1_bet_mask_RAS.nii')
+
+    t1w_wm_mask = nib.load(f'anat/{prefix}_space-DCEref_label-WM_mask_RAS.nii.gz')
+    t1w_wm_mask_data = t1w_wm_mask.get_fdata()
+    t1w_wm_mask_flipped = np.flip(t1w_wm_mask_data, axis=1)
+    t1w_wm_mask_flipped = nib.Nifti1Image(t1w_wm_mask_flipped, t1w_wm_mask.affine, t1w_wm_mask.header)
+    # nib.save(t1w_wm_mask_flipped, str(tp_dir) + '/T1_wm_mask_RAS.nii')
+
+    t1w_gm_mask = nib.load(f'anat/{prefix}_space-DCEref_label-GM_mask_RAS.nii.gz')
+    t1w_gm_mask_data = t1w_gm_mask.get_fdata()
+    t1w_gm_mask_flipped = np.flip(t1w_gm_mask_data, axis=1)
+    t1w_gm_mask_flipped = nib.Nifti1Image(t1w_gm_mask_flipped, t1w_gm_mask.affine, t1w_gm_mask.header)
+    # nib.save(t1w_gm_mask_flipped, str(tp_dir) + '/T1_gm_mask_RAS.nii')
+
+    plotting.plot_roi(t1w_mask_flipped, cmap='gray', bg_img=t1w_dceref_flipped, output_file='figures/t1bet_to_dyn.svg', display_mode='z', vmin=0, vmax=1, dim=0, annotate=True, colorbar=False, draw_cross=False, title='T1w brain mask to DCEref')
+    plotting.plot_roi(t1w_wm_mask_flipped, cmap='gray', bg_img=t1w_dceref_flipped, output_file='figures/t1wm_to_dyn.svg', display_mode='z', vmin=0, vmax=1, dim=0, annotate=True, colorbar=False, draw_cross=False, title='T1w wm to DCEref')
+    plotting.plot_roi(t1w_gm_mask_flipped, cmap='gray', bg_img=t1w_dceref_flipped, output_file='figures/t1gm_to_dyn.svg', display_mode='z', vmin=0, vmax=1, dim=0, annotate=True, colorbar=False, draw_cross=False, title='T1w gm to DCEref')
+except Exception as e:
+    print("Error plotting T1w to dyn")
+    print(e)
+
+try:
+    # T1 map
+    # read txt file
+    FAs = []
+    is_target_line = False
+    with open(f'anat/{prefix}_space-DCEref_T1map.txt', 'r') as f:
+        for line in f:
+            if "User selected TE/TR/FA/TI: " in line:
+                is_target_line = True
+            elif is_target_line:
+                match = re.search(r'\d+', line)
+                if match:
+                    number = int(match.group())
+                    FAs.append(number)
+                else:
+                    is_target_line = False
+    # take last set of non-repeating numbers
+    FAs = FAs[-5:]
+    # convert from list to string
+    FA_str = [str(i) for i in FAs]
+    FA_str = ', '.join(FA_str)
+
+    # now get TR from txt file
+    TR = None
+    is_target_line = False
+    with open(f'anat/{prefix}_space-DCEref_T1map.txt', 'r') as f:
+        for line in f:
+            if "User selected tr: " in line:
+                is_target_line = True
+            elif is_target_line:
+                match = re.search(r'\d+.\d+', line)
+                if match:
+                    TR = match.group()
+                else:
+                    is_target_line = False
+
+    # check if GPU was used
+    GPU = False
+    with open(f'anat/{prefix}_space-DCEref_T1map.txt', 'r') as f:
+        for line in f:
+            if "GPU detected" in line:
+                GPU = True
+    if GPU:
+        GPU_T1 = 'GPU was used'
+    else:
+        GPU_T1 = 'CPU was used'
+except Exception as e:
+    print("Error getting T1 map parameters")
+    FAs = [-1, -1, -1, -1, -1]
+    FA_str = 'Failed to load FAs'
+    TR = -1
+    GPU_T1 = 'Failed to load GPU info'
+    print(e)
+
+try:
+    # T1 map
+    # flip T1 map
+    img = nib.load(f'anat/{prefix}_space-DCEref_T1map_RAS.nii.gz')
+    img_data = img.get_fdata()
+    img_data = np.flip(img_data, axis=0)
+    img_data = np.flip(img_data, axis=1)
+    t1_map_flipped = nib.Nifti1Image(img_data, img.affine, img.header)
+    plotting.plot_anat(t1_map_flipped, cmap='gray', vmin=0, vmax=5000, output_file='figures/t1_map.svg', annotate=False, colorbar=False, draw_cross=False, title='T1 map')
+except Exception as e:
+    print("Error plotting T1 map")
+    print(e)
+
+# try:
+# AIF
+# plot graph of AIF region
+aif = nib.load(f'dce/{prefix}_desc-AIFpos_T1map.nii.gz')
+aif_data = aif.get_fdata()
+# img = nib.load(str(tp_dir) + '/DCE_mc.nii.gz')
+try:
+    img = nib.load(f'dce/{prefix}_desc-hmc_DCE.nii.gz')
+    img_data = img.get_fdata()
+except FileNotFoundError:
+    img = nib.load(f'{source_dir}/dce/{prefix}_DCE.nii.gz')
+    img_data = img.get_fdata()
+# binarize AIF
+aif_data[aif_data > 0] = 1
+aif_data[aif_data < 0] = 0
+# mask DCE where AIF is 1
+# but first ensure that DCE and AIF have same number of dimensions
+if len(aif_data.shape) < len(img_data.shape):
+    aif_data = np.expand_dims(aif_data, axis=-1)
+aif_data_roi = img_data * aif_data
+# sum AIF data for each time point, z-slice independent
+aif_curve = np.sum(aif_data_roi, axis=(0, 1, 2)) / np.sum(aif_data[aif_data > 0])
+# divide by AIF mean of timepoints before contrast agent arrival
+baseline = get_baseline_from_curve(aif_curve)
+aif_curve_ratio = aif_curve / baseline
+
+aif_metric = quality_ultimate_new(aif_curve_ratio)
+
+# plot AIF
+plt.plot(aif_curve_ratio, linewidth=2)
+# plt.text(0.5, 0.95, 'Voxel Baseline Avg SI: ' + str(round(baseline, 2)), transform=plt.gca().transAxes, fontsize=11, verticalalignment='top')
+plt.text(0.5, 0.9, 'AIFitness: ' + str(round(aif_metric, 2)), transform=plt.gca().transAxes, fontsize=18, verticalalignment='top')
+plt.title('AIF Curve', fontsize=18)
+plt.xlabel('Timepoint', fontsize=18)
+plt.ylabel('Normalized Signal Intensity', fontsize=18)
+plt.xticks(fontsize=18)
+plt.yticks(fontsize=18)
+plt.savefig(f'figures/{prefix}_desc-AIF_curve.svg', bbox_inches='tight')
+plt.close()
+
+# save AIF values to file
+np.savetxt('dce/AIF_values.txt', aif_curve_ratio)
+
+# plot AIF overlay
+plt.figure(figsize=(15,5), dpi=250)
+plt.subplot(1,2,1)
+plt.axis('off')
+
+# rotate images
+img_data = np.rot90(img_data, axes=(0,1))
+aif_data = np.rot90(aif_data, axes=(0,1))
+
+# overlay AIF mask
+aif_slice = np.where(aif_data > 0)[2][0]
+cmap = mcolors.LinearSegmentedColormap.from_list('custom cmap', [(0, 0, 0, 0), 'blue', 'green', 'red'])
+plt.imshow(img_data[:,:,aif_slice, 5], cmap='gray')
+plt.imshow(aif_data[:,:,aif_slice], cmap=cmap, alpha=1)
+plt.savefig(f'figures/{prefix}_desc-AIF_overlay.svg', bbox_inches='tight')
+plt.close()
+# except Exception as e:
+#     print("Error plotting AIF")
+#     # print error
+#     print(e)
+
+if freesurfer:
+    # wmparc overlay on DCE
+    wmparc = nib.load(f'anat/{prefix}_space-DCEref_desc-wmparc_RAS.nii.gz')
+    wmparc_data = wmparc.get_fdata()
+    wmparc_flipped = np.flip(wmparc_data, axis=1)
+    wmparc_flipped = nib.Nifti1Image(wmparc_flipped, wmparc.affine, wmparc.header)
+
+    try:
+        dce = nib.load(f'dce/{prefix}_desc-hmc_DCEref_RAS.nii.gz')
+    except FileNotFoundError:
+        dce = nib.load(f'dce/{prefix}_DCEref_RAS.nii.gz')
+    dce_data = dce.get_fdata()
+    dce_flipped = np.flip(dce_data, axis=1)
+    dce_flipped = nib.Nifti1Image(dce_flipped, dce.affine, dce.header)
+    # get 95% percentile of DCE
+    dce_95th = np.percentile(dce_flipped.get_fdata(), 95)
+
+    # overlay wmparc on DCE
+    plt.figure(figsize=(15,5), dpi=250)
+    plt.subplot(1,2,1)
+    plt.axis('off')
+    # rotate images
+    # wmparc_data = np.rot90(wmparc_data, axes=(0,1))
+
+    # overlay wmparc mask on DCE per region
+    try:
+        plotting.plot_roi(dce_flipped, bg_img=dce_flipped, output_file=f'figures/{prefix}_desc-hmc_DCEref.svg', display_mode='z', cut_coords=range(-140, -110, 10), vmin=0, vmax=dce_95th, dim=-1.55, annotate=False, colorbar=False, draw_cross=False, title='DCE', alpha=0)
+        plotting.plot_roi(wmparc_flipped, bg_img=dce_flipped, output_file='figures/wmparc_overlay.svg', display_mode='z', cut_coords=range(-140, -110, 10), cmap='tab20', dim=-1.55, annotate=False, colorbar=False, draw_cross=False, title='wmparc overlay', alpha=0.7)
+    except Exception as e:
+        # plot with default coords
+        plotting.plot_roi(dce_flipped, bg_img=dce_flipped, output_file=f'figures/{prefix}_desc-hmc_DCEref.svg', display_mode='z', vmin=0, vmax=dce_95th, dim=-1, annotate=False, colorbar=False, draw_cross=False, title='DCE', alpha=0)
+        plotting.plot_roi(wmparc_flipped, bg_img=dce_flipped, output_file='figures/wmparc_overlay.svg', display_mode='z', cmap='tab20', dim=-1, annotate=False, colorbar=False, draw_cross=False, title='wmparc overlay', alpha=0.7)
+    # T1 dynamic space
+
+# get DCE parameters
+def extract_value(pattern, text):
+    match = re.search(pattern, text)
+    if match:
+        return match.group(1)
+    return None
+
+try:
+    with open('dce/A_dceR1info.log', 'r') as file:
+        log_text = file.read()
+    RUNA_log = True
+except Exception as e:
+    log_text = ''
+    RUNA_log = False
+    print("Error getting DCE parameters from A_dceR1info.log")
+    print(e)
+
+tr_pattern = r"User selected TR \(ms\):\s+(\d+(\.\d+)?)"
+fa_pattern = r"User selected FA \(degrees\):\s+(\d+)"
+hematocrit_pattern = r"User selected hematocrit \(0 to 1.0\):\s+(\d+\.\d+)"
+snr_threshold_pattern = r"User selected SNR threshold for AIF:\s+(\d+)"
+relaxivity_pattern = r"User selected contrast agent R1 relaxivity \(/mM/sec\):\s+(\d+\.\d+)"
+steady_state_pattern = r"User selected end of steady state time \(image number\):\s+(-?\d+)"
+
+DCE_tr = extract_value(tr_pattern, log_text)
+DCE_fa = extract_value(fa_pattern, log_text)
+hematocrit = extract_value(hematocrit_pattern, log_text)
+snr_threshold = extract_value(snr_threshold_pattern, log_text)
+relaxivity = extract_value(relaxivity_pattern, log_text)
+steady_state = extract_value(steady_state_pattern, log_text)
+blood_t1_pattern = "Average Filtered AIF T1: "
+
+if RUNA_log:
+    # get last line of log file
+    with open('dce/A_dceR1info.log', 'r') as file:
+        match = False
+        for line in file:
+            if line[:-1] == blood_t1_pattern:
+                match = True
+            elif match:
+                blood_t1 = line[:-1]
+                match = False
+        A_last_line = line
+else:
+    A_last_line = 'Failed to load RUNA log'
+
+try:
+    # now get Time Resolution from log file
+    time_resolution = None
+    is_target_line = False
+    B_log = 'dce/B_dcefitted_R1info.log'
+    B_imported_log = 'dce/B_dceimported_R1info.log'
+    if os.path.isfile(B_log):
+        with open(B_log, 'r') as f:
+            for line in f:
+                if "User selected time resolution (sec)" in line:
+                    is_target_line = True
+                elif is_target_line:
+                    match = re.search(r'(\d+)(.*)\d*', line)
+                    if match:
+                        time_resolution = match.group()
+                    else:
+                        is_target_line = False
+    elif os.path.isfile(B_imported_log):
+        with open(B_imported_log, 'r') as f:
+            for line in f:
+                if "User selected time resolution (sec)" in line:
+                    is_target_line = True
+                elif is_target_line:
+                    match = re.search(r'(\d+)(.*)\d*', line)
+                    if match:
+                        time_resolution = match.group()
+                    else:
+                        is_target_line = False
+    RUNB_log = True
+except Exception as e:
+    print("Error getting DCE RUNB parameters from B_dcefitted_R1info.log")
+    print(e)
+    RUNB_log = False
+
+if RUNB_log:
+    def extract_r2_values(log_text):
+        r2_pattern = r"Adjusted R\^2 of AIF fit = (-*\d+\.\d+)"
+        r2_values = re.findall(r2_pattern, log_text)
+        return r2_values
+
+    if os.path.isfile(B_log):
+        with open('dce/B_dcefitted_R1info.log', 'r') as file:
+            log_text = file.read()
+    elif os.path.isfile(B_imported_log):
+        with open('dce/B_dceimported_R1info.log', 'r') as file:
+            log_text = file.read()
+
+    r2_values = extract_r2_values(log_text)
+
+    if len(r2_values) >= 2:
+        r2_aif_fit = r2_values[-2]
+        r2_raw_values = r2_values[-1]
+
+    # get last line of B log file (time elapsed)
+    if os.path.isfile(B_log):
+        with open('dce/B_dcefitted_R1info.log', 'r') as file:
+            for line in file:
+                pass
+            B_last_line = line
+    elif os.path.isfile(B_imported_log):
+        with open('dce/B_dceimported_R1info.log', 'r') as file:
+            for line in file:
+                pass
+            B_last_line = line
+        r2_aif_fit = 'Imported AIF'
+        r2_raw_values = 'Imported AIF'
+else:
+    r2_aif_fit = 'Failed to load RUNB log'
+    r2_raw_values = 'Failed to load RUNB log'
+    B_last_line = 'Failed to load RUNB log'
+
+try:
+    # get GPU info
+    GPU = False
+    latest_log_file = max(glob.glob('dce/dce_*_fit.log'), key=os.path.getctime)
+    latest_log_file_name = os.path.basename(latest_log_file)
+    dce_model = latest_log_file_name.replace('dce_', '').replace('_fit.log', '')
+    with open(latest_log_file, 'r') as f:
+        for line in f:
+            if "Gpufit detected" in line:
+                GPU = True
+    if GPU:
+        GPU_DCE = 'GPU was used'
+    else:
+        GPU_DCE = 'CPU was used'
+    RUND_log = True
+except Exception as e:
+    print("Error getting DCE GPU info from latest dce_*_fit.log")
+    print(e)
+    RUND_log = False
+    GPU_DCE = 'Failed to load GPU info'
+    dce_model = 'Failed to load DCE model'
+
+if RUND_log:
+    # get RUN D time elapsed
+    def extract_elapsed_time(log_text):
+        elapsed_time_pattern = r"Elapsed time is (\d+\.\d+) seconds."
+        match = re.search(elapsed_time_pattern, log_text)
+        if match:
+            return match.group(1)
+        return None
+
+    with open(latest_log_file, 'r') as file:
+        log_text = file.read()
+
+    dce_elapsed_time = extract_elapsed_time(log_text)
+else:
+    dce_elapsed_time = 'Failed to load RUND log'
+
+# Ktrans
+
+# get Ktrans mean wm and gm
+ktrans_wm = nib.load(f'dce/{prefix}_seg-WM_Ktrans.nii.gz')
+ktrans_wm_data = ktrans_wm.get_fdata()
+ktrans_wm_mask = nib.load(f'anat/{prefix}_space-DCEref_label-WM_mask.nii.gz')
+ktrans_wm_mask_data = ktrans_wm_mask.get_fdata()
+# mean_wm = np.nanmean(ktrans_wm_data[ktrans_wm_data > 0])*1000
+ktrans_median_wm = np.nanmedian(ktrans_wm_data[np.logical_and(ktrans_wm_mask_data > 0, ktrans_wm_data > KTRANS_MIN_THRESHOLD)])*1000
+ktrans_std_wm = np.nanstd(ktrans_wm_data[np.logical_and(ktrans_wm_mask_data > 0, ktrans_wm_data > KTRANS_MIN_THRESHOLD)])*1000
+
+ktrans_gm = nib.load(f'dce/{prefix}_seg-GM_Ktrans.nii.gz')
+ktrans_gm_data = ktrans_gm.get_fdata()
+ktrans_gm_mask = nib.load(f'anat/{prefix}_space-DCEref_label-GM_mask.nii.gz')
+ktrans_gm_mask_data = ktrans_gm_mask.get_fdata()
+# mean_gm = np.nanmean(ktrans_gm_data[ktrans_gm_data > 0])*1000
+ktrans_median_gm = np.nanmedian(ktrans_gm_data[np.logical_and(ktrans_gm_mask_data > 0, ktrans_gm_data > KTRANS_MIN_THRESHOLD)])*1000
+ktrans_std_gm = np.nanstd(ktrans_gm_data[np.logical_and(ktrans_gm_mask_data > 0, ktrans_gm_data > KTRANS_MIN_THRESHOLD)])*1000
+
+# get T1 map median wm and gm
+T1_wm = nib.load(f'anat/{prefix}_space-DCEref_label-WM_T1map.nii.gz')
+T1_wm_data = T1_wm.get_fdata()
+T1_wm_median = np.median(T1_wm_data[T1_wm_data > 0])
+T1_wm_std = np.std(T1_wm_data[T1_wm_data > 0])
+
+T1_gm = nib.load(f'anat/{prefix}_space-DCEref_label-GM_T1map.nii.gz')
+T1_gm_data = T1_gm.get_fdata()
+T1_gm_median = np.median(T1_gm_data[T1_gm_data > 0])
+T1_gm_std = np.std(T1_gm_data[T1_gm_data > 0])
+
+# MNI space registration
+# fsl_dir = os.environ['FSLDIR']
+# print(fsl_dir)
+# try:
+#     plotting.plot_anat(os.path.dirname(os.path.realpath(__file__)) + '/MNI152_T1_1mm_brain.nii.gz', title='MNI152_T1_1mm_brain', output_file=tp_dir + '/figures/MNI152_T1_1mm_brain.svg', colorbar=False, draw_cross=False)
+#     plotting.plot_anat(tp_dir + '/t1w_MNIWarped.nii.gz', title='t1w_MNI', cut_coords=(2, -1, 20), output_file=tp_dir + '/figures/t1w_MNI.svg', colorbar=False, draw_cross=False)
+#     plotting.plot_anat(source_dir + '/Ktrans_MNI.nii.gz', title='ktrans_MNI', cut_coords=(2, -1, 20), vmin=0, vmax=0.001, output_file=source_dir + '/figures/Ktrans_MNI.svg', colorbar=False, draw_cross=False)
+# except Exception as e:
+#     print("Error plotting MNI space registration")
+#     print(e)
+
+# if tp_dir != source_dir:
+#     tp_figdir = "../"
+# else:
+#     tp_figdir = ""
+
+data = {
+    'title': subject_id + ' ' + timepoint + ' Report',
+    'heading': 'Summary',
+    'Subject': 'Subject ID: ' + subject_id,
+    'Timepoint': 'Timepoint: ' + timepoint,
+    'Date': 'Date Processed: ' + date,
+    'Commit': 'Commit: ' + commit_hash,
+    'ROCKETSHIP_Commit': 'ROCKETSHIP Commit: ' + rocketship_commit_hash,
+    'Institute': 'Institute: ' + institute,
+    'Machine': 'Machine: ' + manufacturer + ' ' + MR_machine_model + ' ' + str(field_strength) + 'T',
+    'ktrans': '../figures/ktrans.svg',
+    'image_alt1': 'Missing image',
+    'Dimensions': 'Dimensions: ' + str(dimensions),
+    'Voxel_Size': 'Voxel Size: ' + str(voxel_size),
+    'Overlay': '../figures/overlay.svg',
+    'T1w': '../figures/t1w.svg',
+    'T1w_mask': '../figures/t1w_mask.svg',
+    'T1w_gm': '../figures/t1w_gm.svg',
+    'T1w_wm': '../figures/t1w_wm.svg',
+    'T1w_to_DCEref': f'../figures/{prefix}_space-DCEref_T1w.svg',
+    'T1_TR': 'TR: ' + str(TR) + 'ms',
+    'T1_FAs': 'FAs: ' + FA_str,
+    'T1_GPU': str(GPU_T1),
+    'T1_wm_median': 'T1 wm median: ' + str(round(T1_wm_median, 4)),
+    'T1_wm_std': 'T1 wm std: ' + str(round(T1_wm_std, 4)),
+    'T1_gm_median': 'T1 gm median: ' + str(round(T1_gm_median, 4)),
+    'T1_gm_std': 'T1 gm std: ' + str(round(T1_gm_std, 4)),
+    'T1_map': '../figures/t1_map.svg',
+    'displacements' : '../figures/displacements.svg',
+    'AIF_mask': f'../figures/{prefix}_desc-AIF_mask.svg',
+    'AIF_metric' : "AIFitness: " + str(aif_metric),
+    'AIF_curve': f'../figures/{prefix}_desc-AIF_resampledcurve.svg',
+    'AIF_overlay': f'../figures/{prefix}_desc-AIF_overlay.svg',
+    'AIF_graph': f'../figures/{prefix}_desc-AIF_curve.svg',
+    'DCEref': f'../figures/{prefix}_desc-hmc_DCEref.svg',
+    'wmparc_overlay': f'../figures/wmparc_overlay.svg',
+    't1w_dyn' : f'../figures/{prefix}_space-DCEref_T1w.svg',
+    't1w_bet_dyn' : '../figures/t1bet_to_dyn.svg',
+    't1w_wm_dyn' : '../figures/t1wm_to_dyn.svg',
+    't1w_gm_dyn' : '../figures/t1gm_to_dyn.svg',
+    'Z_DCE' : f'../figures/{prefix}_desc-bfcz_DCE.svg',
+    'DCE_TR' : 'Repetition Time: ' + str(DCE_tr) + 'ms',
+    'DCE_FA' : 'Flip Angle: ' + str(DCE_fa) + '°',
+    'Hematocrit' : 'Hematocrit: ' + str(hematocrit),
+    'SNR_Threshold' : 'SNR Threshold: ' + str(snr_threshold),
+    'Relaxivity' : 'Relaxivity: ' + str(relaxivity) + '/mM/sec',
+    'T1_blood' : 'Blood T1: ' + str(blood_t1) + 's',
+    'A_last_line' : str(A_last_line),
+    'Time_Resolution' : 'Time Resolution: ' + str(time_resolution) + 's',
+    'R_squared_fit' : 'R squared of AIF fit (fitted): ' + str(r2_aif_fit),
+    'R_squared_raw' : 'R squared of AIF fit (raw): ' + str(r2_raw_values),
+    'B_last_line' : str(B_last_line),
+    'DCE_AIF_fit' : '../figures/dceAIF_fitting.png',
+    'DCE_AIF_timecurve' : '../figures/dce_timecurves.png',
+    'DCE_model' : 'Model: ' + dce_model,
+    'GPU_DCE' : str(GPU_DCE),
+    'DCE_elapsed_time' : 'Elapsed time: ' + str(dce_elapsed_time) + 's',
+    'ktrans_zeros' : f'../figures/{prefix}_desc-zeros.png',
+    'ktrans_analysis' : f'../figures/{prefix}_desc-analysis.png',
+    'ktrans_wm_mean' : 'Mean wm Ktrans: ' + str(round(mean_wm, 4)),
+    'ktrans_wm_median' : 'Median wm Ktrans: ' + str(round(ktrans_median_wm, 4)),
+    'ktrans_wm_std' : 'Std wm Ktrans: ' + str(round(ktrans_std_wm, 4)),
+    'ktrans_gm_mean' : 'Mean gm Ktrans: ' + str(round(mean_gm, 4)),
+    'ktrans_gm_median' : 'Median gm Ktrans: ' + str(round(ktrans_median_gm, 4)),
+    'ktrans_gm_std' : 'Std gm Ktrans: ' + str(round(ktrans_std_gm, 4)),
+    'MNI_img' : '../figures/MNI152_T1_1mm_brain.svg',
+    'MNI_T1w' : '../figures/t1w_MNI.svg',
+    'MNI_Ktrans' : '../figures/ktrans_MNI.svg',
+}
+
+# insert VFAs into template
+flips = ['flip-01', 'flip-02', 'flip-03', 'flip-04', 'flip-05', 'flip-06', 'flip-07']
+data['FAs'] = []
+data['Zs'] = []
+for i in range(len(FAs)):
+    data['FAs'].append('FA ' + str(FAs[i]))
+    data['num_FAs'] = len(FAs)
+    data['Zs'].append(f'../figures/{prefix}_{flips[i]}_space-DCEref_desc-bfcz_VFA.svg')
+
+output = template.render(data)
+
+# write html to file
+with open(f'reports/{prefix}_desc-casereport.html', 'w') as f:
+    f.write(output)
+
+print(f'Report generated in reports/{prefix}_desc-casereport.html')
diff --git a/chopping_block/compare_2cxm.py b/chopping_block/compare_2cxm.py
deleted file mode 100644
index b6c43db..0000000
--- a/chopping_block/compare_2cxm.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import nibabel as nib
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-cpu_file = '/media/network_mriphysics/GRASP/500181/DCE_2cxm_cpu/DCEBBB_flip_500181-2slices_2cxm_fit_Ktrans.nii'
-gpu_file = '/media/network_mriphysics/GRASP/500181/DCE_2cxm/DCEBBB_flip_500181-2slices_2cxm_fit_Ktrans.nii'
-
-print('Processing file: '+cpu_file)
-cpu_img = nib.load(cpu_file)
-cpu_img_data = cpu_img.get_data()
-
-print('Processing file: '+gpu_file)
-gpu_img = nib.load(gpu_file)
-gpu_img_data = gpu_img.get_data()
-
-
-gpu_filter = gpu_img_data[cpu_img_data>0]
-cpu_filter = cpu_img_data[cpu_img_data>0]
-cpu_filter = cpu_filter[gpu_filter>0]
-gpu_filter = gpu_filter[gpu_filter>0]
-
-gpu_filter = gpu_filter[cpu_filter<0.1]
-cpu_filter = cpu_filter[cpu_filter<0.1]
-cpu_filter = cpu_filter[gpu_filter<0.1]
-gpu_filter = gpu_filter[gpu_filter<0.1]
-
-gpu_small = gpu_filter[::50]
-cpu_small = cpu_filter[::50]
-
-print("Total voxels: ",gpu_img_data.size)
-print("Filtered voxels (median): ",cpu_filter.size)
-print("Plot voxels: ",cpu_small.size)
-
-difference = np.subtract(gpu_filter,cpu_filter)
-diff_abs = np.abs(difference)
-diff_median = np.median(diff_abs)
-diff_mean = np.mean(diff_abs)
-print("Median difference: ",diff_median)
-print("Mean difference: ",diff_mean)
-
-plt.rcParams.update({'font.size': 16})
-plt.figure()
-ax = plt.axes()
-plt.scatter(cpu_small,gpu_small,marker='o',s=10)
-#plt.plot(age_list_short,naa_list_short,'o', xx, yy)
-plt.title('2CXM DCE Fitting')
-plt.xlabel('Ktrans (CPU - Matlab)')
-plt.ylabel('Ktrans (GPU - GPUFit)')
-ax.set_ylim([0, 0.1])
-ax.set_xlim([0, 0.1])
-
-plt.show()
\ No newline at end of file
diff --git a/chopping_block/compare_dixon.py b/chopping_block/compare_dixon.py
deleted file mode 100644
index 9046a79..0000000
--- a/chopping_block/compare_dixon.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import nibabel as nib
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-cpu_file = '/media/network_mriphysics/HAT_data/LLU/15110842 MR2/6echo_processed/6echo_fat_fraction.nii.gz'
-gpu_file = '/media/network_mriphysics/HAT_data/LLU/15110842 MR2/6echo_processed_gpu/6echo_fat_fraction.nii.gz'
-
-print('Processing file: '+cpu_file)
-cpu_img = nib.load(cpu_file)
-cpu_img_data = cpu_img.get_data()
-
-print('Processing file: '+gpu_file)
-gpu_img = nib.load(gpu_file)
-gpu_img_data = gpu_img.get_data()
-
-
-gpu_filter = gpu_img_data[cpu_img_data>0]
-cpu_filter = cpu_img_data[cpu_img_data>0]
-gpu_filter = gpu_filter[cpu_filter<100]
-cpu_filter = cpu_filter[cpu_filter<100]
-
-#cpu_filter = cpu_filter[gpu_filter<0.1]
-#gpu_filter = gpu_filter[gpu_filter<0.1]
-
-gpu_small = gpu_filter[::500]
-cpu_small = cpu_filter[::500]
-
-print("Total voxels: ",gpu_img_data.size)
-print("Filtered voxels (median): ",cpu_filter.size)
-print("Plot voxels: ",cpu_small.size)
-
-difference = np.subtract(gpu_filter,cpu_filter)
-diff_abs = np.abs(difference)
-diff_median = np.median(diff_abs)
-diff_mean = np.mean(diff_abs)
-print("Median difference: ",diff_median)
-print("Mean difference: ",diff_mean)
-
-plt.rcParams.update({'font.size': 16})
-plt.figure()
-ax = plt.axes()
-plt.scatter(cpu_small,gpu_small,marker='o',s=10)
-#plt.plot(age_list_short,naa_list_short,'o', xx, yy)
-plt.title('6 echo Dixon Fitting')
-plt.xlabel('Fat Fraction (CPU - SciPy)')
-plt.ylabel('Fat Fraction (GPU - GPUFit)')
-ax.set_ylim([0, 60])
-ax.set_xlim([0, 60])
-
-plt.show()
\ No newline at end of file
diff --git a/chopping_block/compare_etofts.py b/chopping_block/compare_etofts.py
deleted file mode 100644
index 6d0479b..0000000
--- a/chopping_block/compare_etofts.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import nibabel as nib
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-cpu_file = '/media/network_mriphysics/GRASP/500181/DCE_etofts_cpu/DCEBBB_flip_500181-2slices_ex_tofts_fit_Ktrans.nii'
-gpu_file = '/media/network_mriphysics/GRASP/500181/DCE_etofts_gpu_contraints/DCEBBB_flip_500181-2slices_ex_tofts_fit_Ktrans.nii'
-
-cpu_file = '/media/network_mriphysics/GRASP/500181/DCE_etofts_cpu/DCEBBB_flip_500181-2slices_ex_tofts_fit_vp.nii'
-gpu_file = '/media/network_mriphysics/GRASP/500181/DCE_etofts_gpu_contraints/DCEBBB_flip_500181-2slices_ex_tofts_fit_vp.nii'
-
-print('Processing file: '+cpu_file)
-cpu_img = nib.load(cpu_file)
-cpu_img_data = cpu_img.get_data()
-
-print('Processing file: '+gpu_file)
-gpu_img = nib.load(gpu_file)
-gpu_img_data = gpu_img.get_data()
-
-
-gpu_filter = gpu_img_data[cpu_img_data>0.0001]
-cpu_filter = cpu_img_data[cpu_img_data>0.0001]
-cpu_filter = cpu_filter[gpu_filter>0.0001]
-gpu_filter = gpu_filter[gpu_filter>0.0001]
-
-gpu_filter = gpu_filter[cpu_filter<0.99]
-cpu_filter = cpu_filter[cpu_filter<0.99]
-cpu_filter = cpu_filter[gpu_filter<0.99]
-gpu_filter = gpu_filter[gpu_filter<0.99]
-
-gpu_small = gpu_filter[::50]
-cpu_small = cpu_filter[::50]
-
-print("Total voxels: ",gpu_img_data.size)
-print("Filtered voxels (median): ",cpu_filter.size)
-print("Plot voxels: ",cpu_small.size)
-
-difference = np.subtract(gpu_filter,cpu_filter)
-diff_abs = np.abs(difference)
-diff_percent = np.divide(difference,cpu_filter)
-diff_percent_mean = np.mean(diff_percent)*100
-diff_median = np.median(diff_abs)
-diff_mean = np.mean(diff_abs)
-print("Median difference: ",diff_median)
-print("Mean difference: ",diff_mean)
-print("Mean percent diff: ",diff_percent_mean,"%")
-
-plt.rcParams.update({'font.size': 16})
-plt.figure()
-ax = plt.axes()
-plt.scatter(cpu_small,gpu_small,marker='o',s=10)
-#plt.plot(age_list_short,naa_list_short,'o', xx, yy)
-plt.title('Extended Tofts DCE Fitting')
-plt.xlabel('Ktrans (CPU - Matlab)')
-plt.ylabel('Ktrans (GPU - GPUFit)')
-ax.set_ylim([0, 0.5])
-ax.set_xlim([0, 0.5])
-
-plt.show()
diff --git a/chopping_block/compare_gpu_cpu_dce.m b/chopping_block/compare_gpu_cpu_dce.m
deleted file mode 100644
index fdd6a3b..0000000
--- a/chopping_block/compare_gpu_cpu_dce.m
+++ /dev/null
@@ -1,756 +0,0 @@
-%% INPUTS
-%-----------
-% Human CBF = 22-55 ml/min/100ml (Leenders et al Brain 1990)
-% Human CBV = 2.7-8.6% (Leenders et al Brain 1990)
-% Human CBV = 1.3%,2.6% WM,GM (Sourbron et al MRM 2009)
-% Rat CBF in parietal cortex = 129�18 ml/100g/min  (Adam et al JCBFM 2003)
-% Rat CBV in parietal cortex = 2.1�0.38 ml/100g  (Adam et al JCBFM 2003)
-% Human/Rat Ve Brain = 5% - 9% (He MRM 2007, Bender MRM 2009)
-% Human/Rat Ve Brain = 15% - 30% (Sykova Physiol Rev 2008)
-% Ktrans health brain = 0.5-3*10^-3/min (Taheri 2011 and our data)
-% Ktrans rat brain = 0 - 0.6*10^-3/min (Ewing 2003)
-% Ktrans glioma = 10-50*10^-3/min (Choi 2013)
-snr_adjust = 0;
-double_fit = 1;
-average_across_offset = true;
-show_plots = true;
-save_data = true;
-
-% Full Run
-% ktrans_list     = logspace(-1,1.7,20).*10^-3; % in /min or ml/min/ml
-% vp_list         = [0.01 0.02 0.04 0.08];         % in volume fraction (ml/ml)
-% ve_list         = [0.03 0.1 0.3];              % in volume fraction (ml/ml)
-% fp_list         = [0.20 0.60];         % in ml/min/ml or /min
-% % PS            = derived from Fp and Ktrans = (Fp*PS)/(Fp + PS)
-% ta_list         = [5 15 30];                % in minutes
-% snr_list        = [30 300];               % SNR of the pre SI
-% noise_repeats   = 200;
-% time_resolution_list = [0.5 1.0 15.4 90];         % in seconds (15.4 human)
-% baseline_time   = 91;       % in seconds, time to collect baseline images
-
-% GPU ETofts Run
-ktrans_list     = [10 20 30 40 50 60 70 80 90].*10^-3; % in /min or ml/min/ml
-vp_list         = [0.05];         % in volume fraction (ml/ml)
-ve_list         = [0.4];              % in volume fraction (ml/ml)
-fp_list         = [1];         % in ml/min/ml or /min
-% PS            = derived from Fp and Ktrans = (Fp*PS)/(Fp + PS)
-ta_list         = [5];                % in minutes
-snr_list        = [5 50];               % SNR of the pre SI
-noise_repeats   = 100;
-time_resolution_list = [1];         % in seconds (15.4 human)
-time_offset_list= [0];
-baseline_time   = 15;       % in seconds, time to collect baseline images
-
-% number_cpus     = 4;
-fit_dce_model   ='ex_tofts';        % used to fit the tissue curve
-                                    % tofts, ex_tofts, 2cxm, patlak, 
-                                    % tissue_uptake, fxr, auc
-gen_dce_model   ='2cxm';            % used to generate the tissue curve
-                                    % ex_tofts, patlak, 2cxm, 2cxm_binding
-aif_curve       = 'parker_multihance';         % 'tofts' 'usc' 'parker' 
-                                    % 'parker_multihance' 'exponential'
-max_gd_aif      = 2;                % in mmol, exponential model only
-gd_decay_time   = 10;               % in minutes, exponential model only
-
-aif_t1_pre      = 1200;     % in ms
-tissue_t1_pre   = 1800;     % in ms
-alpha           = 1000;     % arbitrary scalar for SI
-tr              = 8.3;      % in ms
-fa              = 15;       % in degrees
-relaxivity      = 5.5;      % in /mM/sec
-% baseline_images = ;       % number of collected baseline images
-Ka              = 1.5;      % binding constant in /mM, for 2cxm_binding 
-
-time_resolution_list = time_resolution_list./60;%convert to minutes
-baseline_time = baseline_time./60;              %convert to minutes
-time_offset_list = time_offset_list./-60;       %convert to minutes
-% ROI_size = 100;
-% snr_list = [snr_list; snr_list*sqrt(ROI_size)];
-%-----------
-
-%% Processing
-% Sanity check
-if max(ktrans_list)>=min(fp_list)
-    error('Ktrans cannot be greater than Fp');
-end
-if max(time_resolution_list)>baseline_time
-    error('Baseline shorter than time resolution, would result in zero baseline images');
-end
-        
-disp('Starting Simulation')
-disp(datestr(now))
-disp(' ');
-tic
-
-% Inner variables can all be run with a single call to the fitting function
-inner_variable_sizes = [length(ktrans_list) length(vp_list) length(ve_list) length(fp_list)];
-length_inner_variables = length(ktrans_list)*length(vp_list)*length(ve_list)*length(fp_list);
-% Outer variables involve changes to the AIF and therefore require multiple
-% calls to the fitting function
-outer_variable_sizes = [noise_repeats length(ta_list) length(time_resolution_list) length(time_offset_list) length(snr_list)];
-length_outer_variables = noise_repeats*length(ta_list)*length(time_resolution_list)*length(time_offset_list)*length(snr_list);
-toffset_location = numel(inner_variable_sizes)+4;
-repeats_location = numel(inner_variable_sizes)+1;
-
-% number_time_arrays = length(ta_list)*length(time_resolution_list);
-% time_array_length = 0;
-% time_array_list = cell(number_time_arrays,1);
-% for i=1:number_time_arrays
-%     [ta_index, tres_index] = ind2sub([length(ta_list) length(time_resolution_list)],i);
-%     ta = ta_list(ta_index);
-%     time_resolution = time_resolution_list(tres_index);
-%     
-%     time_array_list{i} = 0:time_resolution:ta;
-%     time_array_list{i} = time_array_list{i}+time_offset;
-%     time_array_list{i}(1) = 0;
-%     if length(time_array_list{i})>time_array_length
-%         time_array_length = length(time_array_list{i});
-%     end
-% end
-% All time arrays need to be the same size, pad with zeros
-% for i=1:length(ta_list)
-%     time_array_list{i} = cat(2,zeros(1,time_array_length-length(time_array_list{i})),time_array_list{i});
-% end
-
-% Reserve Space for results
-exponential_ktrans = zeros(length_inner_variables,length_outer_variables);
-exponential_vp = zeros(length_inner_variables,length_outer_variables);
-exponential_ve = zeros(length_inner_variables,length_outer_variables);
-exponential_fp = zeros(length_inner_variables,length_outer_variables);
-
-exponential_residual = zeros(length_inner_variables,length_outer_variables);
-exponential_ktrans_95ci = zeros(length_inner_variables,length_outer_variables,2);
-exponential_vp_95ci = zeros(length_inner_variables,length_outer_variables,2);
-exponential_ve_95ci = zeros(length_inner_variables,length_outer_variables,2);
-exponential_fp_95ci = zeros(length_inner_variables,length_outer_variables,2);
-
-if double_fit
-    d_exponential_ktrans = zeros(length_inner_variables,length_outer_variables);
-    d_exponential_vp = zeros(length_inner_variables,length_outer_variables);
-    d_exponential_ve = zeros(length_inner_variables,length_outer_variables);
-    d_exponential_fp = zeros(length_inner_variables,length_outer_variables);
-
-    d_exponential_residual = zeros(length_inner_variables,length_outer_variables);
-    d_exponential_ktrans_95ci = zeros(length_inner_variables,length_outer_variables,2);
-    d_exponential_vp_95ci = zeros(length_inner_variables,length_outer_variables,2);
-    d_exponential_ve_95ci = zeros(length_inner_variables,length_outer_variables,2);
-    d_exponential_fp_95ci = zeros(length_inner_variables,length_outer_variables,2);
-end
-
-
-% Launch pool if not already running, then disable warnings
-poolobj = gcp;
-pctRunOnAll warning 'off'
-
-
-% pp = ProgressBar(length_inner_variables*length_outer_variables);
-barWidth= int32( 100/3 );
-if length_outer_variables>10000
-    progessbar_outer = 1;
-else
-    progessbar_outer = 0;
-end
-
-if progessbar_outer
-    if length_outer_variables>1000000
-        warning('timed progress bar could significantly slow simulation');
-    end
-    pp =  TimedProgressBar( length_outer_variables, barWidth, ...
-        'Time Remaining: ', ', completed ', 'Concluded in ' );
-else
-    if length_inner_variables*length_outer_variables>1000000
-        warning('timed progress bar could significantly slow simulation');
-    end
-    pp =  TimedProgressBar( length_inner_variables*length_outer_variables, barWidth, ...
-        'Time Remaining: ', ', completed ', 'Concluded in ' );
-end
-
-% Outer loop is over variables that require changes to the AIF (noise, TA,
-% time res, SNR)
-for j=1:length_outer_variables
-    if progessbar_outer
-        pp.progress;
-    end
-    [repeats_index, ta_index, tres_index, toffset_index, snr_index] = ind2sub(outer_variable_sizes,j);
-    ta = ta_list(ta_index);
-    time_resolution = time_resolution_list(tres_index);
-    time_offset = time_offset_list(toffset_index);
-    snr_level = snr_list(snr_index);
-    
-    % For time resolution simulation
-    if snr_adjust
-        if time_resolution==0.25
-            snr_level = 30;
-        elseif time_resolution==1.0
-            snr_level = 60;
-        elseif time_resolution==4.0
-            snr_level = 120;
-        end
-    end
-    
-    % The curve generation and down sampling is time consuming, do not
-    % repeat if doing a noise repeat
-    if repeats_index==1
-        time_array = 0:time_resolution:ta;
-        time_array = time_array+time_offset;
-        time_array(1) = 0;
-
-        baseline_images = floor(baseline_time/time_resolution_list(tres_index));
-        % Reserve Space for tissue matrix
-    %     if j==1
-    %         noise_stdv = zeros(length(time_array),1);
-    %         noise_stdv_tissue = zeros(length(time_array),length_inner_variables);
-    %         noise_stdv_tissue_avg = zeros(length(time_array)/6,length_inner_variables);
-    %     end
-    %     
-    %     tissue_si_matrix = zeros(length(time_array),length_inner_variables);
-    %     tissue_si_noisy_matrix = zeros(length(time_array),length_inner_variables);
-        tissue_gd_matrix = zeros(length(time_array),length_inner_variables);
-        tissue_gd_matrix_noisy = zeros(length(time_array),length_inner_variables);
-
-        % Calculate AIF concentration and SI
-        if strcmp(aif_curve, 'tofts')
-            % Tofts JMRI 1997 published AIF
-            dose = 0.05; % in mmole/kg
-            A = dose * 3.99;
-            B = dose * 4.78;
-            c = 0.144;
-            d = 0.011;
-            aif_function = @(t) (A*exp(-c*t)+B*exp(-d*t)).*logical(t);
-        elseif strcmp(aif_curve, 'usc')
-            % USC Fitted AIF
-            A = 1.0372;
-            B = 0.8375;
-            c = 0.0155;
-            d = 0.5115;
-            aif_function = @(t) (A*exp(-c*t)+B*exp(-d*t)).*logical(t);
-        elseif strcmp(aif_curve, 'parker')    
-            % Parker et al. MRM 2006
-            A_aif_1     = 0.809;
-            sigma_aif_1 = 0.0563;
-            T_aif_1     = 0.17046;
-            A_aif_2     = 0.330;
-            sigma_aif_2 = 0.132;
-            T_aif_2     = 0.365;
-            alpha_aif   = 1.050;
-            beta_aif    = 0.1685;
-            s_aif       = 38.078;
-            tau_aif     = 0.483;
-
-            aif_function = @(t) (A_aif_1./(sigma_aif_1.*sqrt(2.*pi())).*exp(-(t-T_aif_1).^2./(2.*sigma_aif_1.^2)) + ...
-                A_aif_2./(sigma_aif_2.*sqrt(2.*pi()))*exp(-(t-T_aif_2).^2./(2.*sigma_aif_2.^2)) + ...
-                alpha_aif.*exp(-beta_aif.*t)./(1+exp(-s_aif.*(t-tau_aif))) ).*logical(t);
-        elseif strcmp(aif_curve, 'parker_multihance')
-            % Multihance estimate using Parker et al. MRM 2006 as baseline
-            A_aif_1     = 0.409; %
-            sigma_aif_1 = 0.0563;
-            T_aif_1     = 0.17046;
-            A_aif_2     = 0.160; %
-            sigma_aif_2 = 0.132;
-            T_aif_2     = 0.365;
-            alpha_aif   = 0.6646; %
-            beta_aif    = 0.5819;
-            alpha_aif_2   = 0.6695; %
-            beta_aif_2    = 0.0175; %
-            s_aif       = 38.078;
-            tau_aif     = 0.483;
-
-            aif_function = @(t) (A_aif_1./(sigma_aif_1.*sqrt(2.*pi())).*exp(-(t-T_aif_1).^2./(2.*sigma_aif_1.^2)) + ...
-                A_aif_2./(sigma_aif_2.*sqrt(2.*pi()))*exp(-(t-T_aif_2).^2./(2.*sigma_aif_2.^2)) + ...
-                alpha_aif.*exp(-beta_aif.*t)./(1+exp(-s_aif.*(t-tau_aif))) + ...
-                alpha_aif_2.*exp(-beta_aif_2.*t)./(1+exp(-s_aif.*(t-tau_aif))) ).*logical(t);
-        elseif strcmp(aif_curve, 'exponential')
-            % Simple exponential with parameters specified above
-            aif_function = @(t) (max_gd_aif*exp(-t/gd_decay_time)).*logical(t);
-        else
-            error([aif_curve ' is not a valid AIF curve']);
-        end
-
-    %     aif_gd = arrayfun(aif_function,time_array);
-        aif_gd = down_sample(aif_function,time_array,0.1/60);
-        aif_t1_post = 1./(1/aif_t1_pre+aif_gd.*(relaxivity*1/1000));
-        aif_si_pre = sind(fa)*alpha*(1-exp(-tr/aif_t1_pre))/(1-cosd(fa)*exp(-tr/aif_t1_pre));
-        aif_si_post = sind(fa).*alpha.*(1-exp(-tr./aif_t1_post))./(1-cosd(fa).*exp(-tr./aif_t1_post));
-    end
-    
-    % Add noise to SI, noise is in the real and img channels when
-    % aquired, the mag is then taken for the image, this gives the noise
-    % (which is white Gaussian for the acquisition) a Rician distribution
-    noise_real = normrnd(0,mean(aif_si_pre)/snr_level,size(aif_si_post));
-    noise_img = normrnd(0,mean(aif_si_pre)/snr_level,size(aif_si_post));
-    aif_si_post_noisy = sqrt( (aif_si_post+noise_real).^2 + noise_img.^2);
-    noise_real = normrnd(0,mean(aif_si_pre)/(snr_level*sqrt(baseline_images)),size(aif_si_pre));
-    noise_img = normrnd(0,mean(aif_si_pre)/(snr_level*sqrt(baseline_images)),size(aif_si_pre));
-    aif_si_pre_noisy = sqrt( (aif_si_pre+noise_real).^2 + noise_img.^2);
-    
-    % Now get the noisy aif concentration
-    sstar = (1-exp(-tr/aif_t1_pre))/(1-cosd(fa)*exp(-tr/aif_t1_pre));
-    si_ratio_noisy = aif_si_post_noisy./aif_si_pre_noisy;
-    aif_r1_post_noisy = -1./tr*log((sstar.*si_ratio_noisy-1)./(si_ratio_noisy.*sstar.*cosd(fa)-1));
-    aif_gd_noisy = (aif_r1_post_noisy-1/aif_t1_pre)/(relaxivity/1000);
-    %Remove any complex points (introduced if lots of noise)
-    max_aif = max(real(aif_gd_noisy));
-    aif_gd_noisy(aif_gd_noisy~=real(aif_gd_noisy)) = max_aif;
-
-%     noise_stdv(:,1) = ((aif_si_post-aif_si_post_noisy).^2)'+noise_stdv(:,1);
-
-    % Inner loop is for variables that just change the tissue curve
-    % (ktrans, Vp, Ve), this loop generates tissue curves, and caches
-    % them for future noise repeats
-    for i=1:length_inner_variables
-        if ~progessbar_outer
-            pp.progress;
-        end
-        
-        [k_index, vp_index, ve_index, Fp_index] = ind2sub(inner_variable_sizes,i);
-        ktrans  = ktrans_list(k_index);
-        vp      = vp_list(vp_index);
-        ve      = ve_list(ve_index);
-        Fp      = fp_list(Fp_index);
-        PS      = ktrans*Fp/(Fp-ktrans);
-        
-        % The curve generation and down sampling is time consuming, only
-        % caclulate on first noise repeat and cache values
-        if repeats_index==1
-            % Calculate Tissue SI
-            if strcmp(gen_dce_model, 'ex_tofts')
-                integral_function = @(u,t) aif_function(u).*exp(-ktrans*(t-u)/ve);
-                tissue_function = @(t) ktrans*integral(@(u)integral_function(u,t),0,t)+vp*aif_function(t);
-            elseif strcmp(gen_dce_model, 'patlak')
-                tissue_function = @(t) ktrans*integral(@(u)aif_function(u),0,t)+vp*aif_function(t);
-            elseif strcmp(gen_dce_model, '2cxm')
-                E = PS/(PS+Fp);
-                e = ve/(vp+ve);
-                tau_plus  = (E-E*e+e)/(2*E)*(1+sqrt(1-(4*E*e*(1-E)*(1-e))/(E-E*e+e)^2));
-                tau_minus = (E-E*e+e)/(2*E)*(1-sqrt(1-(4*E*e*(1-E)*(1-e))/(E-E*e+e)^2));
-                k_plus  = Fp/((vp+ve)*tau_minus);
-                k_minus = Fp/((vp+ve)*tau_plus);
-                F_plus  =  1*Fp*(tau_plus-1)/(tau_plus-tau_minus);
-                F_minus = -1*Fp*(tau_minus-1)/(tau_plus-tau_minus);
-                integral_function = @(u,t) aif_function(u).*(F_plus*exp(-k_plus*(t-u)) + F_minus*exp(-k_minus*(t-u)));
-                tissue_function = @(t) integral(@(u)integral_function(u,t),0,t);
-            elseif strcmp(gen_dce_model, '2cxm_binding')
-                Cinit = [0,0];              % initial value for Plasma and EEV
-
-                % write down the expressions for the fluxes
-                % C(1) = concentration plasma   C(2) = concentration EEV
-                Pi = @(t,C) PS.*C(2) + Fp.*aif_function(t); % influx to Plasma
-                Pe = @(t,C,ub) PS*ub.*C(1) + Fp.*C(1);      % efflux from Plasma
-                Ei = @(t,C,ub) PS*ub.*C(1);                 % influx to EEV
-                Ee = @(t,C) PS.*C(2);                       % efflux from EEV
-
-                % solve the differential equation 
-                ode_solution = ode45(@TwoCompModelBinding,[0 ta],Cinit,[],Pi,Pe,Ei,Ee,vp,ve,Ka);
-
-                % Get tissue curve
-                c_compartments = deval(ode_solution,time_array);
-                c_tissue = vp.*c_compartments(1,:)+ve.*c_compartments(2,:);
-            else
-                error([gen_dce_model ' is not a valid generation model']);
-            end
-
-            if strcmp(gen_dce_model, '2cxm_binding')
-                tissue_gd_matrix(:,i) = c_tissue;
-            else
-    %             tissue_gd_matrix(:,i) = arrayfun(tissue_function,time_array);
-                tissue_gd_matrix(:,i) = down_sample(tissue_function,time_array,1/60);
-            end      
-        end
-        
-        tissue_t1_post = 1./(1/tissue_t1_pre+tissue_gd_matrix(:,i).*(relaxivity*1/1000));
-        tissue_si_pre = sind(fa)*alpha*(1-exp(-tr/tissue_t1_pre))/(1-cosd(fa)*exp(-tr/tissue_t1_pre));
-        tissue_si_post = sind(fa).*alpha.*(1-exp(-tr./tissue_t1_post))./(1-cosd(fa).*exp(-tr./tissue_t1_post));
-        
-        % Add noise to SI, noise is in the real and img channels when
-        % aquired, the mag is then taken for the image, this gives the noise
-        % (which is white Gaussian for the acquisition) a Rician distribution
-        noise_real_post = normrnd(0,mean(tissue_si_pre)/snr_level,size(tissue_si_post));
-        noise_img_post = normrnd(0,mean(tissue_si_pre)/snr_level,size(tissue_si_post));
-        tissue_si_post_noisy = sqrt( (tissue_si_post+noise_real_post).^2 + noise_img_post.^2);
-        noise_real = normrnd(0,mean(tissue_si_pre)/(snr_level*sqrt(baseline_images)),size(tissue_si_pre));
-        noise_img = normrnd(0,mean(tissue_si_pre)/(snr_level*sqrt(baseline_images)),size(tissue_si_pre));
-        tissue_si_pre_noisy = sqrt( (tissue_si_pre+noise_real).^2 + noise_img.^2);
-
-        % Now get the noisy tissue concentration
-        sstar = (1-exp(-tr/tissue_t1_pre))/(1-cosd(fa)*exp(-tr/tissue_t1_pre));
-        si_ratio_noisy = tissue_si_post_noisy./tissue_si_pre_noisy;
-        tissue_r1_post_noisy = -1./tr*log((sstar.*si_ratio_noisy-1)./(si_ratio_noisy.*sstar.*cosd(fa)-1));
-        tissue_gd_matrix_noisy(:,i) = (tissue_r1_post_noisy-1/tissue_t1_pre)/(relaxivity/1000);
-%         figure(1)
-%         plot(tissue_gd_matrix(:,i));
-%         tissue_si_matrix(:,i) = tissue_si_post;
-%         tissue_si_noisy_matrix(:,i) = tissue_si_post_noisy;
-%         noise_stdv_tissue(:,i) = ((tissue_si_post-tissue_si_post_noisy).^2)+noise_stdv_tissue(:,i);
-    end
-
-    
-    % A single call returns all the inner variable fits
-    roi_data{1}.Cp = aif_gd_noisy;
-    roi_data{1}.timer = time_array';
-    roi_data{1}.Ct = tissue_gd_matrix_noisy;
-
-%     roi_data{1}.Cp = decimate(aif_gd_noisy,4);
-%     roi_data{1}.timer = decimate(time_array',4);
-%     Ct_downsampled = zeros(size(roi_data{1}.Cp,2),size(tissue_gd_matrix_noisy,2));
-%     for ii=1:size(tissue_gd_matrix_noisy,2)
-%         Ct_downsampled(:,ii) = decimate(tissue_gd_matrix_noisy(:,ii),4);
-%     end
-%     roi_data{1}.Ct = Ct_downsampled;
-
-%     n = 6;
-%     si_avg = reshape(mean(reshape(tissue_si_matrix,[n prod(size(tissue_si_matrix))/n])), [size(tissue_si_matrix,1)/n size(tissue_si_matrix,2)]);
-%     si_noise_avg = reshape(mean(reshape(tissue_si_noisy_matrix,[n prod(size(tissue_si_noisy_matrix))/n])), [size(tissue_si_noisy_matrix,1)/n size(tissue_si_noisy_matrix,2)]);
-%     noise_stdv_tissue_avg = ((si_avg-si_noise_avg).^2)+noise_stdv_tissue_avg;
-%     
-%     roi_data{1}.Cp = reshape(mean(reshape(aif_gd_noisy',[n prod(size(aif_gd_noisy'))/n])), [size(aif_gd_noisy',1)/n size(aif_gd_noisy',2)])';
-%     roi_data{1}.timer = reshape(mean(reshape(time_array',[n prod(size(time_array'))/n])), [size(time_array',1)/n size(time_array',2)]);
-%     roi_data{1}.Ct = reshape(mean(reshape(tissue_gd_matrix_noisy,[n prod(size(tissue_gd_matrix_noisy))/n])), [size(tissue_gd_matrix_noisy,1)/n size(tissue_gd_matrix_noisy,2)]);
-    [roi_results, roi_residuals] = FXLfit_generic(roi_data, length_inner_variables, fit_dce_model, 0);
-    
-    % Save results
-    if strcmp(fit_dce_model, 'tofts')
-        exponential_ktrans(:,j) = roi_results(:,1);
-        exponential_ve(:,j) = roi_results(:,2);
-        exponential_residual(:,j) = roi_results(:,3);
-        exponential_ktrans_95ci(:,j,:) = [roi_results(:,4) roi_results(:,5)];
-        exponential_ve_95ci(:,j,:) = [roi_results(:,6) roi_results(:,7)];
-        paramname = {'Ktrans'; 've'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 've_ci_low';'ve_ci_high'};
-    elseif strcmp(fit_dce_model, '2cxm')
-        exponential_ktrans(:,j) = roi_results(:,1);
-        exponential_ve(:,j) = roi_results(:,2);
-        exponential_vp(:,j) = roi_results(:,3);
-        exponential_fp(:,j) = roi_results(:,4);
-        exponential_residual(:,j) = roi_results(:,5);
-        exponential_ktrans_95ci(:,j,:) = [roi_results(:,6) roi_results(:,7)];
-        exponential_ve_95ci(:,j,:) = [roi_results(:,8) roi_results(:,9)];
-        exponential_vp_95ci(:,j,:) = [roi_results(:,10) roi_results(:,11)];
-        exponential_fp_95ci(:,j,:) = [roi_results(:,12) roi_results(:,13)];
-        paramname = {'Ktrans'; 've'; 'vp';'fp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 've_ci_low';'ve_ci_high'; 'vp_ci_low'; 'vp_ci_high'; 'fp_ci_low'; 'fp_ci_high'};
-    elseif strcmp(fit_dce_model, 'ex_tofts') || strcmp(fit_dce_model, 'nested')
-        exponential_ktrans(:,j) = roi_results(:,1);
-        exponential_ve(:,j) = roi_results(:,2);
-        exponential_vp(:,j) = roi_results(:,3);
-        exponential_residual(:,j) = roi_results(:,4);
-        exponential_ktrans_95ci(:,j,:) = [roi_results(:,5) roi_results(:,6)];
-        exponential_ve_95ci(:,j,:) = [roi_results(:,7) roi_results(:,8)];
-        exponential_vp_95ci(:,j,:) = [roi_results(:,9) roi_results(:,10)];
-        paramname = {'Ktrans'; 've'; 'vp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 've_ci_low';'ve_ci_high'; 'vp_ci_low'; 'vp_ci_high'};
-    elseif strcmp(fit_dce_model, 'tissue_uptake')
-        exponential_ktrans(:,j) = roi_results(:,1);
-        exponential_ve(:,j) = roi_results(:,2); %actually fp
-        exponential_vp(:,j) = roi_results(:,3);
-        exponential_residual(:,j) = roi_results(:,4);
-        exponential_ktrans_95ci(:,j,:) = [roi_results(:,5) roi_results(:,6)];
-        exponential_ve_95ci(:,j,:) = [roi_results(:,7) roi_results(:,8)];
-        exponential_vp_95ci(:,j,:) = [roi_results(:,9) roi_results(:,10)];
-        paramname = {'Ktrans'; 'fp'; 'vp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 'fp_ci_low';'fp_ci_high'; 'vp_ci_low'; 'vp_ci_high'};
-    elseif strcmp(fit_dce_model, 'patlak') || strcmp(fit_dce_model, 'patlak_linear')
-        exponential_ktrans(:,j) = roi_results(:,1);
-        exponential_vp(:,j) = roi_results(:,2);
-        exponential_residual(:,j) = roi_results(:,3);
-        exponential_ktrans_95ci(:,j,:) = [roi_results(:,4) roi_results(:,5)];
-        exponential_vp_95ci(:,j,:) = [roi_results(:,6) roi_results(:,7)];
-        paramname = {'Ktrans'; 'vp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 'vp_ci_low'; 'vp_ci_high'};
-    elseif strcmp(fit_dce_model, 'fxr')
-        exponential_ktrans(:,j) = roi_results(:,1);
-        exponential_ve(:,j) = roi_results(:,2);
-        exponential_vp(:,j) = roi_results(:,3);
-        exponential_residual(:,j) = roi_results(:,4);
-        exponential_ktrans_95ci(:,j,:) = [roi_results(:,5) roi_results(:,6)];
-        exponential_ve_95ci(:,j,:) = [roi_results(:,7) roi_results(:,8)];
-        exponential_vp_95ci(:,j,:) = [roi_results(:,9) roi_results(:,10)];
-        paramname = {'Ktrans'; 've'; 'tau'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 've_ci_low';'ve_ci_high'; 'tau_ci_low'; 'tau_ci_high'};
-    elseif strcmp(fit_dce_model, 'auc')
-        if quant
-            paramname = {'AUCc'; 'AUCs'; 'NAUCc'; 'NAUCs'};
-        else
-            paramname = {'AUCs'; 'NAUCs'};
-        end
-    else
-        % Error
-        error('Model not supported');
-    end
-    
-    if double_fit
-        [roi_results, roi_residuals] = FXLfit_generic(roi_data, length_inner_variables, fit_dce_model, 0, 1);
-     
-        if strcmp(fit_dce_model, 'tofts')
-            d_exponential_ktrans(:,j) = roi_results(:,1);
-            d_exponential_ve(:,j) = roi_results(:,2);
-            d_exponential_residual(:,j) = roi_results(:,3);
-            d_exponential_ktrans_95ci(:,j,:) = [roi_results(:,4) roi_results(:,5)];
-            d_exponential_ve_95ci(:,j,:) = [roi_results(:,6) roi_results(:,7)];
-            d_paramname = {'Ktrans'; 've'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 've_ci_low';'ve_ci_high'};
-        elseif strcmp(fit_dce_model, '2cxm')
-            d_exponential_ktrans(:,j) = roi_results(:,1);
-            d_exponential_ve(:,j) = roi_results(:,2);
-            d_exponential_vp(:,j) = roi_results(:,3);
-            d_exponential_fp(:,j) = roi_results(:,4);
-            d_exponential_residual(:,j) = roi_results(:,5);
-            d_exponential_ktrans_95ci(:,j,:) = [roi_results(:,6) roi_results(:,7)];
-            d_exponential_ve_95ci(:,j,:) = [roi_results(:,8) roi_results(:,9)];
-            d_exponential_vp_95ci(:,j,:) = [roi_results(:,10) roi_results(:,11)];
-            d_exponential_fp_95ci(:,j,:) = [roi_results(:,12) roi_results(:,13)];
-            d_paramname = {'Ktrans'; 've'; 'vp';'fp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 've_ci_low';'ve_ci_high'; 'vp_ci_low'; 'vp_ci_high'; 'fp_ci_low'; 'fp_ci_high'};
-        elseif strcmp(fit_dce_model, 'ex_tofts') || strcmp(fit_dce_model, 'nested')
-            d_exponential_ktrans(:,j) = roi_results(:,1);
-            d_exponential_ve(:,j) = roi_results(:,2);
-            d_exponential_vp(:,j) = roi_results(:,3);
-            d_exponential_residual(:,j) = roi_results(:,4);
-            d_exponential_ktrans_95ci(:,j,:) = [roi_results(:,5) roi_results(:,6)];
-            d_exponential_ve_95ci(:,j,:) = [roi_results(:,7) roi_results(:,8)];
-            d_exponential_vp_95ci(:,j,:) = [roi_results(:,9) roi_results(:,10)];
-            d_paramname = {'Ktrans'; 've'; 'vp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 've_ci_low';'ve_ci_high'; 'vp_ci_low'; 'vp_ci_high'};
-        elseif strcmp(fit_dce_model, 'tissue_uptake')
-            d_exponential_ktrans(:,j) = roi_results(:,1);
-            d_exponential_ve(:,j) = roi_results(:,2); %actually fp
-            d_exponential_vp(:,j) = roi_results(:,3);
-            d_exponential_residual(:,j) = roi_results(:,4);
-            d_exponential_ktrans_95ci(:,j,:) = [roi_results(:,5) roi_results(:,6)];
-            d_exponential_ve_95ci(:,j,:) = [roi_results(:,7) roi_results(:,8)];
-            d_exponential_vp_95ci(:,j,:) = [roi_results(:,9) roi_results(:,10)];
-            d_paramname = {'Ktrans'; 'fp'; 'vp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 'fp_ci_low';'fp_ci_high'; 'vp_ci_low'; 'vp_ci_high'};
-        elseif strcmp(fit_dce_model, 'patlak') || strcmp(fit_dce_model, 'patlak_linear')
-            d_exponential_ktrans(:,j) = roi_results(:,1);
-            d_exponential_vp(:,j) = roi_results(:,2);
-            d_exponential_residual(:,j) = roi_results(:,3);
-            d_exponential_ktrans_95ci(:,j,:) = [roi_results(:,4) roi_results(:,5)];
-            d_exponential_vp_95ci(:,j,:) = [roi_results(:,6) roi_results(:,7)];
-            d_paramname = {'Ktrans'; 'vp'; 'residual'; 'ktrans_ci_low'; 'ktrans_ci_high'; 'vp_ci_low'; 'vp_ci_high'};
-        end
-    end
-
-
-end
-pp.stop;
-
-%% Post Processing
-% Verify Noise
-% noise_stdv_sqrt = sqrt(noise_stdv/(length_outer_variables));
-% noise_stdv_sqrt_tissue = sqrt(noise_stdv_tissue/(length_outer_variables));
-% noise_stdv_sqrt_tissue_avg = sqrt(noise_stdv_tissue_avg/(length_outer_variables));
-% actual_SNR_aif = aif_si_post./noise_stdv_sqrt';
-% actual_SNR_tissue = tissue_si_matrix./noise_stdv_sqrt_tissue;
-% actual_SNR_tissue_avg = si_avg./noise_stdv_sqrt_tissue_avg;
-
-
-% actual_SNR = tissue_si_matrix./noise_stdv_sqrt;
-
-% Check if data falls in 95% CI
-% exponential_ktrans_95 = zeros(length_inner_variables,1);
-% exponential_vp_95 = zeros(length_inner_variables,1);
-% exponential_ve_95 = zeros(length_inner_variables,1);
-% 
-% for i=1:length_inner_variables
-% 	for j=1:length_outer_variables	
-% 		if exponential_ktrans(i,j)>min(exponential_ktrans_95ci(i,j,:)) && ...
-% 				exponential_ktrans(i,j)<max(exponential_ktrans_95ci(i,j,:))
-% 			exponential_ktrans_95(i) = exponential_ktrans_95(i)+1;
-% 		end
-% 		if exponential_vp(i,j)>min(exponential_vp_95ci(i,j,:)) && ...
-% 				exponential_vp(i,j)<max(exponential_vp_95ci(i,j,:))
-% 			exponential_vp_95(i) = exponential_vp_95(i)+1;
-% 		end
-% 		if exponential_ve(i,j)>min(exponential_ve_95ci(i,j,:)) && ...
-% 				exponential_ve(i,j)<max(exponential_ve_95ci(i,j,:))
-% 			exponential_ve_95(i) = exponential_ve_95(i)+1;
-% 		end
-% 	end
-% end
-% 
-% exponential_ktrans_95 = exponential_ktrans_95./(length_outer_variables);
-% exponential_vp_95 = exponential_vp_95./(length_outer_variables);
-% exponential_ve_95 = exponential_ve_95./(length_outer_variables);
-
-% Reshape
-exponential_ktrans_shape = reshape(exponential_ktrans, [inner_variable_sizes outer_variable_sizes]);
-exponential_vp_shape = reshape(exponential_vp, [inner_variable_sizes outer_variable_sizes]);
-exponential_ve_shape = reshape(exponential_ve, [inner_variable_sizes outer_variable_sizes]);
-exponential_fp_shape = reshape(exponential_fp, [inner_variable_sizes outer_variable_sizes]);
-
-% Get the standard deviation error from the repeats
-% exponential_ktrans_error_std = permute(exponential_ktrans_shape,[1 2 3 4 6 7 5 8]);
-% exponential_ktrans_error_std = reshape(exponential_ktrans_error_std,[12 1 1 1 4 1 8*125]);
-% exponential_ktrans_error_std = nanstd(exponential_ktrans_error_std,0,7);
-exponential_ktrans_error_std = nanstd(exponential_ktrans_shape,0,repeats_location);
-exponential_vp_error_std = nanstd(exponential_vp_shape,0,repeats_location);
-exponential_ve_error_std = nanstd(exponential_ve_shape,0,repeats_location);
-exponential_fp_error_std = nanstd(exponential_fp_shape,0,repeats_location);
-
-% Average the repeats for bias error
-exponential_ktrans_average = nanmean(exponential_ktrans_shape,repeats_location);
-exponential_vp_average = nanmean(exponential_vp_shape,repeats_location);
-exponential_ve_average = nanmean(exponential_ve_shape,repeats_location);
-exponential_fp_average = nanmean(exponential_fp_shape,repeats_location);
-
-exponential_ktrans_median = nanmedian(exponential_ktrans_shape,repeats_location);
-exponential_vp_median = nanmedian(exponential_vp_shape,repeats_location);
-exponential_ve_median = nanmedian(exponential_ve_shape,repeats_location);
-exponential_fp_median = nanmedian(exponential_fp_shape,repeats_location);
-
-% Remove signleton repeats dimension
-sz = size(exponential_ktrans_median);
-if size(sz)>repeats_location
-    sz(repeats_location) = [];
-end
-exponential_ktrans_error_std = reshape(exponential_ktrans_error_std,sz);
-exponential_vp_error_std = reshape(exponential_vp_error_std,sz);
-exponential_ve_error_std = reshape(exponential_ve_error_std,sz);
-exponential_fp_error_std = reshape(exponential_fp_error_std,sz);
-
-exponential_ktrans_average = reshape(exponential_ktrans_average,sz);
-exponential_vp_average = reshape(exponential_vp_average,sz);
-exponential_ve_average = reshape(exponential_ve_average,sz);
-exponential_fp_average = reshape(exponential_fp_average,sz);
-
-exponential_ktrans_median = reshape(exponential_ktrans_median,sz);
-exponential_vp_median = reshape(exponential_vp_median,sz);
-exponential_ve_median = reshape(exponential_ve_median,sz);
-exponential_fp_median = reshape(exponential_fp_median,sz);
-% Reduce by one since the repeats dimension was removed
-toffset_location = toffset_location-1;
-
-% Subtract out the true values to get the bias error
-exponential_ktrans_error = bsxfun(@minus,exponential_ktrans_average,ktrans_list');
-% These don't work as the vp_list does not line up with the vp dimension
-% exponential_vp_error = bsxfun(@minus,exponential_vp_average,vp_list');
-% exponential_ve_error = bsxfun(@minus,exponential_ve_average,ve_list');
-% exponential_fp_error = bsxfun(@minus,exponential_fp_average,fp_list');
-
-exponential_ktrans_error_percent = bsxfun(@rdivide,exponential_ktrans_error,ktrans_list');
-% These don't work as the vp_list does not line up with the vp dimension
-% exponential_vp_error_percent = bsxfun(@rdivide,exponential_vp_error,vp_list');
-% exponential_ve_error_percent = bsxfun(@rdivide,exponential_ve_error,ve_list');
-% exponential_fp_error_percent = bsxfun(@rdivide,exponential_fp_error,fp_list');
-
-if average_across_offset
-    exponential_ktrans_error_std = nanmean(exponential_ktrans_error_std, toffset_location);
-    exponential_vp_error_std = nanmean(exponential_vp_error_std, toffset_location);
-    exponential_ve_error_std = nanmean(exponential_ve_error_std, toffset_location);
-    exponential_fp_error_std = nanmean(exponential_fp_error_std, toffset_location);
-
-    exponential_ktrans_average = nanmean(exponential_ktrans_average, toffset_location);
-    exponential_vp_average = nanmean(exponential_vp_average, toffset_location);
-    exponential_ve_average = nanmean(exponential_ve_average, toffset_location);
-    exponential_fp_average = nanmean(exponential_fp_average, toffset_location);
-
-    exponential_ktrans_median = nanmean(exponential_ktrans_median, toffset_location);
-    exponential_vp_median = nanmean(exponential_vp_median, toffset_location);
-    exponential_ve_median = nanmean(exponential_ve_median, toffset_location);
-    exponential_fp_median = nanmean(exponential_fp_median, toffset_location);
-    
-    exponential_ktrans_error = nanmean(exponential_ktrans_error, toffset_location);
-    exponential_ktrans_error_percent = nanmean(exponential_ktrans_error_percent, toffset_location);
-end
-
-if double_fit
-    double_fit_gpu_run
-end
-% foo_std = exponential_ktrans_error_std.*1000;
-% foo_mean = exponential_ktrans_average.*1000;
-% exponential_ktrans_average = nanmean(exponential_ktrans_shape,number_variables);
-% foo = squeeze(exponential_ktrans_average).*1000
-% exponential_ktrans_error_std = nanstd(exponential_ktrans_shape,0,number_variables);
-% foo = squeeze(exponential_ktrans_error_std).*1000
-
-if(save_data)
-    disp(' ');
-    sim_save_name = ['simulation_' datestr(now,'yy-mm-dd-HH-MM-SS')];
-    save(sim_save_name);
-    disp(['Results saved to: ' sim_save_name]);
-end
-disp(' ');
-disp('Finished');
-disp(datestr(now))
-toc
-
-%% Make Plots
-number_colors = 2*max([length(vp_list) length(ve_list) length(fp_list) length(ta_list) length(time_resolution_list) length(snr_list)]);
-if number_colors<=4
-    number_colors = 6;
-end
-set(0,'DefaultAxesColorOrder',hot(number_colors));    
-ve_plot = 1;
-ta_plot = 1;
-snr_plot = 1;
-vp_plot = 1;
-fp_plot = 1;
-ktrans_plot = 1;
-tres_plot = 1;
-
-if show_plots
-    % Variable Order:
-    % (Ktrans, Vp, Ve, Fp, TA, Time Res, SNR, noise repeats)
-    % *********
-  
-    figure(1);
-    p = plot(ktrans_list.*1000,squeeze(exponential_ktrans_error_percent(:,vp_plot,ve_plot,fp_plot,ta_plot,tres_plot,:)).*100);
-    title([fit_dce_model ' GPU Accuracy Error'],'Interpreter','none');
-    xlabel('Ktrans (10^{-3} * min^{-1})');
-    ylabel('Accuracy Error (%)')
-    legend(cellfun(@num2str,num2cell(snr_list),'UniformOutput', 0));
-    %ylim([0 50])
-%     savefig('fig1-AccuracyPercent');
-%     saveas(gcf,'fig1-AccuracyPercent', 'png')
-%  
-
-    figure(2);
-    p = plot(ktrans_list.*1000,squeeze(d_exponential_ktrans_error_percent(:,vp_plot,ve_plot,fp_plot,ta_plot,tres_plot,:)).*100);
-    title([fit_dce_model ' CPU Accuracy Error'],'Interpreter','none');
-    xlabel('Ktrans (10^{-3} * min^{-1})');
-    ylabel('Accuracy Error (%)')
-    legend(cellfun(@num2str,num2cell(snr_list),'UniformOutput', 0));
-    %ylim([0 50])
-%     savefig('fig1-AccuracyPercent');
-%     saveas(gcf,'fig1-AccuracyPercent', 'png')
-%  
-
-    figure(3);
-    p = scatter(exponential_ktrans(:),d_exponential_ktrans(:));
-    title([fit_dce_model ' GPU vs. CPU fit Ktrans'],'Interpreter','none');
-    xlabel('GPU Ktrans (10^{-3} * min^{-1})');
-    ylabel('CPU Ktrans (10^{-3} * min^{-1})');
-    %set(gca,'xscale','log');
-    %set(gca,'yscale','log')
-    ylim([0 0.2])
-    xlim([0 0.2])
-    
-    figure(5);
-    p = scatter(exponential_ve(:),d_exponential_ve(:));
-    title([fit_dce_model ' GPU vs. CPU fit Ve'],'Interpreter','none');
-    xlabel('GPU Ve');
-    ylabel('CPU Ve');
-    ylim([0 1])
-    xlim([0 1])
-    
-    figure(6);
-    p = scatter(exponential_vp(:),d_exponential_vp(:));
-    title([fit_dce_model ' GPU vs. CPU fit Vp'],'Interpreter','none');
-    xlabel('GPU Vp');
-    ylabel('CPU Vp');
-    ylim([0 0.1])
-    xlim([0 0.1])
-    
-
-    figure(4);
-    hold on
-    p = errorbar(ktrans_list.*1000,squeeze(exponential_ktrans_median(:,vp_plot,ve_plot,fp_plot,ta_plot,tres_plot,1).*1000),squeeze(exponential_ktrans_error_std(:,vp_plot,ve_plot,fp_plot,ta_plot,tres_plot,1).*1000));
-    p = errorbar(ktrans_list.*1000,squeeze(exponential_ktrans_median(:,vp_plot,ve_plot,fp_plot,ta_plot,tres_plot,2).*1000),squeeze(exponential_ktrans_error_std(:,vp_plot,ve_plot,fp_plot,ta_plot,tres_plot,2).*1000));
-    hold off
-    title([fit_dce_model ' fit vs. ' gen_dce_model ' Ktrans, Various SNR'],'Interpreter','none');
-    xlabel('Ktrans (10^{-3} * min^{-1})');
-    ylabel('Average Fit Ktrans (10^{-3} * min^{-1})')
-    ylim([0 130])
-    xlim([0 130])
-    legend(cellfun(@num2str,num2cell(snr_list),'UniformOutput', 0), 'Location', 'NorthWest');
-    diagonal_line = refline(1,0);
-    set(diagonal_line,'Color',[0.5 0.5 0.5],'LineStyle',':');
-    
-
-%     figure(5)
-%     plot(time_array,aif_gd_noisy,'k');
-
-    
-end
-
-
diff --git a/chopping_block/double_fit_gpu_run.m b/chopping_block/double_fit_gpu_run.m
deleted file mode 100644
index 616d1cf..0000000
--- a/chopping_block/double_fit_gpu_run.m
+++ /dev/null
@@ -1,109 +0,0 @@
-toffset_location = toffset_location+1;
-% Reshape
-d_exponential_ktrans_shape = reshape(d_exponential_ktrans, [inner_variable_sizes outer_variable_sizes]);
-d_exponential_vp_shape = reshape(d_exponential_vp, [inner_variable_sizes outer_variable_sizes]);
-d_exponential_ve_shape = reshape(d_exponential_ve, [inner_variable_sizes outer_variable_sizes]);
-d_exponential_fp_shape = reshape(d_exponential_fp, [inner_variable_sizes outer_variable_sizes]);
-d_exponential_residual_shape = reshape(d_exponential_residual, [inner_variable_sizes outer_variable_sizes]);
-exponential_residual_shape = reshape(exponential_residual, [inner_variable_sizes outer_variable_sizes]);
-exponential_ktrans_shape = reshape(exponential_ktrans, [inner_variable_sizes outer_variable_sizes]);
-
-% % Throw out bad fits
-% voxels_before = numel(d_exponential_ktrans_shape);
-% residual_a_limit = 0.0113;
-% d_exponential_ktrans_shape(d_exponential_residual_shape>residual_a_limit) = NaN;
-% d_exponential_ve_shape(d_exponential_residual_shape>residual_a_limit) = NaN;
-% d_exponential_vp_shape(d_exponential_residual_shape>residual_a_limit) = NaN;
-% d_exponential_fp_shape(d_exponential_residual_shape>residual_a_limit) = NaN;
-% exponential_ktrans_shape(d_exponential_residual_shape>residual_a_limit) = NaN;
-% exponential_residual_shape(d_exponential_residual_shape>residual_a_limit) = NaN;
-% d_exponential_residual_shape(d_exponential_residual_shape>residual_a_limit) = NaN;
-% voxels_after_a = numel(d_exponential_ktrans_shape(~isnan(d_exponential_ktrans_shape)));
-% 
-% % residual_b_limit = 0.026;
-% residual_b_limit = 0.0119;
-% d_exponential_ktrans_shape(exponential_residual_shape>residual_b_limit) = NaN;
-% d_exponential_ve_shape(exponential_residual_shape>residual_b_limit) = NaN;
-% d_exponential_vp_shape(exponential_residual_shape>residual_b_limit) = NaN;
-% d_exponential_fp_shape(exponential_residual_shape>residual_b_limit) = NaN;
-% exponential_ktrans_shape(exponential_residual_shape>residual_b_limit) = NaN;
-% d_exponential_residual_shape(exponential_residual_shape>residual_b_limit) = NaN;
-% exponential_residual_shape(exponential_residual_shape>residual_b_limit) = NaN;
-% voxels_after_b = numel(d_exponential_ktrans_shape(~isnan(d_exponential_ktrans_shape)));
-% 
-% percent_left_a_res_filter = voxels_after_a/voxels_before*100
-% percent_left_b_res_filter = voxels_after_b/voxels_before*100
-
-
-
-% Get the standard deviation error from the repeats
-exponential_ktrans_error_std = nanstd(exponential_ktrans_shape,0,repeats_location);
-d_exponential_ktrans_error_std = nanstd(d_exponential_ktrans_shape,0,repeats_location);
-d_exponential_vp_error_std = nanstd(d_exponential_vp_shape,0,repeats_location);
-d_exponential_ve_error_std = nanstd(d_exponential_ve_shape,0,repeats_location);
-d_exponential_fp_error_std = nanstd(d_exponential_fp_shape,0,repeats_location);
-
-% Average the repeats for bias error
-exponential_ktrans_average = nanmean(exponential_ktrans_shape,repeats_location);
-d_exponential_ktrans_average = nanmean(d_exponential_ktrans_shape,repeats_location);
-d_exponential_vp_average = nanmean(d_exponential_vp_shape,repeats_location);
-d_exponential_ve_average = nanmean(d_exponential_ve_shape,repeats_location);
-d_exponential_fp_average = nanmean(d_exponential_fp_shape,repeats_location);
-
-exponential_ktrans_median = nanmedian(exponential_ktrans_shape,repeats_location);
-d_exponential_ktrans_median = nanmedian(d_exponential_ktrans_shape,repeats_location);
-d_exponential_vp_median = nanmedian(d_exponential_vp_shape,repeats_location);
-d_exponential_ve_median = nanmedian(d_exponential_ve_shape,repeats_location);
-d_exponential_fp_median = nanmedian(d_exponential_fp_shape,repeats_location);
-
-% Remove signleton repeats dimension
-sz = size(d_exponential_ktrans_median);
-if size(sz)>repeats_location
-    sz(repeats_location) = [];
-end
-exponential_ktrans_error_std = reshape(exponential_ktrans_error_std,sz);
-d_exponential_ktrans_error_std = reshape(d_exponential_ktrans_error_std,sz);
-d_exponential_vp_error_std = reshape(d_exponential_vp_error_std,sz);
-d_exponential_ve_error_std = reshape(d_exponential_ve_error_std,sz);
-d_exponential_fp_error_std = reshape(d_exponential_fp_error_std,sz);
-
-exponential_ktrans_average = reshape(exponential_ktrans_average,sz);
-d_exponential_ktrans_average = reshape(d_exponential_ktrans_average,sz);
-d_exponential_vp_average = reshape(d_exponential_vp_average,sz);
-d_exponential_ve_average = reshape(d_exponential_ve_average,sz);
-d_exponential_fp_average = reshape(d_exponential_fp_average,sz);
-
-exponential_ktrans_median = reshape(exponential_ktrans_median,sz);
-d_exponential_ktrans_median = reshape(d_exponential_ktrans_median,sz);
-d_exponential_vp_median = reshape(d_exponential_vp_median,sz);
-d_exponential_ve_median = reshape(d_exponential_ve_median,sz);
-d_exponential_fp_median = reshape(d_exponential_fp_median,sz);
-% Reduce by one since the repeats dimension was removed
-toffset_location = toffset_location-1;
-
-% Subtract out the true values to get the bias error
-d_exponential_ktrans_error = bsxfun(@minus,d_exponential_ktrans_average,ktrans_list');
-d_exponential_ktrans_error_percent = bsxfun(@rdivide,d_exponential_ktrans_error,ktrans_list');
-
-if average_across_offset
-    exponential_ktrans_error_std = nanmean(exponential_ktrans_error_std, toffset_location);
-    d_exponential_ktrans_error_std = nanmean(d_exponential_ktrans_error_std, toffset_location);
-    d_exponential_vp_error_std = nanmean(d_exponential_vp_error_std, toffset_location);
-    d_exponential_ve_error_std = nanmean(d_exponential_ve_error_std, toffset_location);
-    d_exponential_fp_error_std = nanmean(d_exponential_fp_error_std, toffset_location);
-
-    exponential_ktrans_average = nanmean(exponential_ktrans_average, toffset_location);
-    d_exponential_ktrans_average = nanmean(d_exponential_ktrans_average, toffset_location);
-    d_exponential_vp_average = nanmean(d_exponential_vp_average, toffset_location);
-    d_exponential_ve_average = nanmean(d_exponential_ve_average, toffset_location);
-    d_exponential_fp_average = nanmean(d_exponential_fp_average, toffset_location);
-
-    exponential_ktrans_median = nanmean(exponential_ktrans_median, toffset_location);
-    d_exponential_ktrans_median = nanmean(d_exponential_ktrans_median, toffset_location);
-    d_exponential_vp_median = nanmean(d_exponential_vp_median, toffset_location);
-    d_exponential_ve_median = nanmean(d_exponential_ve_median, toffset_location);
-    d_exponential_fp_median = nanmean(d_exponential_fp_median, toffset_location);
-    
-    d_exponential_ktrans_error = nanmean(d_exponential_ktrans_error, toffset_location);
-    d_exponential_ktrans_error_percent = nanmean(d_exponential_ktrans_error_percent, toffset_location);
-end
\ No newline at end of file
diff --git a/chopping_block/get_voxel_drift.py b/chopping_block/get_voxel_drift.py
deleted file mode 100755
index f85788b..0000000
--- a/chopping_block/get_voxel_drift.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python3
-
-import numpy as np
-import nibabel as nib
-import os
-import argparse
-from argparse import Namespace
-
-def run(): 
-    #filename = '/media/network_mriphysics/GRASP/test/400105.nii.gz'
-    #filename = '/media/network_mriphysics/GRASP/test/S3reg_strip_auto_threshold.nii'
-    #output_dir = '/media/network_mriphysics/GRASP/test/'
-    
-    img = nib.load(args.filename)
-    
-    print('Data read with dimensions ',img.shape)
-    
-    data = img.get_fdata()
-    
-    #Prep variables for linear fitting
-    x = np.arange(0,img.shape[3])
-    A = np.vstack([x, np.ones(len(x))]).T
-    
-    y = np.reshape(data,(img.shape[0]*img.shape[1]*img.shape[2],img.shape[3])).T
-    
-    #Do Fit
-    m, c = np.linalg.lstsq(A, y, rcond=None)[0]
-    drift = np.divide(m,c)
-    drift = np.multiply(drift,100*img.shape[3])
-    
-    drift_array = np.reshape(drift,(img.shape[0],img.shape[1],img.shape[2]))
-    
-    
-    #Write drift file
-    img = nib.Nifti1Image(drift_array,np.eye(4))
-    #img.header.set_zooms((size_x,size_y,size_z))
-    #img.header.set_xyzt_units('mm','sec')
-    img.to_filename(args.savename)
-    print('Wrote file to: '+args.savename)
-
-
-def inputs(filename,savename):
-    global args
-    args=Namespace(filename=filename, savename=savename)
-    run()
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Linear fit (over time) of every voxel, outputs percent change')
-    parser.add_argument('-f','--filename', help='dynamic series filename')
-    parser.add_argument('-s','--savename', help='output filename')
-    args = parser.parse_args()
-    run()  
-
-
diff --git a/chopping_block/motion_correct.py b/chopping_block/motion_correct.py
deleted file mode 100755
index c1157ec..0000000
--- a/chopping_block/motion_correct.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import argparse
-from argparse import Namespace
-import sys
-import get_voxel_drift
-import subprocess
-try:
-    import PySimpleGUI as sg
-    gui_installed = True
-except ImportError:
-    gui_installed = False
-
-
-
-def main():
-    output_path = os.path.dirname(os.path.abspath(args.filename))
-    os.chdir(output_path)
-    
-    subname = os.path.splitext(args.filename)[0] #remove .gz
-    subname = os.path.splitext(subname)[0] #remove .nii
-    output_name = subname+'_reg.nii'
-    
-    #output_name = 'S3reg.nii.gz'
-    bet_name = 'S1brain.nii'
-    mask_name = 'S2mask.nii'
-    #delete old results if exist
-    if args.overwrite:
-        print('Delete previous results')
-        try:
-            os.remove(output_name)
-            os.remove(bet_name)
-            os.remove(mask_name)
-        except:   
-            print('') 
-    
-    
-    #Step 1 -- Brain extraction
-    print('Perform brain extration for weighting')
-    #pull out single 3d dataset from 4d series
-    os.system('3dAFNItoNIFTI -prefix single_set.nii.gz '+args.filename+'[0]')
-    #get and save orientation
-    original_orientation = subprocess.check_output("3dinfo -orient single_set.nii.gz", shell=True, universal_newlines=True).rstrip()
-    print('Orientation is: ',original_orientation)
-    #put in standard LPI orientation (required for brain extraction)
-    os.system('3dresample -orient lpi -prefix single_set_std.nii.gz -input single_set.nii.gz')
-    #perform bet
-    os.system('hd-bet -i single_set_std.nii.gz')
-    #return brain mask to original orientation
-    os.system('3dresample -orient '+original_orientation+' -prefix '+mask_name+' -input single_set_std_bet_mask.nii.gz')
-    os.system('3dresample -orient '+original_orientation+' -prefix '+bet_name+' -input single_set_std_bet.nii.gz')
-    #delete intermediate files
-    os.remove('single_set.nii.gz')
-    os.remove('single_set_std.nii.gz')
-    os.remove('single_set_std_bet.nii.gz')
-    os.remove('single_set_std_bet_mask.nii.gz')
-
-    
-    #Step 2 --- Call AFNI registration
-    # two pass = course fitting first, then fine fitting
-    # twodup = output dataset will have its xyz-axes origins reset to those of the base dataset
-    # base 0 = align everything to the first image
-    # nocip = don't rescale image intensities
-    # zpad = pad edges of images then remove, may help with interpolation artifacts or clipping
-    # 1dfile = saves corrections to file used to make plot
-    # heptic = use heptic interpolation, better than Fourier, fourier casus gibbs ringing drift artifacts
-    command_txt = '3dvolreg -weight '+mask_name+'[0] -noclip -zpad 5 -1Dfile dmotion.1d -verbose -rot_thresh 0.002 -x_thresh 0.05 -maxite 50 -heptic -base 0 -prefix '+output_name+' '+args.filename
-    print('Coregistration command: ',command_txt)
-    os.system(command_txt)
-    
-    
-    #Step 3 --- Create voxel drift image
-    if args.makedrift:
-        print('Create voxel drift image')
-        get_voxel_drift.inputs(output_name,'drift_percent_reg.nii.gz')
-        get_voxel_drift.run()
-    
-    #Step 4 --- Plot results of motion correction
-    if args.showplot:
-        os.system('1dplot -volreg -dx 1 -xlabel Acquisition dmotion.1d')
-    
-    print('Wrote motion corrected image to file: '+output_name+'.nii')
-    print('Finished motion correction')
-    
-def inputs(filename,showplot,overwrite,makedrift):
-    global args
-    args=Namespace(filename=filename,showplot=showplot, overwrite=overwrite, makedrift=makedrift)
-    main()
- 
-args=None
-if __name__ == "__main__":
-    if len(sys.argv)==1 and gui_installed:
-        layout = [[sg.Text('Runs motion correction on a DCE dynamic series using AFNI 3dvolreg')],
-                 [sg.Text('_'  * 10)],
-                 [sg.Text('DCE Series', size=(10, 1)), sg.Input(), sg.FileBrowse()],      
-                 [sg.Checkbox('Show 1D Motion Plot',default=True)],
-                 [sg.Checkbox('Overwrite existing files',default=True)],
-                 [sg.Text('Other Options:')],
-                 [sg.Checkbox('Create Voxel Drift Image',default=True)],
-                 [sg.Submit(), sg.Cancel()]]      
-
-        window = sg.Window('motion_correct', layout)
-        
-        
-        event, values = window.Read()
-        window.Close()
-
-        if event=="Cancel":
-            raise SystemExit("Cancelling")
-          
-        print('Running motion_correct with the following inputs:')
-        print("DCE Series: ",values[0])
-        print("Show 1D Motion Plot: ",values[1])
-        print("Overwrite existing files: ",values[2])
-        print("Create Voxel Drift Image: ",values[3])
-        args=Namespace(filename=values[0],showplot=values[1],overwrite=values[2],makedrift=values[3])
-    else:
-        if len(sys.argv)==1 and not gui_installed: 
-            print('install PySimpleGUI and tkinter to use GUI, otherwise use command line options')
-            print('run with "-h" to see command line options')
-        parser = argparse.ArgumentParser(description='Runs motion correction on a DCE dynamic series using AFNI 3dvolreg')
-        parser.add_argument('-o','--overwrite', help='Overwrite exisiting output files', action="store_true")
-        parser.add_argument('-f','--filename', help='DCE series filename')
-        parser.add_argument('-p','--plot', help='Show 1D Motion Plot', action="store_true")
-        parser.add_argument('-d','--drift', help='Create voxel drift image', action="store_true")
-        args = parser.parse_args()
-    main()
-
diff --git a/chopping_block/python_norm.py b/chopping_block/python_norm.py
deleted file mode 100644
index b329889..0000000
--- a/chopping_block/python_norm.py
+++ /dev/null
@@ -1,125 +0,0 @@
-'''
-PYTHON PROGRAM FROR Z-AXIS NORMALIZATION
-PLEASE FOLLOW THE STEPS BELOW SO THAT THE CODE WORKS PROPERLY - 
-IN THE HOME DIRECTORY -
-STEP 1: DOWNLOAD AND INSTALL python 3
-STEP 2: DOWNLOAD AND INSTALL pip
-STEP 3: RUN THE FOLLOWING COMMANDS ON TERMINAL - 
-	pip install numpy
-	pip install nibabel
-	pip install matplotlib
-	pip install scipy
-SETP 4: RUN THE PYTHON CODE (AFTER PLACING IT IN THE CORRECT LOCATION ON THE COMPUTER, GIVEN BELOW IN THE DIAGRAM) WITH THE COMMAND - python3 python_norm.py
-ALSO, IT IS ESSENTIAL THAT THE INPUT DATA AND THIS CODE IS PLACED IN THE PROPER DIRECTORY/FOLDER, SO THAT IT CAN SEARCH AND PROCESS FILES OF ALL SUBJECTS. THE OUTPUT FILES ARE GENERATED AT THE SAME LOCATION OF THE INPUT FILES (FOR EACH SUBJECT).
-THE DIRECTORY STRUCTURE TO BE MAINTAINED - 
-  directory
-	|
-	|
-	|
-	sub directory 1 (containing each subjects' folder and this file)
-								|
-								|
-								|
-								--- subject folder
-								|	|
-								|	|
-								|	|---- folder named "1st_timepoint"
-								|	|			|
-								|	|			|
-								|	|			|
-								|	|			---- file named "2_new.nii.gz"
-								|	|			|
-								|	|			---- file named "5_new.nii.gz"
-								|	|			|
-								|	|			---- file named "10_new.nii.gz"
-								|	|			|
-								|	|			---- file named "12_new.nii.gz"
-								|	|			|
-								|	|			---- file named "15_new.nii.gz"
-								|	|
-								|	|
-								|	|
-								|	|---- folder named "2nd_timepoint"
-								|				|
-								|				|
-								|				|
-								|				---- file named "2_new.nii.gz"
-								|				|
-								|				---- file named "5_new.nii.gz"
-								|				|
-								|				---- file named "10_new.nii.gz"
-								|				|
-								|				---- file named "12_new.nii.gz"
-								|				|
-								|				---- file named "15_new.nii.gz"
-								|	
-								|
-								|
-								|
-								--- this file
-'''
-import os
-import numpy as np
-import matplotlib		
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import nibabel as nib
-from scipy import ndimage
-from PIL import Image
-from statistics import mean, pstdev
-from pathlib import Path
-import re
-
-def normalize(mri_file1, file_dir):			# THE FUNCTION PERFORMING THE NORMALIZATION
-    img1 = nib.load(mri_file1)
-    num = int(re.search(r'\d+', mri_file1.split('/')[-1]).group())
-    img_data1 = img1.get_fdata()
-    data = []
-    for i in range(14):
-    	a = np.where(img_data1[:, :, i] > 0)
-    	data.append(img_data1[:,:,i][a].mean())
-    data_mean = mean(data[1:13])
-    std_dev = pstdev(data[1:13])
-    err = 1*std_dev
-    slice_index = []
-    min_val = data_mean - err
-    max_val = data_mean + err
-    for i in range (14):
-        if not (min_val <= data[i] <= max_val):
-            slice_index.append(i)
-    img_data2 = img_data1
-
-    for i in slice_index:				    
-        norm_val1 = img_data1[:, :, i] * (data_mean/data[i])
-        img_data2[:, :, i] = norm_val1
-
-    data1 = []
-    for i in range(14):
-    	a = np.where(img_data2[:, :, i] > 0)
-    	data1.append(img_data2[:,:,i][a].mean())
-    data_mean1 = mean(data1)
-
-    fig, (ax1, ax2) = plt.subplots(1, 2, sharex = True, sharey=True, figsize=(20,6))
-    ax1.plot(data, 'o-', ms=4)
-    ax1.grid()
-    ax2.plot(data1, 'o-', ms=4)
-    ax2.grid()
-    path2 = file_dir + '/' + str(num) +'_Z.png'			#THE STRING IN THE END CONTAINS THE FILE NAME OF THE GRAPHS GENERATED (I HAVE KEPT IT AS 2_Z.png/5_Z.png/10_Z.png/12_Z.png/15_Z.png AS OF NOW). FOR EACH SUBJECT, THIS FILE GENERATED IS STORED AT THE SAME LOCATION AS THE INPUT FILES
-    plt.savefig(path2)
-
-    final_img = nib.Nifti1Image(img_data2, img1.affine)
-    path3 = file_dir + '/' + str(num) + '_Z.nii'		#THE STRING IN THE END CONTAINS THE FILE NAME OF THE NORMALIZED NIFTI IMAGE GENERATED (I HAVE KEPT IT AS 2_Z.nii/5_Z.nii/10_Z.nii/12_Z.nii/15_Z.nii AS OF NOW). FOR EACH SUBJECT, THIS FILE GENERATED IS STORED AT THE SAME LOCATION AS THE INPUT FILES
-    nib.save(final_img, path3)
-
-
-dir1 = Path(os.getcwd())			# CODE TO AUTOMATICALLY PERFORM THIS NORMALIZATION ON ALL SUBJECTS' ALL VFAs IN THIS DIRECTORY
-files_in_dir1 = dir1.iterdir()
-for file in files_in_dir1:
-    if (os.path.isdir(str(file))):
-        files_in_dir2 = file.iterdir()
-        for item in files_in_dir2:
-            if ((str(item).split('/')[-1] == '1st_timepoint') or (str(item).split('/')[-1] == '2nd_timepoint')):		#SEARCHES FOR THE FOLDERS WITH THESE SPECIFIC NAMES (ENCLOSED IN STRINGS)
-                files_in_dir3 = item.iterdir()
-                for b1_imgs in files_in_dir3:
-                    if ((str(b1_imgs).split('/')[-1] == '2_new.nii.gz') or (str(b1_imgs).split('/')[-1] == '5_new.nii.gz') or (str(b1_imgs).split('/')[-1] == '10_new.nii.gz') or		 (str(b1_imgs).split('/')[-1] == '12_new.nii.gz') or (str(b1_imgs).split('/')[-1] == '15_new.nii.gz')):			#SEARCHES FOR THE NIFTI FILES WITH THESE SPECIFIC NAMES (ENCLOSED IN STRINGS)
-                        normalize(str(b1_imgs), str(item))									#CALLING THE 'normalize()' FUNCTION TO PERFORM THE NORMALIZATION
\ No newline at end of file
diff --git a/chopping_block/python_norm1.py b/chopping_block/python_norm1.py
deleted file mode 100644
index 92ab2d0..0000000
--- a/chopping_block/python_norm1.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import os
-import numpy as np
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import nibabel as nib
-from scipy import ndimage
-from PIL import Image
-from statistics import mean, pstdev
-from pathlib import Path
-import re
-
-def normalize(mri_file1, file_dir):   # THE FUNCTION PERFORMING THE NORMALIZATION
-    dim = {0,1,2}
-    img1 = nib.load(mri_file1)
-    num = int(re.search(r'\d+', mri_file1.split('/')[-1]).group())
-    img_data1 = img1.get_fdata()
-    img_shape = img_data1.shape
-    slice_num = min(img_shape[0], img_shape[1], img_shape[2])
-    slice_loc = img_shape.index(slice_num)
-    img_data1 = np.reshape(img_data1, (img_shape[min(dim-set([slice_loc]))], img_shape[max(dim-set([slice_loc]))], slice_num))
-    data = []
-    for i in range(slice_num):
-        a = np.where(img_data1[:, :, i] > 0)
-        data.append(img_data1[:, :, i][a].mean())
-    data_mean = mean(data[1:13])
-    std_dev = pstdev(data[1:13])
-    err = 1*std_dev
-    slice_index = []
-    min_val = data_mean - err
-    max_val = data_mean + err
-    for i in range (slice_num):
-        if not (min_val <= data[i] <= max_val):
-            slice_index.append(i)
-    img_data2 = img_data1
-
-    for i in slice_index:    
-        norm_val1 = img_data1[:, :, i] * (data_mean/data[i])
-        img_data2[:, :, i] = norm_val1
-
-    data1 = []
-    for i in range(slice_num):
-        a = np.where(img_data2[:, :, i] > 0)
-        data1.append(img_data2[:, :, i][a].mean())
-    data_mean1 = mean(data1)
-
-    fig, (ax1, ax2) = plt.subplots(1, 2, sharex = True, sharey=True, figsize=(20,6))
-    ax1.plot(data, 'o-', ms=4)
-    ax1.grid()
-    ax2.plot(data1, 'o-', ms=4)
-    ax2.grid()
-    path2 = file_dir + '/' + str(num) +'_corr_finalZ.png'   #THE STRING IN THE END CONTAINS THE FILE NAME OF THE GRAPHS GENERATED (I HAVE KEPT IT AS 2_Z.png/5_Z.png/10_Z.png/12_Z.png/15_Z.png AS OF NOW). FOR EACH SUBJECT, THIS FILE GENERATED IS STORED AT THE SAME LOCATION AS THE INPUT FILES
-    plt.savefig(path2)
-    
-    img_data2 = np.reshape(img_data2, img_shape)
-    final_img = nib.Nifti1Image(img_data2, img1.affine)
-    path3 = file_dir + '/' + str(num) + '_corr_finalZ.nii'  #THE STRING IN THE END CONTAINS THE FILE NAME OF THE NORMALIZED NIFTI IMAGE GENERATED (I HAVE KEPT IT AS 2_Z.nii/5_Z.nii/10_Z.nii/12_Z.nii/15_Z.nii AS OF NOW). FOR EACH SUBJECT, THIS FILE GENERATED IS STORED AT THE SAME LOCATION AS THE INPUT FILES
-    nib.save(final_img, path3)
-    
-    
-dir1 = Path(os.getcwd())    # CODE TO AUTOMATICALLY PERFORM THIS NORMALIZATION ON ALL SUBJECTS' ALL VFAs IN THIS DIRECTORY
-files_in_dir1 = dir1.iterdir()
-for file in files_in_dir1:
-    if (os.path.isdir(str(file))):
-        files_in_dir2 = file.iterdir()
-        for item in files_in_dir2:
-            if ((str(item).split('/')[-1] == '1st_timepoint') or (str(item).split('/')[-1] == '2nd_timepoint')):   #SEARCHES FOR THE FOLDERS WITH THESE SPECIFIC NAMES (ENCLOSED IN STRINGS)
-                files_in_dir3 = item.iterdir()
-                for b1_imgs in files_in_dir3:
-                    if str(b1_imgs).endswith('_bfc.nii'):
-                        normalize(str(b1_imgs), str(item))   #CALLING THE 'normalize()' FUNCTION TO PERFORM THE NORMALIZATION
diff --git a/compare_ktrans.py b/compare_ktrans.py
deleted file mode 100644
index 1ac7ea1..0000000
--- a/compare_ktrans.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import nibabel as nib
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-# cpu_file = '/media/network_mriphysics/GRASP/data/500180/DCE_GPU/DCEBBB_flip_500180_patlak_fit_Ktrans_CPU.nii'
-# gpu_file = '/media/network_mriphysics/GRASP/data/500180/DCE_GPU/DCEBBB_flip_500180_patlak_fit_Ktrans.nii'
-cpu_file = '/home/mrispec/Desktop/raw_data/patlak plots/cpu/dce_patlak_fit_Ktrans.nii'
-gpu_file = '/home/mrispec/Desktop/raw_data/patlak plots/gpu/dce_patlak_fit_Ktrans.nii'
-
-print('Processing file: '+cpu_file)
-cpu_img = nib.load(cpu_file)
-cpu_img_data = cpu_img.get_data()
-
-print('Processing file: '+gpu_file)
-gpu_img = nib.load(gpu_file)
-gpu_img_data = gpu_img.get_data()
-
-
-gpu_filter = gpu_img_data[cpu_img_data>0]
-cpu_filter = cpu_img_data[cpu_img_data>0]
-gpu_filter = gpu_filter[cpu_filter<0.1]
-cpu_filter = cpu_filter[cpu_filter<0.1]
-
-cpu_filter = cpu_filter[gpu_filter<0.1]
-gpu_filter = gpu_filter[gpu_filter<0.1]
-
-gpu_small = gpu_filter[::500]
-cpu_small = cpu_filter[::500]
-
-print("Total voxels: ",gpu_img_data.size)
-print("Filtered voxels (median): ",cpu_filter.size)
-print("Plot voxels: ",cpu_small.size)
-
-difference = np.subtract(gpu_filter,cpu_filter)
-diff_abs = np.abs(difference)
-diff_median = np.median(diff_abs)
-diff_mean = np.mean(diff_abs)
-print("Median difference: ",diff_median)
-print("Mean difference: ",diff_mean)
-
-plt.rcParams.update({'font.size': 16})
-plt.figure()
-ax = plt.axes()
-plt.scatter(cpu_small,gpu_small,marker='o',s=10)
-#plt.plot(age_list_short,naa_list_short,'o', xx, yy)
-plt.title('Patlak Fitting')
-plt.xlabel('Ktrans (CPU - Matlab)')
-plt.ylabel('Ktrans (GPU - GPUFit)')
-ax.set_ylim([0, 0.01])
-ax.set_xlim([0, 0.01])
-
-plt.show()
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..e081998
--- /dev/null
+++ b/config.json
@@ -0,0 +1,312 @@
+{
+    "dcm2niixOptions": "-b y -ba n -z y -f '%3s_%f_%p_%t'",
+    "compKeys": [
+        "AcquisitionDateTime",
+        "SeriesNumber",
+        "AcquisitionTime",
+        "SidecarFilename"
+    ],
+    "descriptions": [
+        {
+            "datatype": "anat",
+            "suffix": "T1w",
+            "criteria": {
+                "SeriesDescription": "*MPRAGE*SEL."
+            },
+            "sidecarChanges": {
+                "ProtocolName": "T1w"
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "T1w",
+            "criteria": {
+                "SeriesDescription": "*FSPGR*"
+            },
+            "sidecarChanges": {
+                "ProtocolName": "T1w"
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "T1w",
+            "criteria": {
+                "SeriesDescription": "*mprage*"
+            },
+            "sidecarChanges": {
+                "ProtocolName": "T1w"
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "T1w",
+            "criteria": {
+                "SeriesDescription": "Accelerated Sagittal MPRAGE"
+            },
+            "sidecarChanges": {
+                "ProtocolName": "T1w"
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "FLAIR",
+            "criteria": {
+                "SeriesDescription": "*FLAIR*"
+            }
+        },
+        {
+            "datatype": "dce",
+            "suffix": "DCE",
+            "criteria": {
+                "SeriesDescription": "*DCE*"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "dce",
+            "suffix": "DCE",
+            "criteria": {
+                "SeriesDescription": "*_dyn"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "dce",
+            "suffix": "DCE",
+            "criteria": {
+                "SeriesDescription": "*dynamic_waterext*"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-01",
+            "criteria": {
+                "SeriesDescription": "*FA02*",
+                "FlipAngle": "2"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-01",
+            "criteria": {
+                "SeriesDescription": "*dyn_2*",
+                "FlipAngle": "2"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-01",
+            "criteria": {
+                "SeriesDescription": "*tra_flip_2deg*",
+                "FlipAngle": "2"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-02",
+            "criteria": {
+                "SeriesDescription": "*FA05*",
+                "FlipAngle": "5"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-02",
+            "criteria": {
+                "SeriesDescription": "*dyn_5*",
+                "FlipAngle": "5"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-02",
+            "criteria": {
+                "SeriesDescription": "*tra_flip_5deg*",
+                "FlipAngle": "5"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-03",
+            "criteria": {
+                "SeriesDescription": "*FA10*",
+                "FlipAngle": "10"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-03",
+            "criteria": {
+                "SeriesDescription": "*dyn_10*",
+                "FlipAngle": "10"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-03",
+            "criteria": {
+                "SeriesDescription": "*tra_flip_10deg*",
+                "FlipAngle": "10"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-04",
+            "criteria": {
+                "SeriesDescription": "*FA12*",
+                "FlipAngle": "12"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-04",
+            "criteria": {
+                "SeriesDescription": "*dyn_12*",
+                "FlipAngle": "12"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-03",
+            "criteria": {
+                "SeriesDescription": "*tra_flip_12deg*",
+                "FlipAngle": "12"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-05",
+            "criteria": {
+                "SeriesDescription": "*FA15*",
+                "FlipAngle": "15"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-05",
+            "criteria": {
+                "SeriesDescription": "*dyn_15*",
+                "FlipAngle": "15"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-05",
+            "criteria": {
+                "SeriesDescription": "*tra_flip_15deg*",
+                "FlipAngle": "15"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-06",
+            "criteria": {
+                "SeriesDescription": "*FA20*",
+                "FlipAngle": "20"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-07",
+            "criteria": {
+                "SeriesDescription": "*FA25*",
+                "FlipAngle": "25"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-01",
+            "criteria": {
+                "SeriesDescription": "*FLIP1"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-02",
+            "criteria": {
+                "SeriesDescription": "*FLIP2"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-03",
+            "criteria": {
+                "SeriesDescription": "*FLIP3"
+            },
+            "sidecarChanges": {
+            }
+        },
+        {
+            "datatype": "anat",
+            "suffix": "VFA",
+            "custom_entities": "flip-04",
+            "criteria": {
+                "SeriesDescription": "*FLIP4"
+            },
+            "sidecarChanges": {
+            }
+        }
+    ]
+}
diff --git a/docker/files/freesurfer-exclude.txt b/docker/files/freesurfer-exclude.txt
new file mode 100644
index 0000000..d09d39f
--- /dev/null
+++ b/docker/files/freesurfer-exclude.txt
@@ -0,0 +1,788 @@
+freesurfer/average*
+freesurfer/bin/A*
+freesurfer/bin/c*
+freesurfer/bin/f*
+freesurfer/bin/g*
+freesurfer/bin/mri_a*
+freesurfer/bin/mri_b*
+freesurfer/bin/mri_ca*
+freesurfer/bin/mri_cc
+freesurfer/bin/mri_com*
+freesurfer/bin/mri_conc*
+freesurfer/bin/mri_coreg
+freesurfer/bin/mri_d*
+freesurfer/bin/mri_e*
+freesurfer/bin/mri_f*
+freesurfer/bin/mri_g*
+freesurfer/bin/mri_m*
+freesurfer/bin/mri_n*
+freesurfer/bin/mri_o*
+freesurfer/bin/mri_p*
+freesurfer/bin/mri_r*
+freesurfer/bin/mri_s*
+freesurfer/bin/mri_t*
+freesurfer/bin/mri_vol2s*
+freesurfer/bin/mri_vold*
+freesurfer/bin/mri_w*
+freesurfer/bin/mris*
+freesurfer/bin/p*
+freesurfer/bin/r*
+freesurfer/bin/s*
+freesurfer/bin/ta*
+freesurfer/bin/v*
+freesurfer/bin/3dvolreg.afni
+freesurfer/bin/4dfptoanalyze
+freesurfer/bin/anatomiCutsUtils
+freesurfer/bin/annot2std
+freesurfer/bin/aparc2feat
+freesurfer/bin/aparcstats2table
+freesurfer/bin/aparc_stats_aseg
+freesurfer/bin/aparcstatsdiff
+freesurfer/bin/apas2aseg
+freesurfer/bin/applyMorph
+freesurfer/bin/aseg2feat
+freesurfer/bin/asegstats2table
+freesurfer/bin/asegstatsdiff
+freesurfer/bin/bblabel
+freesurfer/bin/bbmask
+freesurfer/bin/bedpostx_mgh
+freesurfer/bin/beta2sxa
+freesurfer/bin/bet.fsl
+freesurfer/bin/biasfield
+freesurfer/bin/bmedits2surf
+freesurfer/bin/brec
+freesurfer/bin/brec.awk
+freesurfer/bin/browse-minc-header.tcl
+freesurfer/bin/bugr
+freesurfer/bin/build_desikan_killiany_gcs.csh
+freesurfer/bin/cblumwmgyri
+freesurfer/bin/checkMCR.sh
+freesurfer/bin/check_recons.sh
+freesurfer/bin/check_siemens_dir
+freesurfer/bin/check_subject
+freesurfer/bin/clear_fs_env.csh
+freesurfer/bin/compute_interrater_variability.csh
+freesurfer/bin/compute_label_vals.csh
+freesurfer/bin/compute_label_volumes.csh
+freesurfer/bin/connectgraph
+freesurfer/bin/cor_to_minc
+freesurfer/bin/cp-dicom
+freesurfer/bin/createMorph
+freesurfer/bin/csvprint
+freesurfer/bin/dcmdir-info-mgh
+freesurfer/bin/dcmdjpeg.fs
+freesurfer/bin/dcmdrle.fs
+freesurfer/bin/dcmsplit
+freesurfer/bin/dcmunpack
+freesurfer/bin/deface_subject
+freesurfer/bin/defect-seg
+freesurfer/bin/dicom-rename
+freesurfer/bin/diffusionUtils
+freesurfer/bin/dmri_ac.sh
+freesurfer/bin/dmri_AnatomiCuts
+freesurfer/bin/dmri_bset
+freesurfer/bin/dmri_coloredFA
+freesurfer/bin/dmri_extractSurfaceMeasurements
+freesurfer/bin/dmri_forrest
+freesurfer/bin/dmri_group
+freesurfer/bin/dmri_groupByEndpoints
+freesurfer/bin/dmri_match
+freesurfer/bin/dmri_mergepaths
+freesurfer/bin/dmri_motion
+freesurfer/bin/dmri_neighboringRegions
+freesurfer/bin/dmri_paths
+freesurfer/bin/dmri_pathstats
+freesurfer/bin/dmri_projectEndPoints
+freesurfer/bin/dmrirc.example
+freesurfer/bin/dmrirc.long.example
+freesurfer/bin/dmrirc.long.multiscan.example
+freesurfer/bin/dmrirc.multiscan.example
+freesurfer/bin/dmri_saveHistograms
+freesurfer/bin/dmri_spline
+freesurfer/bin/dmri_stats_ac
+freesurfer/bin/dmri_tensoreig
+freesurfer/bin/dmri_train
+freesurfer/bin/dmri_trk2trk
+freesurfer/bin/dmri_violinPlots
+freesurfer/bin/dmri_vox2vox
+freesurfer/bin/dt_recon
+freesurfer/bin/epidewarp.fsl
+freesurfer/bin/exportGcam
+freesurfer/bin/feat2segstats
+freesurfer/bin/feat2surf
+freesurfer/bin/findsession
+freesurfer/bin/fix_subject
+freesurfer/bin/fix_subject_corrected
+freesurfer/bin/fix_subject_corrected-lh
+freesurfer/bin/fix_subject_corrected-rh
+freesurfer/bin/fix_subject-lh
+freesurfer/bin/fix_subject_on_seychelles
+freesurfer/bin/fix_subject-rh
+freesurfer/bin/fixup_mni_paths
+freesurfer/bin/flip_4dfp
+freesurfer/bin/flirt.fsl
+freesurfer/bin/flirt.newdefault.20080811.sch
+freesurfer/bin/fname2ext
+freesurfer/bin/fname2stem
+freesurfer/bin/freesurfer
+freesurfer/bin/freeview
+freesurfer/bin/fscalc
+freesurfer/bin/fscalc.fsl
+freesurfer/bin/fsdcmdecompress
+freesurfer/bin/fsfget
+freesurfer/bin/fsfirst.fsl
+freesurfer/bin/fs_install_mcr
+freesurfer/bin/fsl.5.0.2.xyztrans.sch
+freesurfer/bin/fs_lib_check
+freesurfer/bin/fsl_label2voxel
+freesurfer/bin/fslmaths.fsl
+freesurfer/bin/fslorient.fsl
+freesurfer/bin/fslregister
+freesurfer/bin/fsl_rigid_register
+freesurfer/bin/fsl_sub_mgh
+freesurfer/bin/fslswapdim.fsl
+freesurfer/bin/fspalm
+freesurfer/bin/fsPrintHelp
+freesurfer/bin/fsr-coreg
+freesurfer/bin/fsr-import
+freesurfer/bin/fs_run_from_mcr
+freesurfer/bin/fs_spmreg.glnxa64
+freesurfer/bin/fs_tutorial_data
+freesurfer/bin/fs_update
+freesurfer/bin/fsvglrun
+freesurfer/bin/fvcompare
+freesurfer/bin/gca-apply
+freesurfer/bin/gcainit
+freesurfer/bin/gcaprepone
+freesurfer/bin/gcatrain
+freesurfer/bin/gcatrainskull
+freesurfer/bin/gdcmconv.fs
+freesurfer/bin/gems_compute_binary_atlas_probs
+freesurfer/bin/get_label_thickness
+freesurfer/bin/groupstats
+freesurfer/bin/groupstatsdiff
+freesurfer/bin/gtmseg
+freesurfer/bin/help_xml_validate
+freesurfer/bin/hiam_make_surfaces
+freesurfer/bin/hiam_make_template
+freesurfer/bin/hiam_register
+freesurfer/bin/histo_compute_joint_density
+freesurfer/bin/histo_fix_topology
+freesurfer/bin/histo_register_block
+freesurfer/bin/histo_segment
+freesurfer/bin/histo_synthesize
+freesurfer/bin/ico_supersample
+freesurfer/bin/id.xfm
+freesurfer/bin/inflate_subject
+freesurfer/bin/inflate_subject3
+freesurfer/bin/inflate_subject-lh
+freesurfer/bin/inflate_subject_new
+freesurfer/bin/inflate_subject_new-lh
+freesurfer/bin/inflate_subject_new-rh
+freesurfer/bin/inflate_subject-rh
+freesurfer/bin/inflate_subject_sc
+freesurfer/bin/irepifitvol
+freesurfer/bin/irepifitvol.glnx64
+freesurfer/bin/isanalyze
+freesurfer/bin/isnifti
+freesurfer/bin/isolate_labels.csh
+freesurfer/bin/isolate_labels_keeporigval.csh
+freesurfer/bin/is-surface
+freesurfer/bin/jkgcatrain
+freesurfer/bin/label2flat
+freesurfer/bin/label2patch
+freesurfer/bin/label_area
+freesurfer/bin/label_border
+freesurfer/bin/label_child
+freesurfer/bin/label_elderly_subject
+freesurfer/bin/labels_disjoint
+freesurfer/bin/labels_intersect
+freesurfer/bin/label_subject
+freesurfer/bin/label_subject_flash
+freesurfer/bin/label_subject_mixed
+freesurfer/bin/labels_union
+freesurfer/bin/list_otl_labels
+freesurfer/bin/listsubj
+freesurfer/bin/long_create_base_sigma
+freesurfer/bin/long_create_orig
+freesurfer/bin/longmc
+freesurfer/bin/long_mris_slopes
+freesurfer/bin/long_qdec_table
+freesurfer/bin/long_stats_combine
+freesurfer/bin/long_stats_slopes
+freesurfer/bin/long_stats_tps
+freesurfer/bin/long_submit_jobs
+freesurfer/bin/long_submit_postproc
+freesurfer/bin/lpcregister
+freesurfer/bin/lta_diff
+freesurfer/bin/make_average_subcort
+freesurfer/bin/make_average_subject
+freesurfer/bin/make_average_surface
+freesurfer/bin/make_average_volume
+freesurfer/bin/make_cortex_label
+freesurfer/bin/make_exvivo_filled
+freesurfer/bin/make_folding_atlas
+freesurfer/bin/make_hemi_mask
+freesurfer/bin/make-segvol-table
+freesurfer/bin/make_symmetric
+freesurfer/bin/make_upright
+freesurfer/bin/makevol
+freesurfer/bin/map_all_labels
+freesurfer/bin/map_all_labels-lh
+freesurfer/bin/map_central_sulcus
+freesurfer/bin/map_to_base
+freesurfer/bin/meanval
+freesurfer/bin/mergeseg
+freesurfer/bin/merge_stats_tables
+freesurfer/bin/minc2seqinfo
+freesurfer/bin/mkheadsurf
+freesurfer/bin/mkima_index.tcl
+freesurfer/bin/mkmnc_index.tcl
+freesurfer/bin/mksubjdirs
+freesurfer/bin/mksurfatlas
+freesurfer/bin/mkxsubjreg
+freesurfer/bin/mni152reg
+freesurfer/bin/morph_only_subject
+freesurfer/bin/morph_only_subject-lh
+freesurfer/bin/morph_only_subject-rh
+freesurfer/bin/morph_rgb-lh
+freesurfer/bin/morph_rgb-rh
+freesurfer/bin/morph_subject
+freesurfer/bin/morph_subject-lh
+freesurfer/bin/morph_subject_on_seychelles
+freesurfer/bin/morph_subject-rh
+freesurfer/bin/morph_tables-lh
+freesurfer/bin/morph_tables-rh
+freesurfer/bin/mri_align_long.csh
+freesurfer/bin/mri_aparc2wmseg
+freesurfer/bin/mri_apply_autoencoder
+freesurfer/bin/mri_apply_bias
+freesurfer/bin/mri_apply_inu_correction
+freesurfer/bin/mri_aseg_edit_reclassify
+freesurfer/bin/mri_aseg_edit_train
+freesurfer/bin/mri_auto_fill
+freesurfer/bin/mri_average
+freesurfer/bin/mri_bc_sc_bias_correct
+freesurfer/bin/mri_brain_volume
+freesurfer/bin/mri_build_priors
+freesurfer/bin/mri_cal_renormalize_gca
+freesurfer/bin/mri_ca_tissue_parms
+freesurfer/bin/mri_ca_train
+freesurfer/bin/mri_cht2p
+freesurfer/bin/mri_classify
+freesurfer/bin/mri_cnr
+freesurfer/bin/mri_compute_bias
+freesurfer/bin/mri_compute_change_map
+freesurfer/bin/mri_compute_distances
+freesurfer/bin/mri_compute_layer_fractions
+freesurfer/bin/mri_compute_structure_transforms
+freesurfer/bin/mri_compute_volume_fractions
+freesurfer/bin/mri_compute_volume_intensities
+freesurfer/bin/mri_concatenate_gcam
+freesurfer/bin/mri_convert_mdh
+freesurfer/bin/mri_copy_params
+freesurfer/bin/mri_copy_values
+freesurfer/bin/mri_cor2label
+freesurfer/bin/mri_correct_segmentations
+freesurfer/bin/mri_create_t2combined
+freesurfer/bin/mri_create_tests
+freesurfer/bin/mri_cvs_check
+freesurfer/bin/mri_cvs_data_copy
+freesurfer/bin/mri_cvs_register
+freesurfer/bin/mri_cvs_requiredfiles.txt
+freesurfer/bin/mri_dct_align
+freesurfer/bin/mri_dct_align_binary
+freesurfer/bin/mri_distance_transform
+freesurfer/bin/mri_dist_surf_label
+freesurfer/bin/mri_divide_segmentation
+freesurfer/bin/mri_edit_segmentation
+freesurfer/bin/mri_edit_segmentation_with_surfaces
+freesurfer/bin/mri_elastic_energy
+freesurfer/bin/mri_estimate_tissue_parms
+freesurfer/bin/mri_evaluate_morph
+freesurfer/bin/mri_extract
+freesurfer/bin/mri_extract_conditions
+freesurfer/bin/mri_extract_fcd_features
+freesurfer/bin/mri_extract_label
+freesurfer/bin/mri_extract_largest_CC
+freesurfer/bin/mri_fcili
+freesurfer/bin/mri_fdr
+freesurfer/bin/mri_fieldsign
+freesurfer/bin/mri_fit_bias
+freesurfer/bin/mri_fslmat_to_lta
+freesurfer/bin/mri-func2sph
+freesurfer/bin/mri-funcvits
+freesurfer/bin/mri_fuse_intensity_images
+freesurfer/bin/mri_gca_ambiguous
+freesurfer/bin/mri_gcab_train
+freesurfer/bin/mri_gdfglm
+freesurfer/bin/mri_glmfit
+freesurfer/bin/mri_glmfit-sim
+freesurfer/bin/mri_gradient_info
+freesurfer/bin/mri_gtmpvc
+freesurfer/bin/mri_gtmseg
+freesurfer/bin/mri_hausdorff_dist
+freesurfer/bin/mri_head
+freesurfer/bin/mri_hires_register
+freesurfer/bin/mri_histo_eq
+freesurfer/bin/mri_histo_normalize
+freesurfer/bin/mri_ibmc
+freesurfer/bin/mri_interpolate
+freesurfer/bin/mri_jacobian
+freesurfer/bin/mri_joint_density
+freesurfer/bin/mri_label_accuracy
+freesurfer/bin/mri_label_histo
+freesurfer/bin/mri_label_vals
+freesurfer/bin/mri_label_volume
+freesurfer/bin/mri_linear_align
+freesurfer/bin/mri_linear_align_binary
+freesurfer/bin/mri_linear_register
+freesurfer/bin/mri_long_normalize
+freesurfer/bin/mri_make_bem_surfaces
+freesurfer/bin/mri_make_density_map
+freesurfer/bin/mri_make_labels
+freesurfer/bin/mri_make_register
+freesurfer/bin/mri_make_template
+freesurfer/bin/mri_map_cpdat
+freesurfer/bin/mri_maps2csd
+freesurfer/bin/mri_mark_temporal_lobe
+freesurfer/bin/mri_mc
+freesurfer/bin/mri_mcsim
+freesurfer/bin/mri_mergelabels
+freesurfer/bin/mri_mi
+freesurfer/bin/mri_modify
+freesurfer/bin/mri_morphology
+freesurfer/bin/mri_mosaic
+freesurfer/bin/mri_motion_correct
+freesurfer/bin/mri_motion_correct2
+freesurfer/bin/mri_ms_EM
+freesurfer/bin/mri_ms_EM_with_atlas
+freesurfer/bin/mri_ms_fitparms
+freesurfer/bin/mri_ms_LDA
+freesurfer/bin/mri_multiscale_segment
+freesurfer/bin/mri_multispectral_segment
+freesurfer/bin/mri_nl_align
+freesurfer/bin/mri_nl_align_binary
+freesurfer/bin/mri_nlfilter
+freesurfer/bin/mri_paint
+freesurfer/bin/mri_parselabel
+freesurfer/bin/mri_parse_sdcmdir
+freesurfer/bin/mri_partial_ribbon
+freesurfer/bin/mri_path2label
+freesurfer/bin/mri_polv
+freesurfer/bin/mri_probedicom
+freesurfer/bin/mri_probe_ima
+freesurfer/bin/mri_reduce
+freesurfer/bin/mri_refine_seg
+freesurfer/bin/mri_register
+freesurfer/bin/mri_reorient_LR.csh
+freesurfer/bin/mri_rf_label
+freesurfer/bin/mri_rf_long_label
+freesurfer/bin/mri_rf_long_train
+freesurfer/bin/mri_rf_train
+freesurfer/bin/mri_ribbon
+freesurfer/bin/mri_rigid_register
+freesurfer/bin/mri_sbbr
+freesurfer/bin/mri_segcentroids
+freesurfer/bin/mri_seghead
+freesurfer/bin/mri_segment_hypothalamic_subunits
+freesurfer/bin/mri_segment_tumor
+freesurfer/bin/mri_segment_wm_damage
+freesurfer/bin/mri_seg_overlap
+freesurfer/bin/mri_simulate_atrophy
+freesurfer/bin/mris2rgb
+freesurfer/bin/mris_AA_shrinkwrap
+freesurfer/bin/mris_add_template
+freesurfer/bin/mris_annot_diff
+freesurfer/bin/mris_annot_to_segmentation
+freesurfer/bin/mris_aseg_distance
+freesurfer/bin/mris_average_curvature
+freesurfer/bin/mris_average_parcellation
+freesurfer/bin/mris_BA_segment
+freesurfer/bin/mris_ca_deform
+freesurfer/bin/mris_ca_train
+freesurfer/bin/mris_classify_thickness
+freesurfer/bin/mris_compute_acorr
+freesurfer/bin/mris_compute_layer_intensities
+freesurfer/bin/mris_compute_lgi
+freesurfer/bin/mris_compute_optimal_kernel
+freesurfer/bin/mris_compute_overlap
+freesurfer/bin/mris_compute_parc_overlap
+freesurfer/bin/mris_compute_volume_fractions
+freesurfer/bin/mris_congeal
+freesurfer/bin/mris_copy_header
+freesurfer/bin/mris_curvature2image
+freesurfer/bin/mris_deform
+freesurfer/bin/mris_density
+freesurfer/bin/mris_distance_map
+freesurfer/bin/mris_distance_to_label
+freesurfer/bin/mris_distance_transform
+freesurfer/bin/mris_entropy
+freesurfer/bin/mris_errors
+freesurfer/bin/mris_extract_patches
+freesurfer/bin/mris_extract_values
+freesurfer/bin/mris_exvivo_surfaces
+freesurfer/bin/mris_fbirn_annot
+freesurfer/bin/mris_fill
+freesurfer/bin/mris_find_flat_regions
+freesurfer/bin/mris_flatten
+freesurfer/bin/mris_fwhm
+freesurfer/bin/mris_gradient
+freesurfer/bin/mris_hausdorff_dist
+freesurfer/bin/mris_image2vtk
+freesurfer/bin/mris_info
+freesurfer/bin/mris_init_global_tractography
+freesurfer/bin/mris_intensity_profile
+freesurfer/bin/mris_interpolate_warp
+freesurfer/bin/mris_label_area
+freesurfer/bin/mris_label_calc
+freesurfer/bin/mris_label_mode
+freesurfer/bin/mris_longitudinal_surfaces
+freesurfer/bin/mris_make_average_surface
+freesurfer/bin/mris_make_face_parcellation
+freesurfer/bin/mris_make_map_surfaces
+freesurfer/bin/mris_make_surfaces
+freesurfer/bin/mris_make_template
+freesurfer/bin/mris_map_cuts
+freesurfer/bin/mris_mef_surfaces
+freesurfer/bin/mris_merge_parcellations
+freesurfer/bin/mris_mesh_subdivide
+freesurfer/bin/mris_morph_stats
+freesurfer/bin/mris_ms_refine
+freesurfer/bin/mris_ms_surface_CNR
+freesurfer/bin/mris_multimodal
+freesurfer/bin/mris_multimodal_surface_placement
+freesurfer/bin/mris_multiscale_stats
+freesurfer/bin/mris_niters2fwhm
+freesurfer/bin/mris_nudge
+freesurfer/bin/mris_parcellate_connectivity
+freesurfer/bin/mri-sph2surf
+freesurfer/bin/mris_pmake
+freesurfer/bin/mris_preproc
+freesurfer/bin/mris_profileClustering
+freesurfer/bin/mrisp_write
+freesurfer/bin/mris_refine_surfaces
+freesurfer/bin/mris_register_label_map
+freesurfer/bin/mris_register_to_label
+freesurfer/bin/mris_register_to_volume
+freesurfer/bin/mris_remove_negative_vertices
+freesurfer/bin/mris_remove_variance
+freesurfer/bin/mris_resample
+freesurfer/bin/mris_rescale
+freesurfer/bin/mris_reverse
+freesurfer/bin/mris_rf_label
+freesurfer/bin/mris_rf_train
+freesurfer/bin/mris_rotate
+freesurfer/bin/mris_sample_label
+freesurfer/bin/mris_sample_parc
+freesurfer/bin/mris_seg2annot
+freesurfer/bin/mris_segment
+freesurfer/bin/mris_segmentation_stats
+freesurfer/bin/mris_segment_vals
+freesurfer/bin/mris_shrinkwrap
+freesurfer/bin/mris_simulate_atrophy
+freesurfer/bin/mris_smooth_intracortical
+freesurfer/bin/mris_surf2vtk
+freesurfer/bin/mris_surface_change
+freesurfer/bin/mris_surface_to_vol_distances
+freesurfer/bin/mris_svm_classify
+freesurfer/bin/mris_svm_train
+freesurfer/bin/mris_talairach
+freesurfer/bin/mris_thickness_comparison
+freesurfer/bin/mris_transform
+freesurfer/bin/mris_translate_annotation
+freesurfer/bin/mris_transmantle_dysplasia_paths
+freesurfer/bin/mri_strip_nonwhite
+freesurfer/bin/mri_strip_subject_info
+freesurfer/bin/mris_twoclass
+freesurfer/bin/mri_surfacemask
+freesurfer/bin/mris_volmask_novtk
+freesurfer/bin/mris_volmask_vtk
+freesurfer/bin/mris_volsmooth
+freesurfer/bin/mris_volume
+freesurfer/bin/mris_warp
+freesurfer/bin/mris_wm_volume
+freesurfer/bin/mris_w_to_curv
+freesurfer/bin/mri_synthesize
+freesurfer/bin/mri_synthstrip
+freesurfer/bin/mri_threshold
+freesurfer/bin/mri_topologycorrection
+freesurfer/bin/mri_train
+freesurfer/bin/mri_train_autoencoder
+freesurfer/bin/mri_transform
+freesurfer/bin/mri_transform_to_COR
+freesurfer/bin/mri_twoclass
+freesurfer/bin/mri_update_gca
+freesurfer/bin/mri_validate_skull_stripped
+freesurfer/bin/mri_vessel_segment
+freesurfer/bin/mri_vol2label
+freesurfer/bin/mri_vol2roi
+freesurfer/bin/mri_volcluster
+freesurfer/bin/mri_volsynth
+freesurfer/bin/mri_warp_convert
+freesurfer/bin/mri_wbc
+freesurfer/bin/mri_wmfilter
+freesurfer/bin/mri_xcorr
+freesurfer/bin/mri_xvolavg
+freesurfer/bin/mri_z2p
+freesurfer/bin/ms_refine_subject
+freesurfer/bin/nmovie_qt
+freesurfer/bin/oct_register_mosaic
+freesurfer/bin/oct_rf_train
+freesurfer/bin/oct_train
+freesurfer/bin/optseq2
+freesurfer/bin/orientLAS
+freesurfer/bin/parc_atlas_jackknife_test
+freesurfer/bin/plot_structure_stats.tcl
+freesurfer/bin/polyorder
+freesurfer/bin/predict_v1.sh
+freesurfer/bin/print_unique_labels.csh
+freesurfer/bin/progressbar.tcl
+freesurfer/bin/qatools.py
+freesurfer/bin/qdec
+freesurfer/bin/qdec_glmfit
+freesurfer/bin/qt.conf
+freesurfer/bin/quantifyBrainstemStructures.sh
+freesurfer/bin/quantifyHAsubregions.sh
+freesurfer/bin/quantifyThalamicNuclei.sh
+freesurfer/bin/rbbr
+freesurfer/bin/rbftest
+freesurfer/bin/rcbf-prep
+freesurfer/bin/rebuild_gca_atlas.csh
+freesurfer/bin/recon-all-exvivo
+freesurfer/bin/recon-all.makefile
+freesurfer/bin/regdat2xfm
+freesurfer/bin/reg-feat2anat
+freesurfer/bin/register_child
+freesurfer/bin/register.csh
+freesurfer/bin/register_elderly_subject
+freesurfer/bin/register_subject
+freesurfer/bin/register_subject_flash
+freesurfer/bin/register_subject_mixed
+freesurfer/bin/reg-mni305.2mm
+freesurfer/bin/reinflate_subject
+freesurfer/bin/reinflate_subject-lh
+freesurfer/bin/reinflate_subject-rh
+freesurfer/bin/remove_talairach
+freesurfer/bin/renormalize_subject
+freesurfer/bin/renormalize_subject_keep_editting
+freesurfer/bin/renormalize_T1_subject
+freesurfer/bin/repair_siemens_file
+freesurfer/bin/reregister_subject_mixed
+freesurfer/bin/rtview
+freesurfer/bin/run_mris_preproc
+freesurfer/bin/run-qdec-glm
+freesurfer/bin/run_samseg_long
+freesurfer/bin/run_SegmentSubfieldsT1Longitudinal.sh
+freesurfer/bin/run_SegmentSubject.sh
+freesurfer/bin/run_segmentSubjectT1_autoEstimateAlveusML.sh
+freesurfer/bin/run_segmentSubjectT1T2_autoEstimateAlveusML.sh
+freesurfer/bin/run_segmentSubjectT2_autoEstimateAlveusML.sh
+freesurfer/bin/run_SegmentThalamicNuclei.sh
+freesurfer/bin/samseg
+freesurfer/bin/samseg2recon
+freesurfer/bin/samseg-long
+freesurfer/bin/sbtiv
+freesurfer/bin/seg2filled
+freesurfer/bin/segmentBS.sh
+freesurfer/bin/segmentHA_T1_long.sh
+freesurfer/bin/segmentHA_T1.sh
+freesurfer/bin/segmentHA_T2.sh
+freesurfer/bin/segment_monkey
+freesurfer/bin/SegmentSubfieldsT1Longitudinal
+freesurfer/bin/segment_subject
+freesurfer/bin/segmentSubject
+freesurfer/bin/segment_subject_notal
+freesurfer/bin/segment_subject_notal2
+freesurfer/bin/segment_subject_old_skull_strip
+freesurfer/bin/segment_subject_sc
+freesurfer/bin/segmentSubjectT1_autoEstimateAlveusML
+freesurfer/bin/segmentSubjectT1T2_autoEstimateAlveusML
+freesurfer/bin/segmentSubjectT2_autoEstimateAlveusML
+freesurfer/bin/segment_subject_talmgh
+freesurfer/bin/SegmentThalamicNuclei
+freesurfer/bin/segmentThalamicNuclei.sh
+freesurfer/bin/segpons
+freesurfer/bin/setlabelstat
+freesurfer/bin/sfa2fieldsign
+freesurfer/bin/show_tal
+freesurfer/bin/skip_long_make_checks
+freesurfer/bin/slicedelay
+freesurfer/bin/slicetimer.fsl
+freesurfer/bin/sphere_subject
+freesurfer/bin/sphere_subject-lh
+freesurfer/bin/sphere_subject-rh
+freesurfer/bin/spherical_st
+freesurfer/bin/Spline3_test
+freesurfer/bin/spmmat2register
+freesurfer/bin/spmregister
+freesurfer/bin/spm_t_to_b
+freesurfer/bin/sratio
+freesurfer/bin/stat_normalize
+freesurfer/bin/stattablediff
+freesurfer/bin/stem2fname
+freesurfer/bin/stim_polar
+freesurfer/bin/streamlineFilter
+freesurfer/bin/surf2vol
+freesurfer/bin/surfreg
+freesurfer/bin/swi_preprocess
+freesurfer/bin/swi_process
+freesurfer/bin/t4img_4dfp
+freesurfer/bin/t4imgs_4dfp
+freesurfer/bin/talairach2
+freesurfer/bin/talairach_mgh
+freesurfer/bin/tal_compare
+freesurfer/bin/tal_QC_AZS
+freesurfer/bin/talsegprob
+freesurfer/bin/template
+freesurfer/bin/testOrientationPlanesFromParcellation
+freesurfer/bin/test_recon-all.csh
+freesurfer/bin/test_tutorials.sh
+freesurfer/bin/thickdiffmap
+freesurfer/bin/tkmedit
+freesurfer/bin/tkmeditfv
+freesurfer/bin/tkregisterfv
+freesurfer/bin/tksurfer
+freesurfer/bin/tksurferfv
+freesurfer/bin/trac-all
+freesurfer/bin/trac-paths
+freesurfer/bin/trac-preproc
+freesurfer/bin/tractstats2table
+freesurfer/bin/train-gcs-atlas
+freesurfer/bin/tridec
+freesurfer/bin/trk_tools
+freesurfer/bin/unpack_ima1.tcl
+freesurfer/bin/unpackimadir
+freesurfer/bin/unpackimadir2
+freesurfer/bin/unpack_ima.tcl
+freesurfer/bin/unpackmincdir
+freesurfer/bin/unpack_mnc.tcl
+freesurfer/bin/unpacksdcmdir
+freesurfer/bin/usbtree
+freesurfer/bin/vol2segavg
+freesurfer/bin/vol2subfield
+freesurfer/bin/vol2symsurf
+freesurfer/bin/vsm-smooth
+freesurfer/bin/wfilemask
+freesurfer/bin/wm-anat-snr
+freesurfer/bin/wmedits2surf
+freesurfer/bin/wmsaseg
+freesurfer/bin/xcerebralseg
+freesurfer/bin/xcorr
+freesurfer/bin/xfmrot
+freesurfer/bin/xhemireg
+freesurfer/bin/xhemi-tal
+freesurfer/bin/xsanatreg
+freesurfer/bin/zero_lt_4dfp
+freesurfer/DefectLUT.txt
+freesurfer/diffusion
+freesurfer/docs/xml
+freesurfer/FreeSurferEnv.csh
+freesurfer/FreeSurferEnv.sh
+freesurfer/fsfast
+freesurfer/lib/bem/ic0.tri
+freesurfer/lib/bem/ic1.tri
+freesurfer/lib/bem/ic2.tri
+freesurfer/lib/bem/ic3.tri
+freesurfer/lib/bem/ic6.tri
+freesurfer/lib/bem/inner_skull.dat
+freesurfer/lib/bem/outer_skin.dat
+freesurfer/lib/bem/outer_skull.dat
+freesurfer/lib/images
+freesurfer/lib/qt
+freesurfer/lib/resource
+freesurfer/lib/tcl
+freesurfer/lib/vtk
+freesurfer/matlab
+freesurfer/mni-1.4
+freesurfer/mni/bin/autocrop
+freesurfer/mni/bin/check_scale
+freesurfer/mni/bin/correct_field
+freesurfer/mni/bin/crispify
+freesurfer/mni/bin/dcm2mnc
+freesurfer/mni/bin/Display
+freesurfer/mni/bin/ecattominc
+freesurfer/mni/bin/evaluate_field
+freesurfer/mni/bin/extracttag
+freesurfer/mni/bin/field2imp
+freesurfer/mni/bin/imp2field
+freesurfer/mni/bin/invert_raw_image
+freesurfer/mni/bin/make_model
+freesurfer/mni/bin/make_phantom
+freesurfer/mni/bin/make_template
+freesurfer/mni/bin/mincaverage
+freesurfer/mni/bin/mincbbox
+freesurfer/mni/bin/mincblur
+freesurfer/mni/bin/minccalc
+freesurfer/mni/bin/mincchamfer
+freesurfer/mni/bin/mincconcat
+freesurfer/mni/bin/minccopy
+freesurfer/mni/bin/mincdiff
+freesurfer/mni/bin/mincedit
+freesurfer/mni/bin/mincexpand
+freesurfer/mni/bin/mincextract
+freesurfer/mni/bin/mincheader
+freesurfer/mni/bin/minchistory
+freesurfer/mni/bin/mincinfo
+freesurfer/mni/bin/minclookup
+freesurfer/mni/bin/mincmakescalar
+freesurfer/mni/bin/mincmakevector
+freesurfer/mni/bin/mincmath
+freesurfer/mni/bin/minc_modify_header
+freesurfer/mni/bin/mincpik
+freesurfer/mni/bin/mincresample
+freesurfer/mni/bin/mincreshape
+freesurfer/mni/bin/mincstats
+freesurfer/mni/bin/minctoecat
+freesurfer/mni/bin/minctoraw
+freesurfer/mni/bin/minctracc
+freesurfer/mni/bin/mincview
+freesurfer/mni/bin/mincwindow
+freesurfer/mni/bin/mnc2nii
+freesurfer/mni/bin/mritoself
+freesurfer/mni/bin/mritotal
+freesurfer/mni/bin/mritotal~
+freesurfer/mni/bin/ncdump
+freesurfer/mni/bin/ncgen
+freesurfer/mni/bin/nii2mnc
+freesurfer/mni/bin/nu_estimate
+freesurfer/mni/bin/nu_estimate_np_and_em~
+freesurfer/mni/bin/nu_evaluate
+freesurfer/mni/bin/param2xfm
+freesurfer/mni/bin/rand_param
+freesurfer/mni/bin/rawtominc
+freesurfer/mni/bin/register
+freesurfer/mni/bin/resample_labels
+freesurfer/mni/bin/sharpen_hist
+freesurfer/mni/bin/sharpen_volume
+freesurfer/mni/bin/spline_smooth
+freesurfer/mni/bin/transformtags
+freesurfer/mni/bin/upet2mnc
+freesurfer/mni/bin/volume_cog
+freesurfer/mni/bin/volume_hist
+freesurfer/mni/bin/volume_stats
+freesurfer/mni/bin/voxeltoworld
+freesurfer/mni/bin/worldtovoxel
+freesurfer/mni/bin/xcorr_vol
+freesurfer/mni/bin/xfm2param
+freesurfer/mni/bin/xfmconcat
+freesurfer/mni/bin/xfminvert
+freesurfer/mni/bin/xfmtool
+freesurfer/mni/bin/zscore_vol
+freesurfer/mni/data
+freesurfer/mni/etc
+freesurfer/mni/include
+freesurfer/mni/mni.srcbuild.June2015.tgz
+freesurfer/mni/share/man
+freesurfer/mni/share/mni_autoreg
+freesurfer/mni/share/N3
+freesurfer/models
+freesurfer/python*
+freesurfer/SegmentNoLUT.txt
+freesurfer/sessions
+freesurfer/SetUpFreeSurfer.csh
+freesurfer/SetUpFreeSurfer.sh
+freesurfer/Simple_surface_labels2009.txt
+freesurfer/sources.sh
+freesurfer/subjects*
+freesurfer/trctrain
diff --git a/docker/files/script_preferences.txt b/docker/files/script_preferences.txt
new file mode 100644
index 0000000..697c720
--- /dev/null
+++ b/docker/files/script_preferences.txt
@@ -0,0 +1,191 @@
+%%%%%%%%% GLOBAL SCRIPT PREFERENCES %%%%%%%%%
+%% RUNA OPTIONS
+% Load image, ROI
+% Calculate concentration vs. time curves from images
+%%%%%%%%%%%%%%%%
+
+%% A_make_R1maps_func
+    % loadIMGVOL
+
+    % 1 = 4D, 2 = 3D, 3 = 2D
+    filevolume = 1
+
+    % 1 = pick noise file, 0 = derive noise from corner square 
+    noise_pathpick = 0
+
+    % width of corner square noise selection
+    noise_pixsize = 9
+
+    % probably deprecated (COPIUM)
+    LUT = 1
+
+    % input nifti datasets, can specify subfolders and use wildcard *
+    dynamic_files = /dce/*_desc-bfcz_DCE.nii*
+
+    aif_files = /dce/*desc-AIF_T1map.nii*
+    roi_files = /anat/*space-DCEref_desc-brain_mask.nii*
+    t1map_files = /anat/*space-DCEref_T1map.nii*
+    noise_files =
+    drift_files =
+
+    % what t-dimension image to start from/end on
+    % useful for excluding bad data points at beginning or end
+    % leave blank to do from beginning/to end
+    start_t = 3
+    end_t = 
+
+    % Name of output file
+    rootname = dce
+
+    % X, Y, Z, t 'xyzt' or X, Y, t, Z 'xytz'
+    fileorder = xyzt
+
+    % boolean value indicating whether to pursue
+    % quantitative DCE vs. semi-quant values
+    quant = 1
+
+    % roi_files is a T1 map=0; roi_files is a mask=1
+    roimaskroi = 1
+    % aif_files is a T1 map=0; aif_files is a mask=1
+    aifmaskroi = 0
+
+% Indicates the type of vascular input ROI, 
+% 'rr' = reference region
+% 'aif_roi' = arterial input
+% 'aif_roi_static' = arterial input with user defined T1 value
+% 'aif_auto' = AIF is to be auto found 
+% 'aif_auto_static' = auto found with user defined T1 value 
+aif_rr_type = aif_roi
+
+% repetition time of dynamic scan (ms), overwritten by json if it exists
+tr = 8
+
+% flip angle of dynamic scan (degrees), overwritten by json if it exists
+fa = 15
+
+% hematocrit percent (0 - 1.00) of subject
+hematocrit = 0.45
+
+% snr required for AIF voxels, snr must exceed this
+% value averaged over all time points
+snr_filter = 1
+
+% -1 = determine via figure, -2 = auto
+injection_time = -2
+
+% r1 relaxivity (in mM^-1*sec^-1) of contrast agent
+% if AcquisitionDateTime field exists in json, CLI auto determines for this study:
+% pre-Oct 2017 assumes MultiHance
+% post-Oct 2017 assumes dotarem
+% Shen 2015: https://journals.lww.com/investigativeradiology/fulltext/2015/05000/t1_relaxivities_of_gadolinium_based_magnetic.4.aspx
+% dotarem 3T, human blood = 3.43 ± 0.29, 3.19 ± 0.4 [3.4 ± 0.4]
+% magnevist 3T, human blood = 3.76 ± 0.17, 3.34 ± 0.19 [3.8 ± 0.2]
+% MH 3T - https://journals.lww.com/investigativeradiology/fulltext/2015/05000/t1_relaxivities_of_gadolinium_based_magnetic.4.aspx
+% https://www.bracco.com/sites/default/files/2022-10/kr-en-2016-04-30-brochure-the-strenght-of-relaxivity-multihance.pdf
+% https://journals.lww.com/investigativeradiology/fulltext/2006/03000/relaxivity_of_gadopentetate_dimeglumine.3.aspx
+% https://appliedradiology.com/articles/mri-contrast-selection-greater-stability-or-higher-relaxivity
+% MultiHance 3T, human blood = 5.4 ± 0.3 (2015; appliedradiology.com), 5.9 ± 0.4, <6ish (bracco), 6.3 ± 0.4 (2006, <1 mM), 6.3 (mriquestions.com)
+% Szomolanyi 2019: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738537/
+% dotarem 3T, human blood - 2.72 ± 0.17
+% magnevist 3T, human blood - 3.3 ± 0.2
+relaxivity = 2.8
+% set below to 1 if you wish to use the value above with the CLI command.
+force_use_default_relaxivity = 0
+
+% boolean value, perform drift correction globally,
+% not on a slice by slice basis
+drift_global = 1
+
+% the T1 value of blood (in s) only used if
+% aif_rr_type is set to 'aif_auto_static' or 'aif_roi_static'
+blood_t1 = 2.000
+
+% the length of the contrast agent injection in
+% number of acquisitions, for auto AIF only
+injection_duration = 1
+
+%%%%%%%%%%%%%%%%
+%% RUNB OPTIONS
+% Define timing parameters for analysis, derive fitted AIF or reference
+% region from 1+ datasets
+
+% start time to restrict analysis to (in minutes, 0 if all)
+start_time = 0
+
+% end time to restrict analysis to (in minutes, 0 if all)
+end_time = 0
+
+% disable if you really know what you're doing
+auto_find_injection = 1
+    % start of contrast injection (in minutes)
+    start_injection = 0.0867
+    % end of contrast injection (in minutes)
+    end_injection = 0.2167
+
+% time resolution of dynamic scan (in seconds)
+time_resolution = 15.36
+
+% raw = 2, fitted = 1, import = 3, raw for reference region
+aif_type = 1
+   import_aif_path =
+
+% bool to use manual time vector
+timevectyn = 0
+    % import manual time vector from .mat file (string)
+    timevectpath = 
+
+%%%%%%%%%%%%%%%%
+%% RUND OPTIONS
+% Choose kinetic models for data fitting, derive parameters for single
+% dataset. Remember to check `dce_preferences.txt` for fitting parameters.
+
+% DCE model bools (1 = use), T1 maps required for all except auc
+% Tofts
+tofts = 0
+
+% Tofts extended (w/ Vp)
+ex_tofts = 0
+
+% "shutter speed"
+fxr = 0
+
+% area under the curve (does not require T1 map)
+auc = 0
+
+% series of nested model
+nested = 0
+
+% 2 param model w/ no backflux
+patlak = 1
+
+% 3 param model, assumes cp>>ct
+tissue_uptake = 0
+
+% Two Compartment eXchange Model
+two_cxm = 0
+
+% Fast eXchange Limit tofts with reference region
+FXL_rr = 0
+
+
+%dce_model.fractal = 
+
+% type of time smoothing
+% options: none, moving (avg), rlowess (robust local regression)
+time_smoothing = none
+% size of time smoothing window (in time points)
+time_smoothing_window = 5
+% sigma of the Gaussian low pass smooth function
+xy_smooth_size = 0
+% # of CPU cores for parallel processing (0 for max, -1 for max-1)
+number_cpus = 0
+
+% paths to ROIs that specify homogenous regions to calculate a single
+% DCE fit for. Values >0 considered in the ROI, values <=0 considered
+% outside the ROI
+% multiple files syntax = file1.nii file2.nii file3.nii
+roi_list = 
+% bool to perform DCE fit on individual voxels
+fit_voxels = 1
+% output filetype (1 = NIFTI, 2 = 3D DICOM, 3 = 2D DICOM)
+outputft = 1
diff --git a/iNESMA_GPU.py b/iNESMA_GPU.py
new file mode 100644
index 0000000..a813807
--- /dev/null
+++ b/iNESMA_GPU.py
@@ -0,0 +1,182 @@
+import nibabel as nib
+import numpy as np
+from numba import cuda, float32, int32
+import math
+import sys
+
+def load_image(img_path):
+    img = nib.load(img_path)
+    data = img.get_fdata()
+    return data, img
+
+@cuda.jit
+def inesma_kernel(data, smoothed_data, x_dim, y_dim, z_dim, t_dim, 
+                  local_neighborhood_x, local_neighborhood_y, local_neighborhood_z, 
+                  similarity_threshold, h):
+    x, y, z = cuda.grid(3)
+    if x < x_dim and y < y_dim and z < z_dim:
+        current_curve = cuda.local.array(shape=(100,), dtype=float32)  # Adjust size as needed
+        for t in range(t_dim):
+            current_curve[t] = data[x, y, z, t]
+        array_size=local_neighborhood_x*local_neighborhood_y*local_neighborhood_z
+        similarities = cuda.local.array(shape=(1024,), dtype=float32)
+        indices = cuda.local.array(shape=(1024, 3), dtype=int32)
+        count = 0
+
+        for i in range(max(0, x-local_neighborhood_x), min(x_dim, x+local_neighborhood_x+1)):
+            for j in range(max(0, y-local_neighborhood_y), min(y_dim, y+local_neighborhood_y+1)):
+                for k in range(max(0, z-local_neighborhood_z), min(z_dim, z+local_neighborhood_z+1)):
+                    if i == x and j == y and k == z:
+                        continue
+                    
+                    neighbor_curve = cuda.local.array(shape=(100,), dtype=float32)  # Adjust size as needed
+                    for t in range(t_dim):
+                        neighbor_curve[t] = data[i, j, k, t]
+
+                    dist = 0.0
+                    location = 0.0
+                    for t in range(t_dim):
+                        dist += (current_curve[t] - neighbor_curve[t]) ** 2
+                        location += current_curve[t] ** 2
+                    dist = math.sqrt(dist)
+                    location = math.sqrt(location)
+                    
+                    #similarity = math.exp(-dist / h)
+                    similarity = dist/location
+                    
+                    similarities[count] = similarity
+                    indices[count, 0] = i
+                    indices[count, 1] = j
+                    indices[count, 2] = k
+                    count += 1
+        
+        # # Find the top 5% most similar voxels
+        # threshold_index = max(1, int(similarity_threshold * count))
+        
+        # # Manual selection sort to find top 5% similarities
+        # # Uhhh, there are library functions for this....
+        # for i in range(threshold_index):
+        #     max_idx = i
+        #     for j in range(i+1, count):
+        #         if similarities[j] > similarities[max_idx]:
+        #             max_idx = j
+        #     if max_idx != i:
+        #         # Swap values in similarities
+        #         temp_similarity = similarities[i]
+        #         similarities[i] = similarities[max_idx]
+        #         similarities[max_idx] = temp_similarity
+                
+        #         # Swap values in indices
+        #         temp_indices = (indices[i, 0], indices[i, 1], indices[i, 2])
+        #         indices[i, 0] = indices[max_idx, 0]
+        #         indices[i, 1] = indices[max_idx, 1]
+        #         indices[i, 2] = indices[max_idx, 2]
+        #         indices[max_idx, 0] = temp_indices[0]
+        #         indices[max_idx, 1] = temp_indices[1]
+        #         indices[max_idx, 2] = temp_indices[2]
+        
+        weighted_sum = cuda.local.array(shape=(100,), dtype=float32)  # Adjust size as needed
+        for t in range(t_dim):
+            weighted_sum[t] = 0.0
+        normalization_factor = 0.0
+        
+        for idx in range(count):
+            if similarities[idx] < similarity_threshold:
+                # if constant_weighting:
+                if h == 0:
+                    similarity = 1
+                else:
+                    similarity = math.exp(-similarities[idx] / h)
+                i = indices[idx, 0]
+                j = indices[idx, 1]
+                k = indices[idx, 2]
+                
+                neighbor_curve = cuda.local.array(shape=(100,), dtype=float32)  # Adjust size as needed
+                for t in range(t_dim):
+                    neighbor_curve[t] = data[i, j, k, t]
+                
+                for t in range(t_dim):
+                    weighted_sum[t] += similarity * neighbor_curve[t]
+                normalization_factor += similarity
+        
+        if normalization_factor != 0:
+            for t in range(t_dim):
+                smoothed_data[x, y, z, t] = weighted_sum[t] / normalization_factor
+        else:
+            for t in range(t_dim):
+                smoothed_data[x, y, z, t] = current_curve[t]
+
+def inesma_smoothing(data, num_iterations=2, local_neighborhood_x=5,local_neighborhood_y=5,local_neighborhood_z=2,
+                     similarity_threshold=0.3, h=0.01):
+    # num_iterations defines the number of iterations for smoothing
+
+    # local_neighborhood defines radius in voxels of neighborhood for smoothing
+    # TODO: this should be defined in mm, not in voxels
+
+    # similarity_threshold defines the threshold to accept a voxel for smoothing
+
+    # h defines the exponential decay of the weighting, h=0 means constant weighting
+    if (local_neighborhood_x*2+1)*(local_neighborhood_y*2+1)*(local_neighborhood_z*2+1) > 1024:
+        raise ValueError("The neighborhood size exceeds the fixed array size in the kernel. Adjust the array size in the kernel.")
+
+    x_dim, y_dim, z_dim, t_dim = data.shape
+    smoothed_data = np.copy(data)
+    
+    data_device = cuda.to_device(data)
+    smoothed_data_device = cuda.to_device(smoothed_data)
+    
+    threads_per_block = (8, 8, 8)
+    blocks_per_grid_x = (x_dim + threads_per_block[0] - 1) // threads_per_block[0]
+    blocks_per_grid_y = (y_dim + threads_per_block[1] - 1) // threads_per_block[1]
+    blocks_per_grid_z = (z_dim + threads_per_block[2] - 1) // threads_per_block[2]
+    
+    for _ in range(num_iterations):
+        inesma_kernel[(blocks_per_grid_x, blocks_per_grid_y, blocks_per_grid_z), threads_per_block](
+            data_device, smoothed_data_device, x_dim, y_dim, z_dim, t_dim, 
+            local_neighborhood_x, local_neighborhood_y, local_neighborhood_z,
+            similarity_threshold, h
+        )
+        data_device = cuda.to_device(smoothed_data_device.copy_to_host())
+    
+    smoothed_data = smoothed_data_device.copy_to_host()
+    return smoothed_data
+
+# Load and smooth the image
+prefix = sys.argv[1]
+img_path = sys.argv[2]
+data, img = load_image(img_path)
+
+roi_path = sys.argv[3]
+roi_data, roi_img = load_image(roi_path)
+
+# expand roi_data to 4D and match the shape of data
+# roi_data = np.expand_dims(roi_data, axis=3)
+# roi_data = np.repeat(roi_data, data.shape[3], axis=3)
+
+# save data where roi_data > 0
+data_restore = np.copy(data)
+data_restore[roi_data == 0] = 0
+
+# exclude roi_data from data
+data[roi_data > 0] = 0
+
+# Ensure t_dim does not exceed the array size in the kernel
+t_dim = data.shape[3]
+if t_dim > 100:
+    raise ValueError("The time dimension (t_dim) exceeds the fixed array size in the kernel. Adjust the array size in the kernel.")
+
+# normalize the data robustly (2nd and 98th percentile)
+p2 = np.percentile(data, 2)
+p98 = np.percentile(data, 98)
+data = np.clip(data, p2, p98)
+data = (data - p2) / (p98 - p2)
+
+# SHMOOOTHING
+smoothed_data = inesma_smoothing(data)
+
+# Restore roi in smoothed data
+smoothed_data[roi_data > 0] = data_restore[roi_data > 0]
+
+# Save the smoothed image
+smoothed_img = nib.Nifti1Image(smoothed_data, img.affine, img.header)
+nib.save(smoothed_img, f'dce/{prefix}_desc-bfczSmoothed_DCE.nii.gz')
diff --git a/auto_analysis.py b/ktrans_analysis.py
similarity index 84%
rename from auto_analysis.py
rename to ktrans_analysis.py
index 47112a5..e388181 100644
--- a/auto_analysis.py
+++ b/ktrans_analysis.py
@@ -1,9 +1,10 @@
+from utils.constants import KTRANS_MIN_THRESHOLD
 import sys
 from pathlib import Path
 from statistics import mean, median, pstdev, stdev
 import numpy as np
 import matplotlib
-from numpy.lib.function_base import average
+# from numpy.lib.function_base import average
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mtick
@@ -12,14 +13,19 @@
 
 # add as arg? add mask arg?
 POLYFIT = True
-KTRANS_MIN_THRESHOLD = 0.00001
 
-def analyze(file_dir):
+def analyze(tp_dir):
     # load files from script pipeline
-    files = ['/T1_wm.nii.gz', '/T1_gm.nii.gz', '/T1_csf.nii.gz', '/Ktrans_wm.nii.gz',
-            '/Ktrans_gm.nii.gz', '/Ktrans_csf.nii.gz', '/15_wm_mask_dyn.nii.gz', '/15_gm_mask_dyn.nii.gz']
-    for i, file in enumerate(files):
-        files[i] = file_dir + file
+    files = ['anat/' + prefix + '_space-DCEref_label-WM_T1map.nii.gz',
+             'anat/' + prefix + '_space-DCEref_label-GM_T1map.nii.gz',
+             'anat/' + prefix + '_space-DCEref_label-CSF_T1map.nii.gz',
+             'dce/' + prefix + '_seg-WM_Ktrans.nii.gz',
+             'dce/' + prefix + '_seg-GM_Ktrans.nii.gz',
+             'dce/' + prefix + '_seg-CSF_Ktrans.nii.gz',
+             'anat/' + prefix + '_space-DCEref_label-WM_mask.nii.gz',
+             'anat/' + prefix + '_space-DCEref_label-GM_mask.nii.gz']
+    # for i, file in enumerate(files):
+    #     files[i] = file_dir + file
 
     T1_wm = nib.load(files[0])
     T1_gm = nib.load(files[1])
@@ -104,13 +110,13 @@ def analyze(file_dir):
         
         a = np.where(Ktrans_wm_data[:, :, i] > KTRANS_MIN_THRESHOLD)
         if a[0].size > 0:
-            Ktrans_wm_mean.append(Ktrans_wm_data[:, :, i][a].mean())
+            Ktrans_wm_mean.append(np.nanmean(Ktrans_wm_data[:, :, i][a]))
         else:
             Ktrans_wm_mean.append(0)
         
         a = np.where(Ktrans_gm_data[:, :, i] > KTRANS_MIN_THRESHOLD)
         if a[0].size > 0:
-            Ktrans_gm_mean.append(Ktrans_gm_data[:, :, i][a].mean())
+            Ktrans_gm_mean.append(np.nanmean(Ktrans_gm_data[:, :, i][a]))
         else:
             Ktrans_gm_mean.append(0)
 
@@ -122,13 +128,13 @@ def analyze(file_dir):
         
         a = np.where(Ktrans_wm_data[:, :, i] > KTRANS_MIN_THRESHOLD)
         if a[0].size > 0:
-            Ktrans_wm_median.append(median(Ktrans_wm_data[:, :, i][a]))
+            Ktrans_wm_median.append(np.nanmedian(Ktrans_wm_data[:, :, i][a]))
         else:
             Ktrans_wm_median.append(0)
 
         a = np.where(Ktrans_gm_data[:, :, i] > KTRANS_MIN_THRESHOLD)
         if a[0].size > 0:
-            Ktrans_gm_median.append(median(Ktrans_gm_data[:, :, i][a]))
+            Ktrans_gm_median.append(np.nanmedian(Ktrans_gm_data[:, :, i][a]))
         else:
             Ktrans_gm_median.append(0)
 
@@ -175,10 +181,10 @@ def analyze(file_dir):
     Ktrans_wm_1D = Ktrans_wm_data.flatten()
     Ktrans_gm_1D = Ktrans_gm_data.flatten()
     # Ktrans_csf_data = Ktrans_csf_data.flatten()
-    Ktrans_wm_median_truncated = median(Ktrans_wm_1D[Ktrans_wm_1D > KTRANS_MIN_THRESHOLD])
-    Ktrans_gm_median_truncated = median(Ktrans_gm_1D[Ktrans_gm_1D > KTRANS_MIN_THRESHOLD])
-    Ktrans_wm_stdev_truncated = stdev(Ktrans_wm_1D[Ktrans_wm_1D > KTRANS_MIN_THRESHOLD])
-    Ktrans_gm_stdev_truncated = stdev(Ktrans_gm_1D[Ktrans_gm_1D > KTRANS_MIN_THRESHOLD])
+    Ktrans_wm_median_truncated = np.nanmedian(Ktrans_wm_1D[Ktrans_wm_1D > KTRANS_MIN_THRESHOLD])
+    Ktrans_gm_median_truncated = np.nanmedian(Ktrans_gm_1D[Ktrans_gm_1D > KTRANS_MIN_THRESHOLD])
+    Ktrans_wm_stdev_truncated = np.nanstd(Ktrans_wm_1D[Ktrans_wm_1D > KTRANS_MIN_THRESHOLD])
+    Ktrans_gm_stdev_truncated = np.nanstd(Ktrans_gm_1D[Ktrans_gm_1D > KTRANS_MIN_THRESHOLD])
 
     # for i in range(slice_num)
     #     T1_wm_zeros = size(T1_wm_data[T1_wm_data[:,:,i] == 0])
@@ -241,9 +247,9 @@ def analyze(file_dir):
     _, max_ylim = ax1.get_ylim()
     ax1.axvline(Ktrans_wm_median_truncated, color='pink', linestyle='dashed')
     ax1.axvline(Ktrans_gm_median_truncated, color='gray', linestyle='dashed')
-    ax1.text(Ktrans_wm_median_truncated*.1, max_ylim*0.2, 'Median: {:.5f}'.format(Ktrans_wm_median_truncated), color='whitesmoke')
+    ax1.text(Ktrans_wm_median_truncated*.1, max_ylim*0.2, 'Median: {:.5f}'.format(Ktrans_wm_median_truncated), color='lightgray')
     ax1.text(Ktrans_gm_median_truncated*1.1, max_ylim*0.9, 'Median: {:.5f}'.format(Ktrans_gm_median_truncated), color='gray')
-    ax1.text(Ktrans_wm_median_truncated*.1, max_ylim*0.1, 'stdev: {:.5f}'.format(Ktrans_wm_stdev_truncated), color='whitesmoke')
+    ax1.text(Ktrans_wm_median_truncated*.1, max_ylim*0.1, 'stdev: {:.5f}'.format(Ktrans_wm_stdev_truncated), color='lightgray')
     ax1.text(Ktrans_gm_median_truncated*1.1, max_ylim*0.8, 'stdev: {:.5f}'.format(Ktrans_gm_stdev_truncated), color='gray')
     # ax1.axvline(mean(Ktrans_csf_data[Ktrans_csf_data > 0]), color = 'cyan', linestyle = 'dashed')
     ax1.legend()
@@ -251,7 +257,7 @@ def analyze(file_dir):
     ## T1 medians plot
     ax2.set_xlabel('Slice #')
     ax2.set_ylabel('T1 Medians')
-    ax2.set_ylim([0, 2200])
+    ax2.set_ylim([0, 2700])
     ax2.plot(range(slice_num), T1_wm_median, label='wm', color='pink')
     ax2.plot(range(slice_num), T1_gm_median, label='gm', color='gray')
     # ax2.plot(range(slice_num), T1_csf_mean, label = 'csf', color = 'cyan')
@@ -261,7 +267,7 @@ def analyze(file_dir):
     ## Ktrans medians plot
     ax3.set_xlabel('Slice #')
     ax3.set_ylabel('Ktrans Medians')
-    ax3.set_ylim([0, 0.017])
+    ax3.set_ylim([0, 0.005])
     ax3.plot(range(slice_num), Ktrans_wm_median, label='wm', color='pink')
     ax3.plot(range(slice_num), Ktrans_gm_median, label='gm', color='gray')
     # ax3.plot(range(slice_num), Ktrans_csf_mean, label = 'csf', color = 'cyan')
@@ -269,12 +275,12 @@ def analyze(file_dir):
     ax3.legend()
 
     # Save graphs
-    path2 = file_dir + '/T1_Ktrans_analysis.png'
+    path2 = 'figures/' + prefix + '_desc-analysis.png'
     plt.savefig(path2, bbox_inches='tight')
     
     # Zeros
     # fig2, ((ax4, ax5)) = plt.subplots(2, 1, figsize=(20,6))
-    fig2, ax5 = plt.subplots(1, 1, figsize=(20,6))
+    fig2, ax5 = plt.subplots(1, 1, figsize=(10,6))
 
     ## T1 zeros plot
     # ax4.set_xlabel('Slice #')
@@ -300,8 +306,8 @@ def analyze(file_dir):
     for i in range(slice_num):
         Ktrans_wm_slicevoxels.append(len(Ktrans_wm_data[:, :, i][wm_data[:, :, i] > 0]))
         Ktrans_gm_slicevoxels.append(len(Ktrans_gm_data[:, :, i][gm_data[:, :, i] > 0]))
-    Ktrans_wm_zero_avg = average(Ktrans_wm_zeros, weights=Ktrans_wm_slicevoxels)
-    Ktrans_gm_zero_avg = average(Ktrans_gm_zeros, weights=Ktrans_gm_slicevoxels)
+    Ktrans_wm_zero_avg = np.average(Ktrans_wm_zeros, weights=Ktrans_wm_slicevoxels)
+    Ktrans_gm_zero_avg = np.average(Ktrans_gm_zeros, weights=Ktrans_gm_slicevoxels)
     min_ylim, max_ylim = ax5.get_ylim()
     min_xlim, max_xlim = ax5.get_xlim()
     ax5.hlines(Ktrans_wm_zero_avg, min_xlim, max_xlim, color='pink', linestyle='dashed')
@@ -312,8 +318,12 @@ def analyze(file_dir):
     ax5.grid()
     ax5.legend()
 
-    plt.savefig(file_dir + '/T1_Ktrans_zeros.png', bbox_inches='tight')
+    plt.savefig('figures/' + prefix + '_desc-zeros.png', bbox_inches='tight')
+    # print(any(np.array(Ktrans_wm_zeros) >= 70) or any(np.array(Ktrans_gm_zeros) >= 70))
+    # print((Ktrans_wm_zero_avg >= 55) or (Ktrans_gm_zero_avg >= 55))
 
 
-dir = Path(sys.argv[1])     # takes timepoint directory as argument
-analyze(str(dir))
+tp_dir = Path(sys.argv[1])     # takes timepoint directory as argument
+# output_dir = Path(sys.argv[2]) # takes output directory as argument
+prefix = sys.argv[2]           # takes prefix as argument
+analyze(str(tp_dir))
diff --git a/ktrans_report.py b/ktrans_report.py
new file mode 100644
index 0000000..1c140cc
--- /dev/null
+++ b/ktrans_report.py
@@ -0,0 +1,182 @@
+import json
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import matplotlib.gridspec as gridspec
+from matplotlib.pyplot import subplots_adjust
+import numpy as np
+import nibabel as nib
+from pathlib import Path
+import sys
+import cairosvg
+import imageio.v2 as imageio
+
+
+dir = Path(sys.argv[1])
+# output_dir = Path(sys.argv[2])
+prefix = sys.argv[2]
+# try:
+#     cmap = str(Path(sys.argv[2]))
+# except:
+#     cmap = 'gnuplot'
+
+
+# AIF_overlay = mpimg.imread('figures/AIF_overlay.svg')
+curves = []
+svg_path = f'figures/{prefix}_desc-AIF_overlay.svg'
+png_bytes = cairosvg.svg2png(file_obj=open(svg_path, "rb"))
+png_np_array = imageio.imread(png_bytes, format='png')
+curves.append(png_np_array)
+svg_path = f'figures/{prefix}_desc-AIF_curve.svg'
+png_bytes = cairosvg.svg2png(file_obj=open(svg_path, "rb"))
+png_np_array = imageio.imread(png_bytes, format='png')
+curves.append(png_np_array)
+curves.append(mpimg.imread('figures/dceAIF_fitting.png'))
+curves.append(mpimg.imread('figures/dce_timecurves.png'))
+ktrans = nib.load('dce/' + prefix + '_Ktrans.nii')
+analysis = mpimg.imread('figures/' + prefix + '_desc-analysis.png')
+zeros = mpimg.imread('figures/' + prefix + '_desc-zeros.png')
+aif_curve = mpimg.imread('figures/dceAIF_fitting.png')
+timecurves = mpimg.imread('figures/dce_timecurves.png')
+plots = []
+plots.append(mpimg.imread('figures/' + prefix + '_desc-zeros.png'))
+if Path('figures/displacements.png').exists():
+    plots.append(mpimg.imread('figures/displacements.png'))
+try:
+    json_file = open(f'{dir}/dce/{prefix}_DCE.json')
+    json_dict = json.load(json_file)
+    site = json_dict['InstitutionName']
+    date = json_dict['AcquisitionDateTime'].split('T')[0]
+    json_file.close()
+except:
+    site = "no json"
+    date = "no json"
+
+dim = {0,1,2}
+ktrans_data = ktrans.get_fdata()
+ktrans_shape = ktrans_data.shape
+slice_num = min(ktrans_shape[0], ktrans_shape[1], ktrans_shape[2])
+slice_loc = ktrans_shape.index(slice_num)
+ktrans_data = np.reshape(ktrans_data, (ktrans_shape[min(dim-set([slice_loc]))], ktrans_shape[max(dim-set([slice_loc]))], slice_num))
+slices = []
+for i in range(slice_num):
+    slices.append(ktrans_data[:,:,i].T)
+
+fig, axs = plt.subplots(4, 1, figsize=(8.5,11), dpi=300)
+subject = str(dir).split('/')[-2]
+axs[0].set_title(dir, y=1.02, fontsize='small')
+plt.suptitle(str(subject), fontsize='large', y=1.07)  # Increase the y value to add extra space at the top
+# put text below the title
+axs[0].text(0, 1.2, f'Site: {site}', fontsize='small')
+axs[0].text(0, 1.1, f'Scan Date: {date}', fontsize='small')
+axs[0].axis('off')
+axs[1].axis('off')
+axs[2].axis('off')
+axs[3].axis('off')
+axs[2].imshow(analysis)
+x = axs[3].imshow(zeros, cmap='gnuplot', vmin=0, vmax=.009)
+
+gspec = axs[0].get_subplotspec().get_gridspec()
+gridspec = axs[1].get_subplotspec().get_gridspec()
+gridspec2 = axs[3].get_subplotspec().get_gridspec()
+subfig1 = fig.add_subfigure(gspec[3,:])
+subfig2 = fig.add_subfigure(gridspec[1,:])
+subfig3 = fig.add_subfigure(gridspec2[0,:])
+plot_rows = subfig1.subplots(1,2)
+if slice_num > 8:
+    row = subfig2.subplots(2,int(slice_num/2))
+else:
+    row = subfig2.subplots(1,slice_num)
+curve_rows = subfig3.subplots(1,4)
+
+# cmap = 'gnuplot'
+i = 0
+for ax in curve_rows:
+    ax.axis('off')
+    if i > 1:
+        ax.imshow(curves[i], aspect='auto')
+    else:
+        ax.imshow(curves[i])
+    i+=1
+
+i = 0
+for ax in row.flat:
+    ax.axis('off')
+    ax.set_xlim(30, 290)
+    ax.set_ylim(20, 310)
+    # ax.pcolormesh(slices[i], cmap=cmap, vmin=0, vmax=.009)
+    x=ax.imshow(slices[i], cmap='gnuplot', vmin=0, vmax=0.009)
+    i+=1
+
+i = 0
+for ax in plot_rows:
+    ax.axis('off')
+    ax.imshow(plots[i])
+    i += 1
+
+# fig.tight_layout(pad=-.7)
+subplots_adjust(top=0.99, bottom=0.0, left=-0.0, right=1.0, hspace=0, wspace=-.0)
+# cax = fig.add_axes([0.0, 0.23, 1, .02])
+# colorbar = fig.colorbar(x, orientation='horizontal', label='Ktrans (/min)', pad=.02, aspect = 60)
+cax = fig.add_axes([1,0.5,.01,.246])
+colorbar = fig.colorbar(x, cax=cax, orientation='vertical', label='Ktrans (/min)', pad=.02)
+colorbar.set_label('Ktrans (10^-3/min)', labelpad=-15, fontsize = 'xx-small', color = 'white')
+colorbar.ax.yaxis.set_ticks(np.arange(0, 10, 1))
+colorbar.ax.set_yticklabels(np.arange(0, 10, 1), fontsize='xx-small')
+
+plt.savefig('reports/' + prefix + '_desc-report.png', bbox_inches='tight')
+
+
+## REGISTRATION QC
+diff = nib.load('anat/' + prefix + '_space-DCEref_label-WMQC.nii.gz')
+diff_data = diff.get_fdata()
+# diff_shape = diff_data.shape
+# diff_data = np.reshape(diff_data, (ktrans_shape[min(dim-set([slice_loc]))], ktrans_shape[max(dim-set([slice_loc]))], slice_num))
+
+fig2, ax2 = plt.subplots(figsize=(20, 6))
+ax2.axis('off')
+gridspec_reg = ax2.get_subplotspec().get_gridspec()
+subfig_reg = fig2.add_subfigure(gridspec_reg[0,:])
+if slice_num > 8:
+    reg_rows = subfig_reg.subplots(2,int(slice_num/2))
+else:
+    reg_rows = subfig_reg.subplots(1,slice_num)
+i=0
+for ax in reg_rows.flat:
+    ax.axis('off')
+    # ax.set_xlim(30, 290)
+    # ax.set_ylim(20, 310)
+    # ax.pcolormesh(slices[i], cmap=cmap, vmin=0, vmax=.009)
+    x=ax.imshow(diff_data[:,:,i].T, cmap='gray', origin='lower', vmin=0, vmax=500)
+    i+=1
+fig2.tight_layout(pad=-2)
+# ax.imshow(diff_data[:,:,7], cmap=cmap)
+# ax.axis('off')
+
+plt.savefig('figures/' + prefix + '_desc-WMQC_DCE.png', bbox_inches='tight')
+
+diff = nib.load('anat/' + prefix + '_space-DCEref_label-GMQC.nii.gz')
+diff_data = diff.get_fdata()
+# diff_shape = diff_data.shape
+# diff_data = np.reshape(diff_data, (ktrans_shape[min(dim-set([slice_loc]))], ktrans_shape[max(dim-set([slice_loc]))], slice_num))
+
+fig3, ax3 = plt.subplots(figsize=(20, 6))
+ax3.axis('off')
+gridspec_reg = ax3.get_subplotspec().get_gridspec()
+subfig_reg = fig3.add_subfigure(gridspec_reg[0,:])
+if slice_num > 8:
+    reg_rows = subfig_reg.subplots(2,int(slice_num/2))
+else:
+    reg_rows = subfig_reg.subplots(1,slice_num)
+i=0
+for ax in reg_rows.flat:
+    ax.axis('off')
+    # ax.set_xlim(30, 290)
+    # ax.set_ylim(20, 310)
+    # ax.pcolormesh(slices[i], cmap=cmap, vmin=0, vmax=.009)
+    x=ax.imshow(diff_data[:,:,i].T, cmap='gray', origin='lower', vmin=0, vmax=500)
+    i+=1
+fig3.tight_layout(pad=-2)
+# ax.imshow(diff_data[:,:,7], cmap=cmap)
+# ax.axis('off')
+
+plt.savefig('figures/' + prefix + '_desc-GMQC_DCE.png', bbox_inches='tight')
diff --git a/max_disp.py b/max_disp.py
index 72ded22..a3308ea 100644
--- a/max_disp.py
+++ b/max_disp.py
@@ -1,10 +1,126 @@
 import sys
+import matplotlib.pyplot as plt
+import nibabel as nib
 import numpy as np
+from math import sqrt
 from numpy.core.multiarray import unravel_index
+# from numpy.lib.function_base import corrcoef
+from sklearn.linear_model import LinearRegression
+from statistics import mean
+
 
 dir = sys.argv[1]
-data = np.loadtxt(dir + "/DCE_mc.nii.par", dtype = float)
-data[:,0:3] = data[:,0:3]*50
-max_i = data.argmax()
-i = unravel_index(max_i, data.shape)
-print("Max displacement of " + str(data[i]) + " mm at time slice " + str(i[0] + 1) + "/64, parameter " + str(i[1]))
+prefix = sys.argv[2]
+mc_params = np.loadtxt(dir + "/" + prefix + "_desc-hmc_DCE.nii.par", dtype=float)
+mc_params[:,0:3] = mc_params[:,0:3]*70
+max_i = np.abs(mc_params).argmax()
+max_disp_i = unravel_index(max_i, mc_params.shape)
+max_disp = str(mc_params[max_disp_i])
+param_num = max_disp_i[1]
+# get max displacement of 3d transformation
+vector_max_disp = np.zeros(64)
+# for i in range(64):
+#     vector_max_disp[i] = np.linalg.norm(mc_params[i], axis=0)
+# vector_max_disp = np.abs(vector_max_disp).max()
+for i in range(len(mc_params[:,0])):
+    vector_max_disp = sqrt((mc_params[i,3]+mc_params[i,0])**2 + (mc_params[i,4]+mc_params[i,1])**2 + (mc_params[i,5]+mc_params[i,2])**2)
+vector_max_disp = np.abs(vector_max_disp).max()
+vector_max_disp_i = np.abs(mc_params).argmax(axis=0)[param_num]+1
+
+if param_num < 4:
+    param_type = "rot_"
+else:
+    param_type = "trans_"
+    
+if param_num % 3 == 0:
+    param_type = param_type + 'x'
+elif param_num % 3 == 1:
+    param_type = param_type + 'y'
+elif param_num % 3 == 2:
+    param_type = param_type + 'z'
+
+print("Max displacement: " + str(vector_max_disp) + "mm at frame " + str(vector_max_disp_i) + "/64")
+
+fig, ax = plt.subplots(figsize=(10, 6))
+colors = ['k', 'b', 'g', 'm', 'y', 'c']
+labels = ['rot_x', 'rot_y', 'rot_z', 'trans_x', 'trans_y', 'trans_z']
+for i in range(len(mc_params[0,:])):
+    # label = 'param ' + str(i)
+    ax.plot(range(len(mc_params[:,i])), mc_params[:,i], label=labels[i], color=colors[i])
+
+plt.legend()
+plt.grid()
+plt.ylabel("Displacement (mm)")
+plt.xlabel("")
+plt.ylim([-2.5, 2.5])
+plt.text(len(mc_params[:,0])/3, 2, "Max vector displacement: " + str(round(vector_max_disp,4)) + "mm")
+plt.text(len(mc_params[:,0])/3, 1.8, "Frame: " + str(vector_max_disp_i) + "/64")
+# plt.text(len(mc_params[:,0])/2, 1.8, "param: " + param_type)
+path = dir + '/../figures/displacements.svg'
+plt.savefig(path, bbox_inches='tight')
+
+# save as png too
+plt.savefig(dir + '/../figures/displacements.png', bbox_inches='tight')
+
+## PART 2 - Correlation
+# dce = nib.load(dir + '/DCE.nii')
+# dce_mc = nib.load(dir + '/DCE_mc.nii.gz')
+# ktrans = nib.load(dir + '/dce_patlak_fit_Ktrans.nii')
+# dce_data = dce.get_fdata()
+# dce_mc_data = dce_mc.get_fdata()
+# ktrans_data = ktrans.get_fdata()
+
+# path2 = dir + '/bozo.png'
+
+# mc_params (disps): 64x6
+# dce_mc: 320x320x14x64
+# dce_mc_data = np.reshape(dce_mc_data, (-1,64))
+# bozo = corrcoef(dce_mc_data[0,0,0,:], mc_params[:,0])[0,1]
+
+# Correlation Coefficient Image
+# corr_dce = np.zeros((320,320,14,6))
+# corr_dce_mc = np.zeros((320,320,14,6))
+# for p in range(6):
+#     for i in range(dce_mc_data.shape[0]):
+#         for j in range(dce_mc_data.shape[1]):
+#             for k in range(dce_mc_data.shape[2]):
+#                 corr_dce[i, j, k, p] = corrcoef(dce_data[i,j,k,:], mc_params[:, p])[0,1]
+#                 corr_dce_mc[i, j, k, p] = corrcoef(dce_mc_data[i ,j, k, :], mc_params[:, p])[0,1]
+# final_dce = nib.Nifti1Image(corr_dce, dce.affine)
+# final_dce_mc = nib.Nifti1Image(corr_dce_mc, dce_mc.affine)
+# path3 = dir + '/dce_corrcoef.nii'
+# path4 = dir + '/dce_mc_corrcoef.nii'
+# nib.save(final_dce, path3)
+# nib.save(final_dce_mc, path4)
+
+# testing one voxel
+# model = LinearRegression().fit(mc_params, dce_mc_data[87, 251, 7, :])
+# r_sq = model.score(mc_params, dce_mc_data[87, 251, 7, :])
+# print(r_sq)
+# print(model.coef_)
+# pred = model.predict(mc_params)
+# residuals = (dce_mc_data[87, 251, 7, :] - pred)
+# print(residuals)
+
+# Residuals image
+# dce_mc_residuals = np.zeros((320,320,14,64))
+# for x in range(dce_mc_data.shape[0]):
+#     for y in range(dce_mc_data.shape[1]):
+#         for z in range(dce_mc_data.shape[2]):
+#             model = LinearRegression(positive=True).fit(mc_params, dce_mc_data[x, y, z, :])
+#             pred = model.predict(mc_params)
+#             residuals = dce_mc_data[x, y, z, :] - pred
+#             dce_mc_residuals[x, y, z, :] = residuals + 250
+
+
+# final_dce_mc_residuals = nib.Nifti1Image(dce_mc_residuals, dce_mc.affine)
+# path5 = dir + '/dce_mc_residuals_linear_150.nii'
+# nib.save(final_dce_mc_residuals, path5)
+#
+# fig2, ax2 = plt.subplots(figsize=(10,6))
+# ax2.plot(range(64), mc_params[:, 0] / mean(mc_params[:, 0]), label='param 0')
+# ax2.plot(range(64), dce_mc_data[87, 251, 7, :] / mean(dce_mc_data[87, 251, 7, :]), label = 'MC SI')
+# ax2.plot(range(64), residuals, label='residual')
+
+# plt.legend()
+# plt.savefig(path2, bbox_inches='tight')
diff --git a/overview.png b/overview.png
new file mode 100644
index 0000000..145fe0b
Binary files /dev/null and b/overview.png differ
diff --git a/population_report.py b/population_report.py
new file mode 100644
index 0000000..d6cb881
--- /dev/null
+++ b/population_report.py
@@ -0,0 +1,2738 @@
+import jinja2
+import json
+import os
+import matplotlib.pyplot as plt
+import nibabel as nib
+import numpy as np
+import pandas as pd
+import datetime
+import subprocess
+import threading
+import time
+from sys import argv
+from concurrent.futures import ThreadPoolExecutor
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+from concurrent.futures import as_completed
+from utils.constants import KTRANS_MIN_THRESHOLD
+
+dir = argv[1]
+try:
+    output_dir = argv[2]
+except:
+    output_dir = ""
+
+dceprep_dir = argv[1] + "/dceprep"
+
+try:
+    ROCKETSHIP_dir = argv[3]
+except IndexError:
+    ROCKETSHIP_dir = argv[2]
+    output_dir = ""
+
+if output_dir != "":
+    output_dir = "-" + output_dir
+    dceprep_dir = dceprep_dir + output_dir
+
+# Load MRI population data dict with keys as subject IDs and values as gm and wm data
+population_data = {}
+population_data_exclude = {}
+population_data_failed = {}
+population_data_missing = {}
+# list directories in dir
+if not os.path.isdir(dceprep_dir):
+    print(f"{dceprep_dir} does not exist, trying current working directory")
+    dir_list = os.listdir(os.getcwd())
+else:
+    dir_list = os.listdir(dceprep_dir)
+# filter out non-directories
+subjects = [subject for subject in dir_list if os.path.isdir(os.path.join(dceprep_dir, subject)) and not subject.startswith("figures") and not subject.startswith("logs")]
+subjects.sort()
+# use text file list for subjects, format is subject date timepoint
+# get list of subjects
+# subjects = [f"sub-{line.split(' ')[0]}" for line in open(os.path.join(dir, "../code/CBF_list.txt"), "r").readlines()]
+# get list of timepoints
+# timepoints = [line.split(" ")[2][:-1] for line in open(os.path.join(dir, "../code/CBF_list.txt"), "r").readlines()]
+# print(timepoints)
+# go into each subject directory and count number of successful_timepoints
+# for subject_id in subjects:
+#     # list _timepoint directories in subject directory
+#     for timepoint in os.listdir(os.path.join(dir, subject_id)):
+#         if timepoint.endswith("_timepoint"):
+#             successful_timepoints.append(subject_id + '/' + timepoint)
+manual_aif_status = "AUTO"
+# read log file for command used to run dceprep
+# log is preprocessing_log_{date}.txt, use latest log
+logs = os.listdir(os.path.join(dir, "logs"))
+logs = [log for log in logs if log.startswith("preprocessing_log")]
+logs.sort()
+log = logs[-1]
+log = os.path.join(dir, "logs", log)
+command = ""
+with open(log, "r") as f:
+    for line in f:
+        if "Command: " in line:
+            command = line
+            break
+use_manual_aif = False
+if "-A M" in command or "-A T" in command:
+    use_manual_aif = True
+
+# Outlier and tracking lists
+Ktrans_wm_outliers, Ktrans_gm_outliers = [], []
+whole_hippo_outliers, whole_phg_outliers, whole_putamen_outliers = [], [], []
+whole_pallidum_outliers, whole_thalamus_outliers, whole_caudate_outliers = [], [], []
+whole_amygdala_outliers, whole_entorhinal_cortex_outliers = [], []
+whole_fusiform_gyrus_cortex_outliers, whole_fusiform_gyrus_WM_outliers = [], []
+whole_insula_WM_outliers, whole_superior_temporal_cortex_outliers = [], []
+whole_inferior_temporal_cortex_outliers, whole_posterior_cingulate_cortex_outliers = [], []
+whole_medial_temporal_cortex_outliers = []
+
+wm_outliers_exclude, gm_outliers_exclude = [], []
+whole_hippo_outliers_exclude, whole_phg_outliers_exclude, whole_putamen_outliers_exclude = [], [], []
+whole_pallidum_outliers_exclude, whole_thalamus_outliers_exclude, whole_caudate_outliers_exclude = [], [], []
+whole_amygdala_outliers_exclude, whole_entorhinal_cortex_outliers_exclude = [], []
+whole_fusiform_gyrus_cortex_outliers_exclude, whole_fusiform_gyrus_WM_outliers_exclude = [], []
+whole_insula_WM_outliers_exclude, whole_superior_temporal_cortex_outliers_exclude = [], []
+whole_inferior_temporal_cortex_outliers_exclude, whole_posterior_cingulate_cortex_outliers_exclude = [], []
+whole_medial_temporal_cortex_outliers_exclude = []
+
+total_timepoints, successful_timepoints = [], []
+popAIF_curves, aif_curves = [], []
+def get_case_stats(subject_id, timepoint):
+        wmparc_failed = False
+        stats_failed = False
+        missing = False
+        AIFitness = aif_fitted_r2 = max_disp = T1_wm_median = T1_wm_std = T1_gm_median = T1_gm_std = Ktrans_wm_mean = Ktrans_wm_median = Ktrans_wm_std = Ktrans_gm_mean = Ktrans_gm_median = Ktrans_gm_std = 0
+        hippo_vol = phg_vol = putamen_vol = pallidum_vol = thalamus_vol = caudate_vol = amygdala_vol = -1
+        entorhinal_cortex_vol = fusiform_gyrus_cortex_vol = fusiform_gyrus_wm_vol = insula_wm_vol = -1
+        superior_temporal_cortex_vol = inferior_temporal_cortex_vol = posterior_cingulate_cortex_vol = medial_temporal_cortex_vol = -1
+        Ktrans_Hippo_median = Ktrans_PhG_median = Ktrans_Putamen_median = Ktrans_Pallidum_median = -1
+        Ktrans_Thalamus_median = Ktrans_Caudate_median = Ktrans_Amygdala_median = Ktrans_Entorhinal_cortex_median = -1
+        Ktrans_Fusiform_gyrus_cortex_median = Ktrans_Fusiform_gyrus_WM_median = Ktrans_Insula_WM_median = -1
+        Ktrans_Superior_temporal_cortex_median = Ktrans_Inferior_temporal_cortex_median = Ktrans_Posterior_cingulate_cortex_median = Ktrans_Medial_temporal_cortex_median = -1
+        Vp_Hippo_median = Vp_PhG_median = Vp_Putamen_median = Vp_Pallidum_median = Vp_Thalamus_median = -1
+        Vp_Caudate_median = Vp_Amygdala_median = Vp_Entorhinal_cortex_median = Vp_Fusiform_gyrus_cortex_median = -1
+        Vp_Fusiform_gyrus_WM_median = Vp_Insula_WM_median = Vp_Superior_temporal_cortex_median = Vp_Inferior_temporal_cortex_median = -1
+        Vp_Posterior_cingulate_cortex_median = Vp_Medial_temporal_cortex_median = SNR = -1
+        bankssts_thickness_avg = bankssts_thickness_std = caudalanteriorcingulate_thickness_avg = caudalanteriorcingulate_thickness_std = -1
+        caudalmiddlefrontal_thickness_avg = caudalmiddlefrontal_thickness_std = cuneus_thickness_avg = cuneus_thickness_std = -1
+        entorhinal_thickness_avg = entorhinal_thickness_std = fusiform_thickness_avg = fusiform_thickness_std = -1
+        inferiorparietal_thickness_avg = inferiorparietal_thickness_std = inferiortemporal_thickness_avg = inferiortemporal_thickness_std = -1
+        insula_thickness_avg = insula_thickness_std = isthmuscingulate_thickness_avg = isthmuscingulate_thickness_std = -1
+        lateraloccipital_thickness_avg = lateraloccipital_thickness_std = lateralorbitofrontal_thickness_avg = lateralorbitofrontal_thickness_std = -1
+        lingual_thickness_avg = lingual_thickness_std = medialorbitofrontal_thickness_avg = medialorbitofrontal_thickness_std = -1
+        middletemporal_thickness_avg = middletemporal_thickness_std = parahippocampal_thickness_avg = parahippocampal_thickness_std = -1
+        paracentral_thickness_avg = paracentral_thickness_std = parsopercularis_thickness_avg = parsopercularis_thickness_std = -1
+        parsorbitalis_thickness_avg = parsorbitalis_thickness_std = parstriangularis_thickness_avg = parstriangularis_thickness_std = -1
+        pericalcarine_thickness_avg = pericalcarine_thickness_std = postcentral_thickness_avg = postcentral_thickness_std = -1
+        posteriorcingulate_thickness_avg = posteriorcingulate_thickness_std = precentral_thickness_avg = precentral_thickness_std = -1
+        precuneus_thickness_avg = precuneus_thickness_std = rostralanteriorcingulate_thickness_avg = rostralanteriorcingulate_thickness_std = -1
+        rostralmiddlefrontal_thickness_avg = rostralmiddlefrontal_thickness_std = superiorfrontal_thickness_avg = superiorfrontal_thickness_std = -1
+        superiorparietal_thickness_avg = superiorparietal_thickness_std = superiortemporal_thickness_avg = superiortemporal_thickness_std = -1
+        frontalpole_thickness_avg = frontalpole_thickness_std = supramarginal_thickness_avg = supramarginal_thickness_std = -1
+        temporalpole_thickness_avg = temporalpole_thickness_std = transversetemporal_thickness_avg = transversetemporal_thickness_std = -1
+
+        if timepoint.startswith("ses-"):
+            total_timepoints.append(subject_id + '/' + timepoint)
+            # Check for missing data in rawdata folder
+            prefix = f"{subject_id}_{timepoint}"
+            rawdata_dir = os.path.join(dir, "../rawdata", subject_id, timepoint)
+            missing_files = []
+            # Check for anat/prefix_T1w.nii.gz
+            t1w_path = os.path.join(rawdata_dir, f"anat/{prefix}_T1w.nii.gz")
+            if not os.path.exists(t1w_path):
+                missing_files.append("anat/" + os.path.basename(t1w_path))
+            # Check for at least one anat/prefix_*_VFA.nii.gz
+            anat_dir = os.path.join(rawdata_dir, "anat")
+            vfa_files = []
+            if os.path.isdir(anat_dir):
+                vfa_files = [f for f in os.listdir(anat_dir) if f.startswith(prefix) and "_VFA" in f and f.endswith(".nii.gz")]
+            if len(vfa_files) == 0:
+                missing_files.append("anat/*_VFA.nii.gz")
+            # Check for dce/prefix_DCE.nii.gz
+            dce_path = os.path.join(rawdata_dir, f"dce/{prefix}_DCE.nii.gz")
+            if not os.path.exists(dce_path):
+                missing_files.append("dce/" + os.path.basename(dce_path))
+            if missing_files:
+                print(f"Missing rawdata for {subject_id} {timepoint}: {', '.join(missing_files)}")
+                population_data_missing[subject_id + "_" + timepoint] = {"Missing_files": missing_files}
+                missing = True
+                return
+            else:
+                missing = False
+            # read AIF curve by applying aif.nii to dce.nii
+            try:
+                dce = os.path.join(dceprep_dir, subject_id, timepoint, f"dce/{subject_id}_{timepoint}_desc-bfcz_DCE.nii.gz")
+                aif = os.path.join(dceprep_dir, subject_id, timepoint, f"dce/{subject_id}_{timepoint}_desc-AIF_T1map.nii.gz")
+                if os.path.exists(dce) and os.path.exists(aif):
+                    # load files
+                    dce_img = nib.load(dce)
+                    aif_img = nib.load(aif)
+
+                    # get data from file
+                    aif = aif_img.get_fdata()
+                    dce = dce_img.get_fdata()
+
+                    # binarize aif
+                    aif = aif > 400
+
+                    # get curve from masked dce
+                    aif = aif.reshape(aif.shape[0], aif.shape[1], aif.shape[2], 1)
+                    roi_ = dce * aif
+                    num = np.sum(roi_, axis = (0, 1, 2), keepdims=False)
+                    den = np.sum(aif, axis = (0, 1, 2), keepdims=False)
+
+                    # normalize to baseline
+                    intensities = num/(den+1e-8)
+                    intensities = np.asarray(intensities)
+                    intensities = intensities/intensities[0]
+                    if intensities[0] != 1:
+                        print("error")
+                    # if intensities[1] < 3 and intensities[2] < 3:
+                    #     print(file + " has a weak AIF curve with " + str(intensities[1]) + " and " + str(intensities[2]))
+                    # if intensities[2] > intensities[1] or intensities[3] > intensities[2]+.5:
+                    #     print(file + " has a delayed injection with " + str(intensities[1]) + " and " + str(intensities[2]) + " and " + str(intensities[3]))
+                    # if any(intensities[10:30] < 2):
+                    #     print(subject_id, timepoint, "has an intensity < 2")
+                    # line up curve peaks
+                    max_index = np.argmax(intensities)
+                    # intensities = np.roll(intensities, -max_index+2)
+                    if intensities.shape[0] < 40:
+                        mean_last_7 = np.mean(intensities[-7:])
+                        intensities = np.pad(intensities, (0, 40-intensities.shape[0]), 'constant', constant_values=(mean_last_7))
+                    aif_curves.append(intensities[0:40])
+                    intensities = np.roll(intensities, -max_index)
+                    if intensities.shape[0] == 64:
+                        # make last five values 0 then roll back
+                        intensities[-5:] = 1
+                        intensities = np.roll(intensities, 5)
+                        if not np.isnan(intensities).any():
+                            popAIF_curves.append(intensities)
+                else:
+                    print("DCE or AIF file does not exist for", subject_id, timepoint)
+                    return
+            except Exception as e:
+                print("Error reading DCE or AIF for", subject_id, timepoint)
+                print(e)
+                return
+
+            # if use manual AIF and file exists, mark as manual
+            manual_aif_path = os.path.join(dceprep_dir, subject_id, timepoint, f"dce/{subject_id}_{timepoint}_desc-AIF_mask.nii.gz")
+            if use_manual_aif and os.path.isfile(manual_aif_path):
+                manual_aif_status = "MANUAL"
+            elif not use_manual_aif and os.path.isfile(manual_aif_path):
+                manual_aif_status = "OMITTED"
+            else:
+                manual_aif_status = "AUTO"
+            # get fields we want from json
+            json_file = os.path.join(dir, "../rawdata", subject_id, timepoint, f"dce/{subject_id}_{timepoint}_DCE.json")
+            try:
+                with open(json_file, 'r') as f:
+                    data = json.load(f)
+                    manufacturer = data.get("Manufacturer", "json field error")
+                    field_strength = data.get("MagneticFieldStrength", "json field error")
+                    machine = data.get("ManufacturersModelName", "json field error")
+                    institution = data.get("InstitutionName", "json field error")
+                    date = data.get("AcquisitionDateTime", "json field error").split("T")[0]
+                    if date != "json field error":
+                        date = datetime.datetime.strptime(date, "%Y-%m-%d").strftime("%m/%d/%Y")
+                        date = datetime.datetime.strptime(date, "%m/%d/%Y")
+                    sex = data.get("PatientSex", "json field error")
+                    age = data.get("PatientAge", "json field error")
+                    if "ReceiveCoilName" in data:
+                        coil = data.get("ReceiveCoilName", "json field error")
+                    else:
+                        coil = data.get("CoilString", "json field error")
+                    scan_options = data.get("ScanOptions", "json field error")
+                    TE = data.get("EchoTime", "json field error")
+                    # flip_angle = data.get("FlipAngle", "json field error")
+                    # if "RepetitionTimeExcitation" in data:
+                    #     # TR = data.get("RepetitionTimeExcitation", "json field error")
+                    #     time_resolution = data.get("RepetitionTime", "json field error")
+                    # else:
+                    #     # TR = data.get("RepetitionTime", "json field error")
+                    #     time_resolution = "not in header"
+            except Exception as e:
+                print("Error reading " + json_file)
+                print(e)
+                manufacturer = field_strength = machine = institution = date = sex = age = coil = scan_options = TE = flip_angle = TR = time_resolution = "json read error"
+
+            if not missing:
+                # read wm and gm data from html file
+                filename = os.path.join(dceprep_dir, subject_id, timepoint, f"reports/{subject_id}_{timepoint}_desc-casereport.html")
+                if os.path.exists(filename):
+                    try:
+                        with open(filename, "r") as f:
+                            lines = f.readlines()
+                            for i, line in enumerate(lines):
+                                if "T1 wm median:" in line:
+                                    T1_wm_median = float(line.split(":")[-1].strip()[:-5])
+                                if "T1 gm median:" in line:
+                                    T1_gm_median = float(line.split(":")[-1].strip()[:-5])
+                                if "Blood T1: " in line:
+                                    T1_blood = float(line.split(":")[-1].strip()[:-6])
+                                if "Median wm Ktrans" in line:
+                                    Ktrans_wm_median = float(line.split()[-1][:-5])
+                                if "Median gm Ktrans" in line:
+                                    Ktrans_gm_median = float(line.split()[-1][:-5])
+                                if "AIFitness" in line:
+                                    AIFitness = line.split(":")[-1].strip()[:-4]
+                                    AIFitness = float(AIFitness)
+                                    AIFitness = round(AIFitness, 4)
+                                if Ktrans_wm_median > 5:
+                                    if subject_id + "_" + timepoint not in Ktrans_wm_outliers:
+                                        Ktrans_wm_outliers.append(subject_id + "_" + timepoint)
+                                if Ktrans_gm_median > 5:
+                                    if subject_id + "_" + timepoint not in Ktrans_gm_outliers:
+                                        Ktrans_gm_outliers.append(subject_id + "_" + timepoint)
+                    except Exception as e:
+                        print("Error reading " + filename)
+                        print(e)
+                        T1_wm_median = T1_gm_median = T1_blood = Ktrans_wm_median = Ktrans_gm_median = AIFitness = -1
+                else:
+                    print(f"{filename} does not exist")
+                    T1_wm_median = T1_gm_median = T1_blood = Ktrans_wm_median = Ktrans_gm_median = AIFitness = -1
+
+                A_log = os.path.join(dceprep_dir, subject_id, timepoint, "dce/A_dceR1info.log")
+                try:
+                    with open(A_log, 'r') as f:
+                        for line in f:
+                            if "User selected TR (ms):" in line:
+                                TR = next(f).strip()
+                                TR = float(TR)
+                            if "User selected FA (degrees):" in line:
+                                flip_angle = next(f).strip()
+                                flip_angle = float(flip_angle)
+                            if "time points = " in line:
+                                n_reps = line.split(" ")[-1]
+                                n_reps = int(n_reps)
+                except Exception as e:
+                    print("Error reading " + A_log)
+                    print(e)
+                    TR = -1
+                    flip_angle = -1
+
+                # read lines after "AIF mmol:"
+                aif_mmol = []
+                B_log = os.path.join(dceprep_dir, subject_id, timepoint, "dce/B_dcefitted_R1info.log")
+                B_imported_log = os.path.join(dceprep_dir, subject_id, timepoint, "dce/B_dceimported_R1info.log")
+                try:
+                    if os.path.isfile(B_log):
+                        with open(B_log, 'r') as f:
+                            fitted_done = False
+                            for line in f:
+                                if "User selected time resolution (sec)" in line:
+                                    # take next line as time resolution
+                                    time_resolution = next(f).strip()
+                                    time_resolution = float(time_resolution)
+                                if "AIF mmol:" in line:
+                                    aif_mmol = f.readlines()
+                                    # find index of line after last numbers ("MAT results saved to: \n")
+                                    try:
+                                        lastline = aif_mmol.index("MAT results saved to: \n")
+                                    except ValueError:
+                                        lastline = aif_mmol.index("Finished B\n")
+                                    aif_mmol = aif_mmol[:lastline-1]
+                                    # remove \n and \t
+                                    aif_mmol = [i[2:-2] for i in aif_mmol]
+                                    # split each item into list
+                                    aif_mmol = [i.split() for i in aif_mmol]
+                                    # unite all lists into one
+                                    aif_mmol = [item for sublist in aif_mmol for item in sublist]
+                                    # convert to float
+                                    aif_mmol = [float(i) for i in aif_mmol]
+                                    # take last 33% of aif
+                                    aif_mmol = aif_mmol[int(len(aif_mmol) * 0.66):]
+                                    # convert to numpy array
+                                    aif_mmol = np.array(aif_mmol)
+                                    # take mean
+                                    aif_mmol = np.mean(aif_mmol)
+                                if "Adjusted R^2 of AIF fit = " in line and not fitted_done:
+                                    aif_fitted_r2 = line.split()[-1]
+                                    aif_fitted_r2 = float(aif_fitted_r2)
+                                    fitted_done = True
+                    elif os.path.isfile(B_imported_log):
+                        with open(B_imported_log, 'r') as f:
+                            for line in f:
+                                if "User selected time resolution (sec)" in line:
+                                    # take next line as time resolution
+                                    time_resolution = next(f).strip()
+                                    time_resolution = float(time_resolution)
+                                if "AIF mmol:" in line:
+                                    aif_mmol = f.readlines()
+                                    # find index of line after last numbers ("MAT results saved to: \n")
+                                    try:
+                                        lastline = aif_mmol.index("MAT results saved to: \n")
+                                    except ValueError:
+                                        lastline = aif_mmol.index("Finished B\n")
+                                    aif_mmol = aif_mmol[:lastline-1]
+                                    # remove \n and \t
+                                    aif_mmol = [i[2:-2] for i in aif_mmol]
+                                    # split each item into list
+                                    aif_mmol = [i.split() for i in aif_mmol]
+                                    # unite all lists into one
+                                    aif_mmol = [item for sublist in aif_mmol for item in sublist]
+                                    # convert to float
+                                    aif_mmol = [float(i) for i in aif_mmol]
+                                    # take last 33% of aif
+                                    aif_mmol = aif_mmol[int(len(aif_mmol) * 0.66):]
+                                    # convert to numpy array
+                                    aif_mmol = np.array(aif_mmol)
+                                    # take mean
+                                    aif_mmol = np.mean(aif_mmol)
+                except Exception as e:
+                    print("Error reading " + B_log)
+                    print(e)
+                    aif_mmol = -1
+                    aif_fitted_r2 = -1
+                # get max_disp from {prefix}_desc-hmcmaxdisp.txt
+                max_disp_path = os.path.join(dceprep_dir, subject_id, timepoint, f"dce/{subject_id}_{timepoint}_desc-hmc_maxdisp.txt")
+                if os.path.exists(max_disp_path):
+                    try:
+                        with open(max_disp_path, 'r') as f:
+                            for line in f:
+                                if "Max displacement" in line:
+                                    max_disp = line.split(":")[-1].strip()
+                                    max_disp = max_disp.split("mm")[0]
+                                    max_disp = float(max_disp)
+                                    break
+                    except Exception as e:
+                        print("Error reading " + max_disp_path)
+                        print(e)
+                        max_disp = -1
+                else:
+                    print(f"{max_disp_path} does not exist")
+                    max_disp = -1
+            else:
+                print(f"Skipping stats for {subject_id} {timepoint} due to missing rawdata")
+                return
+            # read ktrans map
+            try:
+                ktrans_map = os.path.join(dceprep_dir, subject_id, timepoint, f"dce/{subject_id}_{timepoint}_Ktrans.nii")
+                ktrans_map = nib.load(ktrans_map)
+                ktrans_map = ktrans_map.get_fdata()
+            except:
+                print("Error reading " + ktrans_map)
+                wmparc_failed = True
+                stats_failed = True
+                return
+
+            # read Vp map
+            try:
+                Vp_map = os.path.join(dceprep_dir, subject_id, timepoint, f"dce/{subject_id}_{timepoint}_Vp.nii")
+                Vp_map = nib.load(Vp_map)
+                Vp_map = Vp_map.get_fdata()
+            except:
+                print("Error reading " + Vp_map)
+                stats_failed = True
+                return
+            # Numbers are locations of regions in freesurfer wmparc.mgz
+            regions = {
+                "HIPPO": (17, 53),
+                "PHG": (1016, 2016),
+                "PUTAMEN": (12, 51),
+                "PALLIDUM": (13, 52),
+                "THALAMUS": (10, 49),
+                "CAUDATE": (11, 50),
+                "AMYGDALA": (18, 54),
+                "ENTORHINAL_CORTEX": (1006, 2006),
+                "FUSIFORM_GYRUS_CORTEX": (1007, 2007),
+                "FUSIFORM_GYRUS_WM": (3007, 4007),
+                "INSULA_WM": (3035, 4035),
+                "SUPERIOR_TEMPORAL_CORTEX": (1030, 2030),
+                "INFERIOR_TEMPORAL_CORTEX": (1009, 2009),
+                "POSTERIOR_CINGULATE_CORTEX": (1023, 2023)
+            }
+
+            # atlas file is where this script is located
+            # atlas = os.path.join(os.path.dirname(os.path.realpath(__file__)), "BN_Atlas_246_1mm.nii.gz")
+            # atlas = nib.load(atlas)
+            # atlas = atlas.get_fdata()
+            # atlas = ktrans_map_hippo
+            # atlas = atlas[:,110,:]
+            error = ""
+            try:
+                prefix = f"{subject_id}_{timepoint}"
+                wmparc_path = os.path.join(dceprep_dir, subject_id, timepoint, f"anat/{prefix}_space-DCEref_desc-wmparc.nii.gz")
+                if os.path.isfile(wmparc_path):
+                    wmparc = nib.load(wmparc_path)
+                    wmparc = wmparc.get_fdata()
+                else:
+                    print(f"{wmparc_path} does not exist")
+                    error = "anat/wmparc does not exist"
+                    wmparc_failed = True
+                    stats_failed = True
+                # read stats from tsv
+                freesurfer_path = os.path.join(dir, 'freesurfer', subject_id, timepoint, "stats")
+                if os.path.isfile(os.path.join(freesurfer_path, "wmparc.stats")) and os.path.isfile(os.path.join(freesurfer_path, "aseg.stats")) and os.path.isfile(os.path.join(freesurfer_path, "lh.aparc.stats")) and os.path.isfile(os.path.join(freesurfer_path, "rh.aparc.stats")):
+                    wmparc_stats = os.path.join(freesurfer_path, "wmparc.stats")
+                    aseg_stats = os.path.join(freesurfer_path, "aseg.stats")
+                    lh_aparc_stats = os.path.join(freesurfer_path, "lh.aparc.stats")
+                    rh_aparc_stats = os.path.join(freesurfer_path, "rh.aparc.stats")
+                    # fastsurfer = False
+                # elif os.path.isfile(os.path.join(freesurfer_path, "wmparc.DKTatlas.mapped.stats")):
+                #     # fastsurfer outputs
+                #     wmparc_stats = os.path.join(freesurfer_path, "wmparc.DKTatlas.mapped.stats")
+                #     aseg_stats = os.path.join(freesurfer_path, "aseg.stats")
+                #     lh_aparc_stats = os.path.join(freesurfer_path, "lh.aparc.DKTatlas.mapped.stats")
+                #     rh_aparc_stats = os.path.join(freesurfer_path, "rh.aparc.DKTatlas.mapped.stats")
+                #     fastsurfer = True
+                else:
+                    print("wmparc stats do not exist for", prefix)
+                    if error == "":
+                        error = "stats files are missing"
+                    wmparc_failed = True
+                    stats_failed = True
+                    # fastsurfer = False
+
+            except Exception as e:
+                print("Error reading freesurfer stats for", subject_id, timepoint)
+                print(e)
+                error = "stats error"
+                wmparc_failed = True
+                stats_failed = True
+            if not stats_failed:
+                with open(wmparc_stats, 'r') as f:
+                    lines = f.readlines()
+                    for i, line in enumerate(lines):
+                        if line.startswith("# ColHeaders"):
+                            break
+                    lines = lines[i:]
+                    # remove # from beginning of each line
+                    lines_split = [line.replace('#','').strip().split() for line in lines]
+
+                    df_wmparc = pd.DataFrame(lines_split)
+                    # Convert only numeric columns (skip first column and header row)
+                    # First, set column names from the first row (shifted)
+                    df_wmparc.columns = df_wmparc.iloc[0].shift(-1)
+                    df_wmparc = df_wmparc.drop(df_wmparc.index[0])
+                    # Identify numeric columns (skip 'StructName')
+                    numeric_cols = [col for col in df_wmparc.columns if col != 'StructName']
+                    df_wmparc[numeric_cols] = df_wmparc[numeric_cols].apply(pd.to_numeric)
+                    df_wmparc = df_wmparc.set_index(df_wmparc.iloc[:, 0])
+                    # drop first column
+                    df_wmparc = df_wmparc.drop(df_wmparc.columns[0], axis=1)
+                
+                # fraudsurfer thalamus name varies
+                right_thalamus = ''
+                left_thalamus = ''
+                with open(aseg_stats, 'r') as f:
+                    # find line starting with # ColHeaders, skip up to that line
+                    lines = f.readlines()
+                    for i, line in enumerate(lines):
+                        if line.startswith("# ColHeaders"):
+                            break
+                    lines = lines[i:]
+                    # remove # from beginning of each line
+                    lines_split = [line.replace('#','').strip().split() for line in lines]
+
+                    # find name of thalamus column labels
+                    left_thalamus = [line for line in lines if 'Left-Thalamus' in line]
+                    # split thalamus line and take element with 'Thalamus' in it
+                    left_thalamus = left_thalamus[0].split()
+                    left_thalamus = [col for col in left_thalamus if 'Left-Thalamus' in col][0]
+                    # left_thalamus = left_thalamus.split('-')[-1]
+
+                    right_thalamus = [line for line in lines if 'Right-Thalamus' in line]
+                    right_thalamus = right_thalamus[0].split()
+                    right_thalamus = [col for col in right_thalamus if 'Right-Thalamus' in col][0]
+                    # right_thalamus = right_thalamus.split('-')[-1]
+
+                    df_aseg = pd.DataFrame(lines_split)
+                    # Set column names from the first row (shifted)
+                    df_aseg.columns = df_aseg.iloc[0].shift(-1)
+                    df_aseg = df_aseg.drop(df_aseg.index[0])
+                    # Identify numeric columns (skip 'StructName')
+                    numeric_cols = [col for col in df_aseg.columns if col != 'StructName']
+                    df_aseg[numeric_cols] = df_aseg[numeric_cols].apply(pd.to_numeric)
+                    # df_aseg = df_aseg.set_index(df_aseg.iloc[:, 0])
+                    # drop first column
+                    df_aseg = df_aseg.drop(df_aseg.columns[0], axis=1)
+
+                with open(lh_aparc_stats, 'r') as f:
+                    lines = f.readlines()
+                    for i, line in enumerate(lines):
+                        if line.startswith("# ColHeaders"):
+                            break
+                    lines = lines[i:]
+                    # remove # from beginning of each line
+                    lines_split = [line.replace('#','').strip().split() for line in lines]
+
+                    df_lh_aparc = pd.DataFrame(lines_split)
+                    # Shift column names left by 1 to remove 'ColHeaders' and align 'StructName'
+                    df_lh_aparc.columns = df_lh_aparc.iloc[0].shift(-1)
+                    df_lh_aparc = df_lh_aparc.drop(df_lh_aparc.index[0])
+                    # Identify numeric columns (skip 'StructName')
+                    numeric_cols = [col for col in df_lh_aparc.columns if col != 'StructName']
+                    df_lh_aparc[numeric_cols] = df_lh_aparc[numeric_cols].apply(pd.to_numeric)
+                    # df_lh_aparc = df_lh_aparc.set_index(df_lh_aparc.iloc[:, 0])
+                
+                with open(rh_aparc_stats, 'r') as f:
+                    lines = f.readlines()
+                    for i, line in enumerate(lines):
+                        if line.startswith("# ColHeaders"):
+                            break
+                    lines = lines[i:]
+                    # remove # from beginning of each line
+                    lines_split = [line.replace('#','').strip().split() for line in lines]
+
+                    df_rh_aparc = pd.DataFrame(lines_split)
+                    # Shift column names left by 1 to remove 'ColHeaders' and align 'StructName'
+                    df_rh_aparc.columns = df_rh_aparc.iloc[0].shift(-1)
+                    df_rh_aparc = df_rh_aparc.drop(df_rh_aparc.index[0])
+                    # Identify numeric columns (skip 'StructName')
+                    numeric_cols = [col for col in df_rh_aparc.columns if col != 'StructName']
+                    df_rh_aparc[numeric_cols] = df_rh_aparc[numeric_cols].apply(pd.to_numeric)
+                    # df_rh_aparc = df_rh_aparc.set_index(df_rh_aparc.iloc[:, 0])
+                # assign regional volumes to variables
+                hippo_vol = float(df_aseg.loc[df_aseg['StructName'] == 'Left-Hippocampus', 'Volume_mm3'].values[0]) + float(df_aseg.loc[df_aseg['StructName'] == 'Right-Hippocampus', 'Volume_mm3'].values[0])
+                phg_vol = float(df_wmparc.loc[df_wmparc['StructName'] == 'wm-lh-parahippocampal', 'Volume_mm3'].values[0]) + float(df_wmparc.loc[df_wmparc['StructName'] == 'wm-rh-parahippocampal', 'Volume_mm3'].values[0])
+                putamen_vol = float(df_aseg.loc[df_aseg['StructName'] == 'Left-Putamen', 'Volume_mm3'].values[0]) + float(df_aseg.loc[df_aseg['StructName'] == 'Right-Putamen', 'Volume_mm3'].values[0])
+                pallidum_vol = float(df_aseg.loc[df_aseg['StructName'] == 'Left-Pallidum', 'Volume_mm3'].values[0]) + float(df_aseg.loc[df_aseg['StructName'] == 'Right-Pallidum', 'Volume_mm3'].values[0])
+                thalamus_vol = float(df_aseg.loc[df_aseg['StructName'] == left_thalamus, 'Volume_mm3'].values[0]) + float(df_aseg.loc[df_aseg['StructName'] == right_thalamus, 'Volume_mm3'].values[0])
+                caudate_vol = float(df_aseg.loc[df_aseg['StructName'] == 'Left-Caudate', 'Volume_mm3'].values[0]) + float(df_aseg.loc[df_aseg['StructName'] == 'Right-Caudate', 'Volume_mm3'].values[0])
+                amygdala_vol = float(df_aseg.loc[df_aseg['StructName'] == 'Left-Amygdala', 'Volume_mm3'].values[0]) + float(df_aseg.loc[df_aseg['StructName'] == 'Right-Amygdala', 'Volume_mm3'].values[0])
+                entorhinal_cortex_vol = float(df_lh_aparc.loc[df_lh_aparc['StructName'] == 'entorhinal', 'GrayVol'].values[0]) + float(df_rh_aparc.loc[df_rh_aparc['StructName'] == 'entorhinal', 'GrayVol'].values[0])
+                fusiform_gyrus_cortex_vol = float(df_lh_aparc.loc[df_lh_aparc['StructName'] == 'fusiform', 'GrayVol'].values[0]) + float(df_rh_aparc.loc[df_rh_aparc['StructName'] == 'fusiform', 'GrayVol'].values[0])
+                fusiform_gyrus_wm_vol = float(df_wmparc.loc[df_wmparc['StructName'] == 'wm-lh-fusiform', 'Volume_mm3'].values[0]) + float(df_wmparc.loc[df_wmparc['StructName'] == 'wm-rh-fusiform', 'Volume_mm3'].values[0])
+                insula_wm_vol = float(df_wmparc.loc[df_wmparc['StructName'] == 'wm-lh-insula', 'Volume_mm3'].values[0]) + float(df_wmparc.loc[df_wmparc['StructName'] == 'wm-rh-insula', 'Volume_mm3'].values[0])
+                superior_temporal_cortex_vol = float(df_lh_aparc.loc[df_lh_aparc['StructName'] == 'superiortemporal', 'GrayVol'].values[0]) + float(df_rh_aparc.loc[df_rh_aparc['StructName'] == 'superiortemporal', 'GrayVol'].values[0])
+                inferior_temporal_cortex_vol = float(df_lh_aparc.loc[df_lh_aparc['StructName'] == 'inferiortemporal', 'GrayVol'].values[0]) + float(df_rh_aparc.loc[df_rh_aparc['StructName'] == 'inferiortemporal', 'GrayVol'].values[0])
+                posterior_cingulate_cortex_vol = float(df_lh_aparc.loc[df_lh_aparc['StructName'] == 'posteriorcingulate', 'GrayVol'].values[0]) + float(df_rh_aparc.loc[df_rh_aparc['StructName'] == 'posteriorcingulate', 'GrayVol'].values[0])
+                medial_temporal_cortex_vol = hippo_vol + phg_vol + entorhinal_cortex_vol
+                # get all cortical thickness values from left and right aparc
+                cortical_thickness_lh = df_lh_aparc[['StructName', 'NumVert', 'ThickAvg', 'ThickStd']].values.tolist()
+                cortical_thickness_rh = df_rh_aparc[['StructName', 'NumVert', 'ThickAvg', 'ThickStd']].values.tolist()
+                # for each region, get whole-brain mean cortical thickness, weighted by number of vertices in region
+                cortical_thickness = {}
+                for lh, rh in zip(cortical_thickness_lh, cortical_thickness_rh):
+                    # print(lh, rh)
+                    if lh[0] == rh[0] and lh[0] not in cortical_thickness:
+                        region = lh[0]
+                        num_vert = int(lh[1]) + int(rh[1])
+                        thickness = (float(lh[1])*float(lh[2]) + float(rh[1])*float(rh[2])) / num_vert
+                        thickness_std = np.sqrt((float(lh[1]) * float(lh[3])**2 + float(rh[1]) * float(rh[3])**2) / num_vert)
+                        cortical_thickness[region] = {
+                            "thickness": thickness,
+                            "thickness_std": thickness_std,
+                        }
+                bankssts_thickness_avg = cortical_thickness.get('bankssts', {}).get('thickness', -1)
+                bankssts_thickness_std = cortical_thickness.get('bankssts', {}).get('thickness_std', -1)
+                caudalanteriorcingulate_thickness_avg = cortical_thickness.get('caudalanteriorcingulate', {}).get('thickness', -1)
+                caudalanteriorcingulate_thickness_std = cortical_thickness.get('caudalanteriorcingulate', {}).get('thickness_std', -1)
+                caudalmiddlefrontal_thickness_avg = cortical_thickness.get('caudalmiddlefrontal', {}).get('thickness', -1)
+                caudalmiddlefrontal_thickness_std = cortical_thickness.get('caudalmiddlefrontal', {}).get('thickness_std', -1)
+                cuneus_thickness_avg = cortical_thickness.get('cuneus', {}).get('thickness', -1)
+                cuneus_thickness_std = cortical_thickness.get('cuneus', {}).get('thickness_std', -1)
+                entorhinal_thickness_avg = cortical_thickness.get('entorhinal', {}).get('thickness', -1)
+                entorhinal_thickness_std = cortical_thickness.get('entorhinal', {}).get('thickness_std', -1)
+                fusiform_thickness_avg = cortical_thickness.get('fusiform', {}).get('thickness', -1)
+                fusiform_thickness_std = cortical_thickness.get('fusiform', {}).get('thickness_std', -1)
+                inferiorparietal_thickness_avg = cortical_thickness.get('inferiorparietal', {}).get('thickness', -1)
+                inferiorparietal_thickness_std = cortical_thickness.get('inferiorparietal', {}).get('thickness_std', -1)
+                inferiortemporal_thickness_avg = cortical_thickness.get('inferiortemporal', {}).get('thickness', -1)
+                inferiortemporal_thickness_std = cortical_thickness.get('inferiortemporal', {}).get('thickness_std', -1)
+                isthmuscingulate_thickness_avg = cortical_thickness.get('isthmuscingulate', {}).get('thickness', -1)
+                isthmuscingulate_thickness_std = cortical_thickness.get('isthmuscingulate', {}).get('thickness_std', -1)
+                lateraloccipital_thickness_avg = cortical_thickness.get('lateraloccipital', {}).get('thickness', -1)
+                lateraloccipital_thickness_std = cortical_thickness.get('lateraloccipital', {}).get('thickness_std', -1)
+                lateralorbitofrontal_thickness_avg = cortical_thickness.get('lateralorbitofrontal', {}).get('thickness', -1)
+                lateralorbitofrontal_thickness_std = cortical_thickness.get('lateralorbitofrontal', {}).get('thickness_std', -1)
+                lingual_thickness_avg = cortical_thickness.get('lingual', {}).get('thickness', -1)
+                lingual_thickness_std = cortical_thickness.get('lingual', {}).get('thickness_std', -1)
+                medialorbitofrontal_thickness_avg = cortical_thickness.get('medialorbitofrontal', {}).get('thickness', -1)
+                medialorbitofrontal_thickness_std = cortical_thickness.get('medialorbitofrontal', {}).get('thickness_std', -1)
+                middletemporal_thickness_avg = cortical_thickness.get('middletemporal', {}).get('thickness', -1)
+                middletemporal_thickness_std = cortical_thickness.get('middletemporal', {}).get('thickness_std', -1)
+                parahippocampal_thickness_avg = cortical_thickness.get('parahippocampal', {}).get('thickness', -1)
+                parahippocampal_thickness_std = cortical_thickness.get('parahippocampal', {}).get('thickness_std', -1)
+                paracentral_thickness_avg = cortical_thickness.get('paracentral', {}).get('thickness', -1)
+                paracentral_thickness_std = cortical_thickness.get('paracentral', {}).get('thickness_std', -1)
+                parsopercularis_thickness_avg = cortical_thickness.get('parsopercularis', {}).get('thickness', -1)
+                parsopercularis_thickness_std = cortical_thickness.get('parsopercularis', {}).get('thickness_std', -1)
+                parsorbitalis_thickness_avg = cortical_thickness.get('parsorbitalis', {}).get('thickness', -1)
+                parsorbitalis_thickness_std = cortical_thickness.get('parsorbitalis', {}).get('thickness_std', -1)
+                parstriangularis_thickness_avg = cortical_thickness.get('parstriangularis', {}).get('thickness', -1)
+                parstriangularis_thickness_std = cortical_thickness.get('parstriangularis', {}).get('thickness_std', -1)
+                pericalcarine_thickness_avg = cortical_thickness.get('pericalcarine', {}).get('thickness', -1)
+                pericalcarine_thickness_std = cortical_thickness.get('pericalcarine', {}).get('thickness_std', -1)
+                postcentral_thickness_avg = cortical_thickness.get('postcentral', {}).get('thickness', -1)
+                postcentral_thickness_std = cortical_thickness.get('postcentral', {}).get('thickness_std', -1)
+                posteriorcingulate_thickness_avg = cortical_thickness.get('posteriorcingulate', {}).get('thickness', -1)
+                posteriorcingulate_thickness_std = cortical_thickness.get('posteriorcingulate', {}).get('thickness_std', -1)
+                precentral_thickness_avg = cortical_thickness.get('precentral', {}).get('thickness', -1)
+                precentral_thickness_std = cortical_thickness.get('precentral', {}).get('thickness_std', -1)
+                precuneus_thickness_avg = cortical_thickness.get('precuneus', {}).get('thickness', -1)
+                precuneus_thickness_std = cortical_thickness.get('precuneus', {}).get('thickness_std', -1)
+                rostralanteriorcingulate_thickness_avg = cortical_thickness.get('rostralanteriorcingulate', {}).get('thickness', -1)
+                rostralanteriorcingulate_thickness_std = cortical_thickness.get('rostralanteriorcingulate', {}).get('thickness_std', -1)
+                rostralmiddlefrontal_thickness_avg = cortical_thickness.get('rostralmiddlefrontal', {}).get('thickness', -1)
+                rostralmiddlefrontal_thickness_std = cortical_thickness.get('rostralmiddlefrontal', {}).get('thickness_std', -1)
+                superiorfrontal_thickness_avg = cortical_thickness.get('superiorfrontal', {}).get('thickness', -1)
+                superiorfrontal_thickness_std = cortical_thickness.get('superiorfrontal', {}).get('thickness_std', -1)
+                superiorparietal_thickness_avg = cortical_thickness.get('superiorparietal', {}).get('thickness', -1)
+                superiorparietal_thickness_std = cortical_thickness.get('superiorparietal', {}).get('thickness_std', -1)
+                superiortemporal_thickness_avg = cortical_thickness.get('superiortemporal', {}).get('thickness', -1)
+                superiortemporal_thickness_std = cortical_thickness.get('superiortemporal', {}).get('thickness_std', -1)
+                inferiortemporal_thickness_avg = cortical_thickness.get('inferiortemporal', {}).get('thickness', -1)
+                inferiortemporal_thickness_std = cortical_thickness.get('inferiortemporal', {}).get('thickness_std', -1)
+                supramarginal_thickness_avg = cortical_thickness.get('supramarginal', {}).get('thickness', -1)
+                supramarginal_thickness_std = cortical_thickness.get('supramarginal', {}).get('thickness_std', -1)
+                frontalpole_thickness_avg = cortical_thickness.get('frontalpole', {}).get('thickness', -1)
+                frontalpole_thickness_std = cortical_thickness.get('frontalpole', {}).get('thickness_std', -1)
+                temporalpole_thickness_avg = cortical_thickness.get('temporalpole', {}).get('thickness', -1)
+                temporalpole_thickness_std = cortical_thickness.get('temporalpole', {}).get('thickness_std', -1)
+                transversetemporal_thickness_avg = cortical_thickness.get('transversetemporal', {}).get('thickness', -1)
+                transversetemporal_thickness_std = cortical_thickness.get('transversetemporal', {}).get('thickness_std', -1)
+                insula_thickness_avg = cortical_thickness.get('insula', {}).get('thickness', -1)
+                insula_thickness_std = cortical_thickness.get('insula', {}).get('thickness_std', -1)
+
+            if not wmparc_failed:
+                HIPPO_INDICES = np.where(((wmparc == regions["HIPPO"][0]) | (wmparc == regions["HIPPO"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                PHG_INDICES = np.where(((wmparc == regions["PHG"][0]) | (wmparc == regions["PHG"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                PUTAMEN_INDICES = np.where(((wmparc == regions["PUTAMEN"][0]) | (wmparc == regions["PUTAMEN"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                PALLIDUM_INDICES = np.where(((wmparc == regions["PALLIDUM"][0]) | (wmparc == regions["PALLIDUM"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                THALAMUS_INDICES = np.where(((wmparc == regions["THALAMUS"][0]) | (wmparc == regions["THALAMUS"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                CAUDATE_INDICES = np.where(((wmparc == regions["CAUDATE"][0]) | (wmparc == regions["CAUDATE"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                AMYGDALA_INDICES = np.where(((wmparc == regions["AMYGDALA"][0]) | (wmparc == regions["AMYGDALA"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                ENTORHINAL_CORTEX_INDICES = np.where(((wmparc == regions["ENTORHINAL_CORTEX"][0]) | (wmparc == regions["ENTORHINAL_CORTEX"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                FUSIFORM_GYRUS_CORTEX_INDICES = np.where(((wmparc == regions["FUSIFORM_GYRUS_CORTEX"][0]) | (wmparc == regions["FUSIFORM_GYRUS_CORTEX"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                FUSIFORM_GYRUS_WM_INDICES = np.where(((wmparc == regions["FUSIFORM_GYRUS_WM"][0]) | (wmparc == regions["FUSIFORM_GYRUS_WM"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                INSULA_WM_INDICES = np.where(((wmparc == regions["INSULA_WM"][0]) | (wmparc == regions["INSULA_WM"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                SUPERIOR_TEMPORAL_CORTEX_INDICES = np.where(((wmparc == regions["SUPERIOR_TEMPORAL_CORTEX"][0]) | (wmparc == regions["SUPERIOR_TEMPORAL_CORTEX"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                INFERIOR_TEMPORAL_CORTEX_INDICES = np.where(((wmparc == regions["INFERIOR_TEMPORAL_CORTEX"][0]) | (wmparc == regions["INFERIOR_TEMPORAL_CORTEX"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                POSTERIOR_CINGULATE_CORTEX_INDICES = np.where(((wmparc == regions["POSTERIOR_CINGULATE_CORTEX"][0]) | (wmparc == regions["POSTERIOR_CINGULATE_CORTEX"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+                MEDIAL_TEMPORAL_CORTEX_INDICES = np.where(((wmparc == regions["HIPPO"][0]) | (wmparc == regions["HIPPO"][1]) | (wmparc == regions["PHG"][0]) | (wmparc == regions["PHG"][1]) | (wmparc == regions["ENTORHINAL_CORTEX"][0]) | (wmparc == regions["ENTORHINAL_CORTEX"][1])) & (ktrans_map > KTRANS_MIN_THRESHOLD))
+            
+                Ktrans_Hippo = ktrans_map[HIPPO_INDICES]*1000
+                Ktrans_PhG = ktrans_map[PHG_INDICES]*1000
+                Ktrans_Putamen = ktrans_map[PUTAMEN_INDICES]*1000
+                Ktrans_Pallidum = ktrans_map[PALLIDUM_INDICES]*1000
+                Ktrans_Thalamus = ktrans_map[THALAMUS_INDICES]*1000
+                Ktrans_Caudate = ktrans_map[CAUDATE_INDICES]*1000
+                Ktrans_Amygdala = ktrans_map[AMYGDALA_INDICES]*1000
+                Ktrans_Entorhinal_cortex = ktrans_map[ENTORHINAL_CORTEX_INDICES]*1000
+                Ktrans_Fusiform_gyrus_cortex = ktrans_map[FUSIFORM_GYRUS_CORTEX_INDICES]*1000
+                Ktrans_Fusiform_gyrus_WM = ktrans_map[FUSIFORM_GYRUS_WM_INDICES]*1000
+                Ktrans_Insula_WM = ktrans_map[INSULA_WM_INDICES]*1000
+                Ktrans_Superior_temporal_cortex = ktrans_map[SUPERIOR_TEMPORAL_CORTEX_INDICES]*1000
+                Ktrans_Inferior_temporal_cortex = ktrans_map[INFERIOR_TEMPORAL_CORTEX_INDICES]*1000
+                Ktrans_Posterior_cingulate_cortex = ktrans_map[POSTERIOR_CINGULATE_CORTEX_INDICES]*1000
+                Ktrans_Medial_temporal_cortex = ktrans_map[MEDIAL_TEMPORAL_CORTEX_INDICES]*1000
+                Vp_Hippo = Vp_map[HIPPO_INDICES]
+                Vp_PhG = Vp_map[PHG_INDICES]
+                Vp_Putamen = Vp_map[PUTAMEN_INDICES]
+                Vp_Pallidum = Vp_map[PALLIDUM_INDICES]
+                Vp_Thalamus = Vp_map[THALAMUS_INDICES]
+                Vp_Caudate = Vp_map[CAUDATE_INDICES]
+                Vp_Amygdala = Vp_map[AMYGDALA_INDICES]
+                Vp_Entorhinal_cortex = Vp_map[ENTORHINAL_CORTEX_INDICES]
+                Vp_Fusiform_gyrus_cortex = Vp_map[FUSIFORM_GYRUS_CORTEX_INDICES]
+                Vp_Fusiform_gyrus_WM = Vp_map[FUSIFORM_GYRUS_WM_INDICES]
+                Vp_Insula_WM = Vp_map[INSULA_WM_INDICES]
+                Vp_Superior_temporal_cortex = Vp_map[SUPERIOR_TEMPORAL_CORTEX_INDICES]
+                Vp_Inferior_temporal_cortex = Vp_map[INFERIOR_TEMPORAL_CORTEX_INDICES]
+                Vp_Posterior_cingulate_cortex = Vp_map[POSTERIOR_CINGULATE_CORTEX_INDICES]
+                Vp_Medial_temporal_cortex = Vp_map[MEDIAL_TEMPORAL_CORTEX_INDICES]
+            
+                Ktrans_Hippo_median = np.nanmedian(Ktrans_Hippo)
+                Ktrans_PhG_median = np.nanmedian(Ktrans_PhG)
+                Ktrans_Putamen_median = np.nanmedian(Ktrans_Putamen)
+                Ktrans_Pallidum_median = np.nanmedian(Ktrans_Pallidum)
+                Ktrans_Thalamus_median = np.nanmedian(Ktrans_Thalamus)
+                Ktrans_Caudate_median = np.nanmedian(Ktrans_Caudate)
+                Ktrans_Amygdala_median = np.nanmedian(Ktrans_Amygdala)
+                Ktrans_Entorhinal_cortex_median = np.nanmedian(Ktrans_Entorhinal_cortex)
+                Ktrans_Fusiform_gyrus_cortex_median = np.nanmedian(Ktrans_Fusiform_gyrus_cortex)
+                Ktrans_Fusiform_gyrus_WM_median = np.nanmedian(Ktrans_Fusiform_gyrus_WM)
+                Ktrans_Insula_WM_median = np.nanmedian(Ktrans_Insula_WM)
+                Ktrans_Superior_temporal_cortex_median = np.nanmedian(Ktrans_Superior_temporal_cortex)
+                Ktrans_Inferior_temporal_cortex_median = np.nanmedian(Ktrans_Inferior_temporal_cortex)
+                Ktrans_Posterior_cingulate_cortex_median = np.nanmedian(Ktrans_Posterior_cingulate_cortex)
+                Ktrans_Medial_temporal_cortex_median = np.nanmedian(Ktrans_Medial_temporal_cortex)
+                Vp_Hippo_median = np.nanmedian(Vp_Hippo)
+                Vp_PhG_median = np.nanmedian(Vp_PhG)
+                Vp_Putamen_median = np.nanmedian(Vp_Putamen)
+                Vp_Pallidum_median = np.nanmedian(Vp_Pallidum)
+                Vp_Thalamus_median = np.nanmedian(Vp_Thalamus)
+                Vp_Caudate_median = np.nanmedian(Vp_Caudate)
+                Vp_Amygdala_median = np.nanmedian(Vp_Amygdala)
+                Vp_Entorhinal_cortex_median = np.nanmedian(Vp_Entorhinal_cortex)
+                Vp_Fusiform_gyrus_cortex_median = np.nanmedian(Vp_Fusiform_gyrus_cortex)
+                Vp_Fusiform_gyrus_WM_median = np.nanmedian(Vp_Fusiform_gyrus_WM)
+                Vp_Insula_WM_median = np.nanmedian(Vp_Insula_WM)
+                Vp_Superior_temporal_cortex_median = np.nanmedian(Vp_Superior_temporal_cortex)
+                Vp_Inferior_temporal_cortex_median = np.nanmedian(Vp_Inferior_temporal_cortex)
+                Vp_Posterior_cingulate_cortex_median = np.nanmedian(Vp_Posterior_cingulate_cortex)
+                Vp_Medial_temporal_cortex_median = np.nanmedian(Vp_Medial_temporal_cortex)
+            
+                if Ktrans_Hippo_median > 5:
+                    if subject_id + "_" + timepoint not in whole_hippo_outliers:
+                        whole_hippo_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_PhG_median > 5:
+                    if subject_id + "_" + timepoint not in whole_phg_outliers:
+                        whole_phg_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Putamen_median > 5:
+                    if subject_id + "_" + timepoint not in whole_putamen_outliers:
+                        whole_putamen_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Pallidum_median > 5:
+                    if subject_id + "_" + timepoint not in whole_pallidum_outliers:
+                        whole_pallidum_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Thalamus_median > 5:
+                    if subject_id + "_" + timepoint not in whole_thalamus_outliers:
+                        whole_thalamus_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Caudate_median > 5:
+                    if subject_id + "_" + timepoint not in whole_caudate_outliers:
+                        whole_caudate_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Amygdala_median > 5:
+                    if subject_id + "_" + timepoint not in whole_amygdala_outliers:
+                        whole_amygdala_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Entorhinal_cortex_median > 5:
+                    if subject_id + "_" + timepoint not in whole_entorhinal_cortex_outliers:
+                        whole_entorhinal_cortex_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Fusiform_gyrus_cortex_median > 5:
+                    if subject_id + "_" + timepoint not in whole_fusiform_gyrus_cortex_outliers:
+                        whole_fusiform_gyrus_cortex_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Fusiform_gyrus_WM_median > 5:
+                    if subject_id + "_" + timepoint not in whole_fusiform_gyrus_WM_outliers:
+                        whole_fusiform_gyrus_WM_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Insula_WM_median > 5:
+                    if subject_id + "_" + timepoint not in whole_insula_WM_outliers:
+                        whole_insula_WM_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Superior_temporal_cortex_median > 5:
+                    if subject_id + "_" + timepoint not in whole_superior_temporal_cortex_outliers:
+                        whole_superior_temporal_cortex_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Inferior_temporal_cortex_median > 5:
+                    if subject_id + "_" + timepoint not in whole_inferior_temporal_cortex_outliers:
+                        whole_inferior_temporal_cortex_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Posterior_cingulate_cortex_median > 5:
+                    if subject_id + "_" + timepoint not in whole_posterior_cingulate_cortex_outliers:
+                        whole_posterior_cingulate_cortex_outliers.append(subject_id + "_" + timepoint)
+                if Ktrans_Medial_temporal_cortex_median > 5:
+                    if subject_id + "_" + timepoint not in whole_medial_temporal_cortex_outliers:
+                        whole_medial_temporal_cortex_outliers.append(subject_id + "_" + timepoint)
+
+                # Calculate SNR from getting mean SI in DCE thalamus then stdev of the difference between the last 2 DCE measures
+                DCE_img_path = os.path.join(dceprep_dir, subject_id, timepoint, f"dce/{subject_id}_{timepoint}_desc-bfcz_DCE.nii.gz")
+                if os.path.exists(DCE_img_path):
+                    try:
+                        # DCE_img = nib.load(DCE_img_path)
+                        # DCE_img = DCE_img.get_fdata()
+                        SI_Thalamus_DCE = dce[THALAMUS_INDICES]
+
+                        SI_Thalamus_DCE_mean = np.mean(SI_Thalamus_DCE)
+                        SI_Thalamus_DCE_last = SI_Thalamus_DCE[:,-1]
+                        SI_Thalamus_DCE_penultimate = SI_Thalamus_DCE[:,-2]
+                        SI_Thalamus_DCE_last2_difference = SI_Thalamus_DCE_last - SI_Thalamus_DCE_penultimate
+                        SI_Thalamus_DCE_noise_stdev = np.std(SI_Thalamus_DCE_last2_difference)
+                        # calculate SNR
+                        SNR = SI_Thalamus_DCE_mean / SI_Thalamus_DCE_noise_stdev
+                    except Exception as e:
+                        SNR = -1
+                        print(f"Could not calculate SNR for {subject_id} {timepoint}: {e}")
+                else:
+                    SNR = -1
+            
+            entry = subject_id + "_" + timepoint
+            # Common data for both success and fail
+            case_data = {
+                "AIFitness": AIFitness,
+                "aif_mmol": aif_mmol,
+                "aif_fitted_r2": aif_fitted_r2,
+                "T1_wm_median": T1_wm_median,
+                "T1_gm_median": T1_gm_median,
+                "T1_blood": T1_blood,
+                "Ktrans_wm_median": Ktrans_wm_median,
+                "Ktrans_gm_median": Ktrans_gm_median,
+                "max_disp": max_disp,
+                "Manufacturer": manufacturer,
+                "Field_strength": field_strength,
+                "Machine": machine,
+                "Institution": institution,
+                "Date": date,
+                "Sex": sex,
+                "Age": age,
+                "Coil": coil,
+                "Scan_options": scan_options,
+                "TE": TE,
+                "Time_resolution": time_resolution,
+                "Flip_angle": flip_angle,
+                "TR": TR,
+                "n_reps": n_reps,
+                "Approximate SNR": SNR,
+                "Ktrans_Hippo_median": Ktrans_Hippo_median,
+                "Ktrans_PhG_median": Ktrans_PhG_median,
+                "Ktrans_Putamen_median": Ktrans_Putamen_median,
+                "Ktrans_Pallidum_median": Ktrans_Pallidum_median,
+                "Ktrans_Thalamus_median": Ktrans_Thalamus_median,
+                "Ktrans_Caudate_median": Ktrans_Caudate_median,
+                "Ktrans_Amygdala_median": Ktrans_Amygdala_median,
+                "Ktrans_Entorhinal_cortex_median": Ktrans_Entorhinal_cortex_median,
+                "Ktrans_Fusiform_gyrus_cortex_median": Ktrans_Fusiform_gyrus_cortex_median,
+                "Ktrans_Fusiform_gyrus_WM_median": Ktrans_Fusiform_gyrus_WM_median,
+                "Ktrans_Insula_WM_median": Ktrans_Insula_WM_median,
+                "Ktrans_Superior_temporal_cortex_median": Ktrans_Superior_temporal_cortex_median,
+                "Ktrans_Inferior_temporal_cortex_median": Ktrans_Inferior_temporal_cortex_median,
+                "Ktrans_Posterior_cingulate_cortex_median": Ktrans_Posterior_cingulate_cortex_median,
+                "Ktrans_Medial_temporal_cortex_median": Ktrans_Medial_temporal_cortex_median,
+                "Vp_Hippo_median": Vp_Hippo_median,
+                "Vp_PhG_median": Vp_PhG_median,
+                "Vp_Putamen_median": Vp_Putamen_median,
+                "Vp_Pallidum_median": Vp_Pallidum_median,
+                "Vp_Thalamus_median": Vp_Thalamus_median,
+                "Vp_Caudate_median": Vp_Caudate_median,
+                "Vp_Amygdala_median": Vp_Amygdala_median,
+                "Vp_Entorhinal_cortex_median": Vp_Entorhinal_cortex_median,
+                "Vp_Fusiform_gyrus_cortex_median": Vp_Fusiform_gyrus_cortex_median,
+                "Vp_Fusiform_gyrus_WM_median": Vp_Fusiform_gyrus_WM_median,
+                "Vp_Insula_WM_median": Vp_Insula_WM_median,
+                "Vp_Superior_temporal_cortex_median": Vp_Superior_temporal_cortex_median,
+                "Vp_Inferior_temporal_cortex_median": Vp_Inferior_temporal_cortex_median,
+                "Vp_Posterior_cingulate_cortex_median": Vp_Posterior_cingulate_cortex_median,
+                "Vp_Medial_temporal_cortex_median": Vp_Medial_temporal_cortex_median,
+                "hippo_vol": hippo_vol,
+                "phg_vol": phg_vol,
+                "putamen_vol": putamen_vol,
+                "pallidum_vol": pallidum_vol,
+                "thalamus_vol": thalamus_vol,
+                "caudate_vol": caudate_vol,
+                "amygdala_vol": amygdala_vol,
+                "entorhinal_cortex_vol": entorhinal_cortex_vol,
+                "fusiform_gyrus_cortex_vol": fusiform_gyrus_cortex_vol,
+                "fusiform_gyrus_wm_vol": fusiform_gyrus_wm_vol,
+                "insula_wm_vol": insula_wm_vol,
+                "superior_temporal_cortex_vol": superior_temporal_cortex_vol,
+                "inferior_temporal_cortex_vol": inferior_temporal_cortex_vol,
+                "posterior_cingulate_cortex_vol": posterior_cingulate_cortex_vol,
+                "medial_temporal_cortex_vol": medial_temporal_cortex_vol,
+                "manual_aif_status": manual_aif_status,
+                "bankssts_thickness_avg": bankssts_thickness_avg,
+                "bankssts_thickness_std": bankssts_thickness_std,
+                "caudalanteriorcingulate_thickness_avg": caudalanteriorcingulate_thickness_avg,
+                "caudalanteriorcingulate_thickness_std": caudalanteriorcingulate_thickness_std,
+                "caudalmiddlefrontal_thickness_avg": caudalmiddlefrontal_thickness_avg,
+                "caudalmiddlefrontal_thickness_std": caudalmiddlefrontal_thickness_std,
+                "cuneus_thickness_avg": cuneus_thickness_avg,
+                "cuneus_thickness_std": cuneus_thickness_std,
+                "entorhinal_thickness_avg": entorhinal_thickness_avg,
+                "entorhinal_thickness_std": entorhinal_thickness_std,
+                "fusiform_thickness_avg": fusiform_thickness_avg,
+                "fusiform_thickness_std": fusiform_thickness_std,
+                "inferiorparietal_thickness_avg": inferiorparietal_thickness_avg,
+                "inferiorparietal_thickness_std": inferiorparietal_thickness_std,
+                "inferiortemporal_thickness_avg": inferiortemporal_thickness_avg,
+                "inferiortemporal_thickness_std": inferiortemporal_thickness_std,
+                "isthmuscingulate_thickness_avg": isthmuscingulate_thickness_avg,
+                "isthmuscingulate_thickness_std": isthmuscingulate_thickness_std,
+                "lateraloccipital_thickness_avg": lateraloccipital_thickness_avg,
+                "lateraloccipital_thickness_std": lateraloccipital_thickness_std,
+                "lateralorbitofrontal_thickness_avg": lateralorbitofrontal_thickness_avg,
+                "lateralorbitofrontal_thickness_std": lateralorbitofrontal_thickness_std,
+                "lingual_thickness_avg": lingual_thickness_avg,
+                "lingual_thickness_std": lingual_thickness_std,
+                "medialorbitofrontal_thickness_avg": medialorbitofrontal_thickness_avg,
+                "medialorbitofrontal_thickness_std": medialorbitofrontal_thickness_std,
+                "middletemporal_thickness_avg": middletemporal_thickness_avg,
+                "middletemporal_thickness_std": middletemporal_thickness_std,
+                "parahippocampal_thickness_avg": parahippocampal_thickness_avg,
+                "parahippocampal_thickness_std": parahippocampal_thickness_std,
+                "paracentral_thickness_avg": paracentral_thickness_avg,
+                "paracentral_thickness_std": paracentral_thickness_std,
+                "parsopercularis_thickness_avg": parsopercularis_thickness_avg,
+                "parsopercularis_thickness_std": parsopercularis_thickness_std,
+                "parsorbitalis_thickness_avg": parsorbitalis_thickness_avg,
+                "parsorbitalis_thickness_std": parsorbitalis_thickness_std,
+                "parstriangularis_thickness_avg": parstriangularis_thickness_avg,
+                "parstriangularis_thickness_std": parstriangularis_thickness_std,
+                "pericalcarine_thickness_avg": pericalcarine_thickness_avg,
+                "pericalcarine_thickness_std": pericalcarine_thickness_std,
+                "postcentral_thickness_avg": postcentral_thickness_avg,
+                "postcentral_thickness_std": postcentral_thickness_std,
+                "posteriorcingulate_thickness_avg": posteriorcingulate_thickness_avg,
+                "posteriorcingulate_thickness_std": posteriorcingulate_thickness_std,
+                "precentral_thickness_avg": precentral_thickness_avg,
+                "precentral_thickness_std": precentral_thickness_std,
+                "precuneus_thickness_avg": precuneus_thickness_avg,
+                "precuneus_thickness_std": precuneus_thickness_std,
+                "rostralanteriorcingulate_thickness_avg": rostralanteriorcingulate_thickness_avg,
+                "rostralanteriorcingulate_thickness_std": rostralanteriorcingulate_thickness_std,
+                "rostralmiddlefrontal_thickness_avg": rostralmiddlefrontal_thickness_avg,
+                "rostralmiddlefrontal_thickness_std": rostralmiddlefrontal_thickness_std,
+                "superiorfrontal_thickness_avg": superiorfrontal_thickness_avg,
+                "superiorfrontal_thickness_std": superiorfrontal_thickness_std,
+                "superiorparietal_thickness_avg": superiorparietal_thickness_avg,
+                "superiorparietal_thickness_std": superiorparietal_thickness_std,
+                "superiortemporal_thickness_avg": superiortemporal_thickness_avg,
+                "superiortemporal_thickness_std": superiortemporal_thickness_std,
+                "supramarginal_thickness_avg": supramarginal_thickness_avg,
+                "supramarginal_thickness_std": supramarginal_thickness_std,
+                "frontalpole_thickness_avg": frontalpole_thickness_avg,
+                "frontalpole_thickness_std": frontalpole_thickness_std,
+                "temporalpole_thickness_avg": temporalpole_thickness_avg,
+                "temporalpole_thickness_std": temporalpole_thickness_std,
+                "transversetemporal_thickness_avg": transversetemporal_thickness_avg,
+                "transversetemporal_thickness_std": transversetemporal_thickness_std,
+                "insula_thickness_avg": insula_thickness_avg,
+                "insula_thickness_std": insula_thickness_std
+            }
+
+            if wmparc_failed is False:
+                # case_data["fastsurfer"] = fastsurfer
+                successful_timepoints.append(entry.replace("_", "/"))
+                population_data[entry] = case_data
+            else:
+                case_data["Reason"] = error
+                population_data_failed[entry] = case_data
+
+# time
+# start = time.time()
+# lock = threading.Lock()
+subject_timepoints = {subject_id: sorted(os.listdir(os.path.join(dceprep_dir, subject_id))) for subject_id in subjects}
+
+with ThreadPoolExecutor() as executor:
+    futures = [executor.submit(get_case_stats, subject_id, timepoint) for subject_id, timepoints in subject_timepoints.items() for timepoint in timepoints]
+    for future in futures:
+        try:
+            future.result()
+        except Exception as e:
+            print(f"Error in future: {future}, {e}")
+# end = time.time()
+# print(f"Time taken: {end - start} seconds")
+# get flagged cases
+flagged_cases = []
+flagged_links = []
+MOTION_THRESHOLD = 3.8
+AIFITNESS_THRESHOLD = 59
+for entry in successful_timepoints:
+    flag_str = ""
+    subject = entry.split('/')[0]
+    session = entry.split('/')[1]
+    save_name = f"{output_dir}/{entry}/reports/{subject}_{session}_desc-casereport.html"
+    flag = False
+    entry = subject + "_" + session
+    if entry in population_data.keys() and population_data[entry]['max_disp'] > MOTION_THRESHOLD:
+        flag = True
+        flag_str += "motion"
+        save_name += "#MCFLIRT"
+    if entry in population_data.keys() and population_data[entry]['AIFitness'] < AIFITNESS_THRESHOLD:
+        flag = True
+        if flag_str != "":
+            flag_str += ", "
+        flag_str += "AIFitness"
+        if not save_name[-1].endswith("MCFLIRT"):
+            save_name += "#AIF"
+    if flag_str != "":
+        flagged_cases.append(f'{entry} ({flag_str})')
+        flagged_links.append(save_name)
+    if flag:
+        # move to population_data_exclude
+        # put AUTO at beginning of flag_str
+        flag_str = "AUTO: " + flag_str
+        population_data_exclude[entry] = population_data.pop(entry)
+        population_data_exclude[entry]['Reason'] = flag_str
+        # move outliers to exclude
+        if entry in whole_hippo_outliers:
+            whole_hippo_outliers_exclude.append(whole_hippo_outliers.pop(whole_hippo_outliers.index(entry)))
+        if entry in whole_phg_outliers:
+            whole_phg_outliers_exclude.append(whole_phg_outliers.pop(whole_phg_outliers.index(entry)))
+        if entry in whole_putamen_outliers:
+            whole_putamen_outliers_exclude.append(whole_putamen_outliers.pop(whole_putamen_outliers.index(entry)))
+        if entry in whole_pallidum_outliers:
+            whole_pallidum_outliers_exclude.append(whole_pallidum_outliers.pop(whole_pallidum_outliers.index(entry)))
+        if entry in whole_thalamus_outliers:
+            whole_thalamus_outliers_exclude.append(whole_thalamus_outliers.pop(whole_thalamus_outliers.index(entry)))
+        if entry in whole_caudate_outliers:
+            whole_caudate_outliers_exclude.append(whole_caudate_outliers.pop(whole_caudate_outliers.index(entry)))
+        if entry in whole_amygdala_outliers:
+            whole_amygdala_outliers_exclude.append(whole_amygdala_outliers.pop(whole_amygdala_outliers.index(entry)))
+        if entry in whole_entorhinal_cortex_outliers:
+            whole_entorhinal_cortex_outliers_exclude.append(whole_entorhinal_cortex_outliers.pop(whole_entorhinal_cortex_outliers.index(entry)))
+        if entry in whole_fusiform_gyrus_cortex_outliers:
+            whole_fusiform_gyrus_cortex_outliers_exclude.append(whole_fusiform_gyrus_cortex_outliers.pop(whole_fusiform_gyrus_cortex_outliers.index(entry)))
+        if entry in whole_fusiform_gyrus_WM_outliers:
+            whole_fusiform_gyrus_WM_outliers_exclude.append(whole_fusiform_gyrus_WM_outliers.pop(whole_fusiform_gyrus_WM_outliers.index(entry)))
+        if entry in whole_insula_WM_outliers:
+            whole_insula_WM_outliers_exclude.append(whole_insula_WM_outliers.pop(whole_insula_WM_outliers.index(entry)))
+        if entry in whole_superior_temporal_cortex_outliers:
+            whole_superior_temporal_cortex_outliers_exclude.append(whole_superior_temporal_cortex_outliers.pop(whole_superior_temporal_cortex_outliers.index(entry)))
+        if entry in whole_inferior_temporal_cortex_outliers:
+            whole_inferior_temporal_cortex_outliers_exclude.append(whole_inferior_temporal_cortex_outliers.pop(whole_inferior_temporal_cortex_outliers.index(entry)))
+        if entry in whole_posterior_cingulate_cortex_outliers:
+            whole_posterior_cingulate_cortex_outliers_exclude.append(whole_posterior_cingulate_cortex_outliers.pop(whole_posterior_cingulate_cortex_outliers.index(entry)))
+        if entry in whole_medial_temporal_cortex_outliers:
+            whole_medial_temporal_cortex_outliers_exclude.append(whole_medial_temporal_cortex_outliers.pop(whole_medial_temporal_cortex_outliers.index(entry)))
+
+# remove cases with Machine == Signa HDxt from population_data_exclude and population_data
+# population_data_exclude_signa = {}
+# for entry in list(population_data_exclude.keys()):
+#     if population_data_exclude[entry]["Machine"] == "Signa HDxt":
+#         population_data_exclude_signa[entry] = population_data_exclude.pop(entry)
+#         population_data_exclude_signa[entry]['Reason'] = "AUTO: Crazy GE Data"
+
+# for entry in list(population_data.keys()):
+#     if population_data[entry]["Machine"] == "Signa HDxt":
+#         population_data_exclude_signa[entry] = population_data.pop(entry)
+#         population_data_exclude_signa[entry]['Reason'] = "AUTO: Crazy GE Data"
+
+try:
+    AIFitness_values = [float(population_data[entry]["AIFitness"]) for entry in population_data]
+    AIFitness_mean = np.mean(AIFitness_values)
+    AIFitness_median = np.median(AIFitness_values)
+    AIFitness_std = np.std(AIFitness_values)
+    AIFitness_5th_percentile = np.percentile(AIFitness_values, 5)
+except Exception as e:
+    print("AIFitness issue.", e)
+    AIFitness_mean = -1
+    AIFitness_median = -1
+    AIFitness_std = -1
+    AIFitness_5th_percentile = -1
+
+try:
+    AIFitness_exclude = [float(population_data_exclude[entry]["AIFitness"]) for entry in population_data_exclude]
+    AIFitness_exclude_mean = np.mean(AIFitness_exclude)
+    AIFitness_exclude_median = np.median(AIFitness_exclude)
+    AIFitness_exclude_std = np.std(AIFitness_exclude)
+    AIFitness_exclude_5th_percentile = np.percentile(AIFitness_exclude, 5)
+except Exception as e:
+    print("AIFitness issue.", e)
+    AIFitness_exclude_mean = -1
+    AIFitness_exclude_median = -1
+    AIFitness_exclude_std = -1
+    AIFitness_exclude_5th_percentile = -1
+
+try:
+    aif_mmol_mean = np.mean([population_data[entry]["aif_mmol"] for entry in population_data])
+    aif_mmol_median = np.median([population_data[entry]["aif_mmol"] for entry in population_data])
+    aif_mmol_std = np.std([population_data[entry]["aif_mmol"] for entry in population_data])
+    aif_mmol_5th_percentile = np.percentile([population_data[entry]["aif_mmol"] for entry in population_data], 5)
+    aif_mmol_95th_percentile = np.percentile([population_data[entry]["aif_mmol"] for entry in population_data], 95)
+except Exception as e:
+    print(e)
+    aif_mmol_mean = -1
+    aif_mmol_median = -1
+    aif_mmol_std = -1
+    aif_mmol_5th_percentile = -1
+    aif_mmol_95th_percentile = -1
+
+try:
+    aif_mmol_exclude = [population_data_exclude[entry]["aif_mmol"] for entry in population_data_exclude]
+    aif_mmol_mean_exclude = np.mean(aif_mmol_exclude)
+    aif_mmol_median_exclude = np.median(aif_mmol_exclude)
+    aif_mmol_std_exclude = np.std(aif_mmol_exclude)
+    aif_mmol_5th_percentile_exclude = np.percentile(aif_mmol_exclude, 5)
+    aif_mmol_95th_percentile_exclude = np.percentile(aif_mmol_exclude, 95)
+except Exception as e:
+    print(e)
+    aif_mmol_mean_exclude = -1
+    aif_mmol_median_exclude = -1
+    aif_mmol_std_exclude = -1
+    aif_mmol_5th_percentile_exclude = -1
+    aif_mmol_95th_percentile_exclude = -1
+
+try:
+    T1_wm_mean = np.mean([population_data[entry]["T1_wm_median"] for entry in population_data])
+    T1_wm_median = np.median([population_data[entry]["T1_wm_median"] for entry in population_data])
+    T1_wm_std = np.std([population_data[entry]["T1_wm_median"] for entry in population_data])
+    T1_wm_5th_percentile = np.percentile([population_data[entry]["T1_wm_median"] for entry in population_data], 5)
+    T1_wm_95th_percentile = np.percentile([population_data[entry]["T1_wm_median"] for entry in population_data], 95)
+except Exception as e:
+    print(e)
+    T1_wm_mean = -1
+    T1_wm_median = -1
+    T1_wm_std = -1
+    T1_wm_5th_percentile = -1
+    T1_wm_95th_percentile = -1
+
+try:
+    T1_wm_mean_exclude = np.mean([population_data_exclude[entry]["T1_wm_median"] for entry in population_data_exclude])
+    T1_wm_median_exclude = np.median([population_data_exclude[entry]["T1_wm_median"] for entry in population_data_exclude])
+    T1_wm_std_exclude = np.std([population_data_exclude[entry]["T1_wm_median"] for entry in population_data_exclude])
+    T1_wm_5th_percentile_exclude = np.percentile([population_data_exclude[entry]["T1_wm_median"] for entry in population_data_exclude], 5)
+    T1_wm_95th_percentile_exclude = np.percentile([population_data_exclude[entry]["T1_wm_median"] for entry in population_data_exclude], 95)
+except Exception as e:
+    print(e)
+    T1_wm_mean_exclude = -1
+    T1_wm_median_exclude = -1
+    T1_wm_std_exclude = -1
+    T1_wm_5th_percentile_exclude = -1
+    T1_wm_95th_percentile_exclude = -1
+
+try:
+    T1_gm_mean = np.mean([population_data[entry]["T1_gm_median"] for entry in population_data])
+    T1_gm_median = np.median([population_data[entry]["T1_gm_median"] for entry in population_data])
+    T1_gm_std = np.std([population_data[entry]["T1_gm_median"] for entry in population_data])
+    T1_gm_5th_percentile = np.percentile([population_data[entry]["T1_gm_median"] for entry in population_data], 5)
+    T1_gm_95th_percentile = np.percentile([population_data[entry]["T1_gm_median"] for entry in population_data], 95)
+except Exception as e:
+    print(e)
+    T1_gm_mean = -1
+    T1_gm_median = -1
+    T1_gm_std = -1
+    T1_gm_5th_percentile = -1
+    T1_gm_95th_percentile = -1
+
+try:
+    T1_gm_mean_exclude = np.mean([population_data_exclude[entry]["T1_gm_median"] for entry in population_data_exclude])
+    T1_gm_median_exclude = np.median([population_data_exclude[entry]["T1_gm_median"] for entry in population_data_exclude])
+    T1_gm_std_exclude = np.std([population_data_exclude[entry]["T1_gm_median"] for entry in population_data_exclude])
+    T1_gm_5th_percentile_exclude = np.percentile([population_data_exclude[entry]["T1_gm_median"] for entry in population_data_exclude], 5)
+    T1_gm_95th_percentile_exclude = np.percentile([population_data_exclude[entry]["T1_gm_median"] for entry in population_data_exclude], 95)
+except Exception as e:
+    print(e)
+    T1_gm_mean_exclude = -1
+    T1_gm_median_exclude = -1
+    T1_gm_std_exclude = -1
+    T1_gm_5th_percentile_exclude = -1
+    T1_gm_95th_percentile_exclude = -1
+
+try:
+    T1_blood_mean = np.mean([population_data[entry]["T1_blood"] for entry in population_data])
+    T1_blood_median = np.median([population_data[entry]["T1_blood"] for entry in population_data])
+    T1_blood_std = np.std([population_data[entry]["T1_blood"] for entry in population_data])
+    T1_blood_5th_percentile = np.percentile([population_data[entry]["T1_blood"] for entry in population_data], 5)
+    T1_blood_95th_percentile = np.percentile([population_data[entry]["T1_blood"] for entry in population_data], 95)
+except Exception as e:
+    print(e)
+    T1_blood_mean = -1
+    T1_blood_median = -1
+    T1_blood_std = -1
+    T1_blood_5th_percentile = -1
+    T1_blood_95th_percentile = -1
+
+try:
+    T1_blood_mean_exclude = np.mean([population_data_exclude[entry]["T1_blood"] for entry in population_data_exclude])
+    T1_blood_median_exclude = np.median([population_data_exclude[entry]["T1_blood"] for entry in population_data_exclude])
+    T1_blood_std_exclude = np.std([population_data_exclude[entry]["T1_blood"] for entry in population_data_exclude])
+    T1_blood_5th_percentile_exclude = np.percentile([population_data_exclude[entry]["T1_blood"] for entry in population_data_exclude], 5)
+    T1_blood_95th_percentile_exclude = np.percentile([population_data_exclude[entry]["T1_blood"] for entry in population_data_exclude], 95)
+except Exception as e:
+    print(e)
+    T1_blood_mean_exclude = -1
+    T1_blood_median_exclude = -1
+    T1_blood_std_exclude = -1
+    T1_blood_5th_percentile_exclude = -1
+    T1_blood_95th_percentile_exclude = -1
+
+try:
+    Ktrans_wm_mean = np.nanmean([population_data[entry]["Ktrans_wm_median"] for entry in population_data])
+    Ktrans_wm_median = np.nanmedian([population_data[entry]["Ktrans_wm_median"] for entry in population_data])
+    Ktrans_wm_std = np.nanstd([population_data[entry]["Ktrans_wm_median"] for entry in population_data])
+    Ktrans_gm_mean = np.nanmean([population_data[entry]["Ktrans_gm_median"] for entry in population_data])
+    Ktrans_gm_median = np.nanmedian([population_data[entry]["Ktrans_gm_median"] for entry in population_data])
+    Ktrans_gm_std = np.nanstd([population_data[entry]["Ktrans_gm_median"] for entry in population_data])
+except Exception as e:
+    print(e)
+    Ktrans_wm_mean = -1
+    Ktrans_wm_median = -1
+    Ktrans_wm_std = -1
+    Ktrans_gm_mean = -1
+    Ktrans_gm_median = -1
+    Ktrans_gm_std = -1
+
+try:
+    wm_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_wm_median"] for entry in population_data_exclude])
+    wm_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_wm_median"] for entry in population_data_exclude])
+    wm_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_wm_median"] for entry in population_data_exclude])
+    gm_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_gm_median"] for entry in population_data_exclude])
+    gm_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_gm_median"] for entry in population_data_exclude])
+    gm_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_gm_median"] for entry in population_data_exclude])
+except Exception as e:
+    print(e)
+    wm_mean_exclude = -1
+    wm_median_exclude = -1
+    wm_std_exclude = -1
+    gm_mean_exclude = -1
+    gm_median_exclude = -1
+    gm_std_exclude = -1
+
+whole_hippo_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Hippo_median"] for entry in population_data])
+whole_hippo_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Hippo_median"] for entry in population_data])
+whole_hippo_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Hippo_median"] for entry in population_data])
+
+whole_hippo_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Hippo_median"] for entry in population_data_exclude])
+whole_hippo_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Hippo_median"] for entry in population_data_exclude])
+whole_hippo_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Hippo_median"] for entry in population_data_exclude])
+
+whole_phg_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_PhG_median"] for entry in population_data])
+whole_phg_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_PhG_median"] for entry in population_data])
+whole_phg_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_PhG_median"] for entry in population_data])
+
+whole_phg_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_PhG_median"] for entry in population_data_exclude])
+whole_phg_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_PhG_median"] for entry in population_data_exclude])
+whole_phg_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_PhG_median"] for entry in population_data_exclude])
+
+whole_putamen_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Putamen_median"] for entry in population_data])
+whole_putamen_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Putamen_median"] for entry in population_data])
+whole_putamen_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Putamen_median"] for entry in population_data])
+
+whole_putamen_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Putamen_median"] for entry in population_data_exclude])
+whole_putamen_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Putamen_median"] for entry in population_data_exclude])
+whole_putamen_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Putamen_median"] for entry in population_data_exclude])
+
+whole_pallidum_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Pallidum_median"] for entry in population_data])
+whole_pallidum_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Pallidum_median"] for entry in population_data])
+whole_pallidum_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Pallidum_median"] for entry in population_data])
+
+whole_pallidum_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Pallidum_median"] for entry in population_data_exclude])
+whole_pallidum_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Pallidum_median"] for entry in population_data_exclude])
+whole_pallidum_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Pallidum_median"] for entry in population_data_exclude])
+
+whole_thalamus_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Thalamus_median"] for entry in population_data])
+whole_thalamus_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Thalamus_median"] for entry in population_data])
+whole_thalamus_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Thalamus_median"] for entry in population_data])
+
+whole_thalamus_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Thalamus_median"] for entry in population_data_exclude])
+whole_thalamus_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Thalamus_median"] for entry in population_data_exclude])
+whole_thalamus_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Thalamus_median"] for entry in population_data_exclude])
+
+whole_caudate_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Caudate_median"] for entry in population_data])
+whole_caudate_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Caudate_median"] for entry in population_data])
+whole_caudate_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Caudate_median"] for entry in population_data])
+
+whole_caudate_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Caudate_median"] for entry in population_data_exclude])
+whole_caudate_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Caudate_median"] for entry in population_data_exclude])
+whole_caudate_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Caudate_median"] for entry in population_data_exclude])
+
+whole_amygdala_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Amygdala_median"] for entry in population_data])
+whole_amygdala_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Amygdala_median"] for entry in population_data])
+whole_amygdala_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Amygdala_median"] for entry in population_data])
+
+whole_amygdala_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Amygdala_median"] for entry in population_data_exclude])
+whole_amygdala_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Amygdala_median"] for entry in population_data_exclude])
+whole_amygdala_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Amygdala_median"] for entry in population_data_exclude])
+
+whole_entorhinal_cortex_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Entorhinal_cortex_median"] for entry in population_data])
+whole_entorhinal_cortex_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Entorhinal_cortex_median"] for entry in population_data])
+whole_entorhinal_cortex_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Entorhinal_cortex_median"] for entry in population_data])
+
+whole_entorhinal_cortex_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Entorhinal_cortex_median"] for entry in population_data_exclude])
+whole_entorhinal_cortex_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Entorhinal_cortex_median"] for entry in population_data_exclude])
+whole_entorhinal_cortex_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Entorhinal_cortex_median"] for entry in population_data_exclude])
+
+whole_fusiform_gyrus_cortex_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Fusiform_gyrus_cortex_median"] for entry in population_data])
+whole_fusiform_gyrus_cortex_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Fusiform_gyrus_cortex_median"] for entry in population_data])
+whole_fusiform_gyrus_cortex_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Fusiform_gyrus_cortex_median"] for entry in population_data])
+
+whole_fusiform_gyrus_cortex_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Fusiform_gyrus_cortex_median"] for entry in population_data_exclude])
+whole_fusiform_gyrus_cortex_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Fusiform_gyrus_cortex_median"] for entry in population_data_exclude])
+whole_fusiform_gyrus_cortex_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Fusiform_gyrus_cortex_median"] for entry in population_data_exclude])
+
+whole_fusiform_gyrus_WM_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Fusiform_gyrus_WM_median"] for entry in population_data])
+whole_fusiform_gyrus_WM_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Fusiform_gyrus_WM_median"] for entry in population_data])
+whole_fusiform_gyrus_WM_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Fusiform_gyrus_WM_median"] for entry in population_data])
+
+whole_fusiform_gyrus_WM_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Fusiform_gyrus_WM_median"] for entry in population_data_exclude])
+whole_fusiform_gyrus_WM_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Fusiform_gyrus_WM_median"] for entry in population_data_exclude])
+whole_fusiform_gyrus_WM_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Fusiform_gyrus_WM_median"] for entry in population_data_exclude])
+
+whole_insula_WM_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Insula_WM_median"] for entry in population_data])
+whole_insula_WM_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Insula_WM_median"] for entry in population_data])
+whole_insula_WM_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Insula_WM_median"] for entry in population_data])
+
+whole_insula_WM_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Insula_WM_median"] for entry in population_data_exclude])
+whole_insula_WM_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Insula_WM_median"] for entry in population_data_exclude])
+whole_insula_WM_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Insula_WM_median"] for entry in population_data_exclude])
+
+whole_superior_temporal_cortex_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Superior_temporal_cortex_median"] for entry in population_data])
+whole_superior_temporal_cortex_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Superior_temporal_cortex_median"] for entry in population_data])
+whole_superior_temporal_cortex_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Superior_temporal_cortex_median"] for entry in population_data])
+
+whole_superior_temporal_cortex_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Superior_temporal_cortex_median"] for entry in population_data_exclude])
+whole_superior_temporal_cortex_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Superior_temporal_cortex_median"] for entry in population_data_exclude])
+whole_superior_temporal_cortex_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Superior_temporal_cortex_median"] for entry in population_data_exclude])
+
+whole_inferior_temporal_cortex_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Inferior_temporal_cortex_median"] for entry in population_data])
+whole_inferior_temporal_cortex_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Inferior_temporal_cortex_median"] for entry in population_data])
+whole_inferior_temporal_cortex_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Inferior_temporal_cortex_median"] for entry in population_data])
+
+whole_inferior_temporal_cortex_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Inferior_temporal_cortex_median"] for entry in population_data_exclude])
+whole_inferior_temporal_cortex_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Inferior_temporal_cortex_median"] for entry in population_data_exclude])
+whole_inferior_temporal_cortex_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Inferior_temporal_cortex_median"] for entry in population_data_exclude])
+
+whole_posterior_cingulate_cortex_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Posterior_cingulate_cortex_median"] for entry in population_data])
+whole_posterior_cingulate_cortex_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Posterior_cingulate_cortex_median"] for entry in population_data])
+whole_posterior_cingulate_cortex_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Posterior_cingulate_cortex_median"] for entry in population_data])
+
+whole_posterior_cingulate_cortex_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Posterior_cingulate_cortex_median"] for entry in population_data_exclude])
+whole_posterior_cingulate_cortex_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Posterior_cingulate_cortex_median"] for entry in population_data_exclude])
+whole_posterior_cingulate_cortex_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Posterior_cingulate_cortex_median"] for entry in population_data_exclude])
+
+whole_medial_temporal_cortex_Ktrans_mean = np.nanmean([population_data[entry]["Ktrans_Medial_temporal_cortex_median"] for entry in population_data])
+whole_medial_temporal_cortex_Ktrans_median = np.nanmedian([population_data[entry]["Ktrans_Medial_temporal_cortex_median"] for entry in population_data])
+whole_medial_temporal_cortex_Ktrans_std = np.nanstd([population_data[entry]["Ktrans_Medial_temporal_cortex_median"] for entry in population_data])
+
+whole_medial_temporal_cortex_Ktrans_mean_exclude = np.nanmean([population_data_exclude[entry]["Ktrans_Medial_temporal_cortex_median"] for entry in population_data_exclude])
+whole_medial_temporal_cortex_Ktrans_median_exclude = np.nanmedian([population_data_exclude[entry]["Ktrans_Medial_temporal_cortex_median"] for entry in population_data_exclude])
+whole_medial_temporal_cortex_Ktrans_std_exclude = np.nanstd([population_data_exclude[entry]["Ktrans_Medial_temporal_cortex_median"] for entry in population_data_exclude])
+
+# if no outliers, set to "None"
+if len(Ktrans_wm_outliers) == 0:
+    Ktrans_wm_outliers = "None"
+if len(Ktrans_gm_outliers) == 0:
+    Ktrans_gm_outliers = "None"
+
+# make figures directory if it doesn't exist
+if not os.path.exists(os.path.join("figures/")):
+    os.makedirs(os.path.join("figures/"))
+
+# make T1 blood histogram
+T1_blood_histogram = []
+for entry in population_data.keys():
+    T1_blood_histogram.append(population_data[entry]["T1_blood"])
+
+date_filename = datetime.datetime.now().strftime("%Y-%m-%d")
+
+# plot histogram
+plt.hist(T1_blood_histogram, bins=30)
+plt.title("T1 Blood")
+plt.xlabel("T1 Blood")
+T1_blood_histogram_path = os.path.join("figures/", "T1_blood_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(T1_blood_histogram_path, bbox_inches='tight')
+plt.close()
+
+T1_blood_histogram_exclude = []
+for entry in population_data_exclude.keys():
+    T1_blood_histogram_exclude.append(population_data_exclude[entry]["T1_blood"])
+
+# plot histogram
+plt.hist(T1_blood_histogram_exclude, bins=30)
+plt.title("T1 Blood (Exclude)")
+plt.xlabel("T1 Blood")
+T1_blood_histogram_exclude_path = os.path.join("figures/", "T1_blood_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(T1_blood_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+# make AIFitness histogram
+plt.hist(AIFitness_values, bins=30)
+plt.title("AIFitness Median")
+plt.xlabel("AIFitness")
+aifitness_histogram_path = os.path.join("figures/", "aifitness_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(aifitness_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(AIFitness_exclude, bins=30)
+plt.title("AIFitness Median (Exclude)")
+plt.xlabel("AIFitness")
+aifitness_histogram_exclude_path = os.path.join("figures/", "aifitness_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(aifitness_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+# make aif_mmol histogram
+aif_mmol_histogram = []
+for entry in population_data.keys():
+    aif_mmol_histogram.append(population_data[entry]["aif_mmol"])
+
+# plot histogram
+plt.hist(aif_mmol_histogram, bins=30)
+plt.title("AIF mmol (mean of last 1/3)")
+plt.xlabel("AIF mmol")
+aif_mmol_histogram_path = os.path.join("figures/", "aif_mmol_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(aif_mmol_histogram_path, bbox_inches='tight')
+plt.close()
+
+aif_mmol_histogram_exclude = []
+for entry in population_data_exclude.keys():
+    aif_mmol_histogram_exclude.append(population_data_exclude[entry]["aif_mmol"])
+
+# plot histogram
+plt.hist(aif_mmol_histogram_exclude, bins=30)
+plt.title("AIF mmol (mean of last 1/3) (Exclude)")
+plt.xlabel("AIF mmol")
+aif_mmol_histogram_exclude_path = os.path.join("figures/", "aif_mmol_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(aif_mmol_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+# make ktrans histograms from each timepoint mean
+wm_histogram = []
+for entry in population_data.keys():
+    wm_histogram.append(population_data[entry]["Ktrans_wm_median"])
+
+# plot histogram
+plt.hist(wm_histogram, bins=50, range=(0, 5))
+plt.title("White Matter Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+ktrans_wm_histogram_path = os.path.join("figures/", "wm_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(ktrans_wm_histogram_path, bbox_inches='tight')
+plt.close()
+
+ktrans_wm_histogram_exclude = []
+for entry in population_data_exclude.keys():
+    ktrans_wm_histogram_exclude.append(population_data_exclude[entry]["Ktrans_wm_median"])
+
+# plot histogram
+plt.hist(ktrans_wm_histogram_exclude, bins=50, range=(0, 5))
+plt.title("White Matter Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+ktrans_wm_histogram_exclude_path = os.path.join("figures/", "wm_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(ktrans_wm_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+# now get gm mean histogram
+gm_histogram = []
+for entry in population_data.keys():
+    gm_histogram.append(population_data[entry]["Ktrans_gm_median"])
+
+# plot histogram
+plt.hist(gm_histogram, bins=50, range=(0, 5))
+plt.title("Gray Matter Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+ktrans_gm_histogram_path = os.path.join("figures/", "gm_histogram" + output_dir + "_" + date_filename + ".png")
+# save range of histogram for later use
+gm_histogram_range = plt.xlim()
+plt.savefig(ktrans_gm_histogram_path, bbox_inches='tight')
+plt.close()
+
+ktrans_gm_histogram_exclude = []
+for entry in population_data_exclude.keys():
+    ktrans_gm_histogram_exclude.append(population_data_exclude[entry]["Ktrans_gm_median"])
+
+# plot histogram
+plt.hist(ktrans_gm_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Gray Matter Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+ktrans_gm_histogram_exclude_path = os.path.join("figures/", "gm_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.xlim(gm_histogram_range)
+plt.savefig(ktrans_gm_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+try:
+    pop_avg_AIF = np.asarray(popAIF_curves)
+    pop_avg_AIF = np.mean(pop_avg_AIF, axis=0)
+    # save average curve to export into MATLAB
+    np.savetxt("average_aif_curve.csv", pop_avg_AIF, delimiter=",")
+    for aif in popAIF_curves:
+        plt.plot(aif, linewidth=0.5, color='grey', alpha=0.5)
+    plt.plot(pop_avg_AIF, linewidth=1, color='black')
+    # plot stdev per timepoint
+    plt.fill_between(np.arange(0, len(pop_avg_AIF)), pop_avg_AIF - np.std(popAIF_curves, axis=0), pop_avg_AIF + np.std(popAIF_curves, axis=0), alpha=0.3)
+    plt.xlabel('Time (s)')
+    plt.ylabel('Normalized Intensity')
+    plt.title('AIF Curves (Modified for MATLAB Import)')
+    aif_pop_avg_path = os.path.join("figures/", "aif_pop_avg_AIF" + output_dir + "_" + date_filename + ".png")
+    plt.savefig(aif_pop_avg_path, bbox_inches='tight', dpi=300)  # Increase dpi for higher resolution
+    plt.close()
+except Exception as e:
+    print("Error plotting population AIF curve.", e)
+    aif_pop_avg_path = "Error plotting population average AIF curve."
+
+try:
+    # plot true AIFs
+    aif_curves = np.asarray(aif_curves)
+    aif_curves_avg = np.nanmean(aif_curves, axis=0)
+    for aif in aif_curves:
+        plt.plot(aif, linewidth=0.5, color='grey', alpha=0.5)
+    plt.plot(aif_curves_avg, linewidth=1, color='black')
+    # plot 95% confidence interval
+    plt.fill_between(np.arange(0, len(aif_curves_avg)), aif_curves_avg - np.std(aif_curves, axis=0), aif_curves_avg + np.std(aif_curves, axis=0), alpha=0.3)
+    plt.xlabel('Time (s)')
+    plt.ylabel('Normalized Intensity')
+    plt.title('AIF Curves')
+    aif_curves_path = os.path.join("figures/", "aif_pop_AIF" + output_dir + "_" + date_filename + ".png")
+    plt.savefig(aif_curves_path, bbox_inches='tight', dpi=300)  # Increase dpi for higher resolution
+    plt.close()
+except Exception as e:
+    print("Error plotting population AIF curve.", e)
+    aif_curves_path = "Error plotting population AIF curves."
+
+Ktrans_histograms = {
+    "whole_hippo": [],
+    "whole_phg": [],
+    "whole_putamen": [],
+    "whole_pallidum": [],
+    "whole_thalamus": [],
+    "whole_caudate": [],
+    "whole_amygdala": [],
+    "whole_entorhinal_cortex": [],
+    "whole_fusiform_gyrus_cortex": [],
+    "whole_fusiform_gyrus_WM": [],
+    "whole_insula_WM": [],
+    "whole_superior_temporal_cortex": [],
+    "whole_inferior_temporal_cortex": [],
+    "whole_posterior_cingulate_cortex": [],
+    "whole_medial_temporal_cortex": []
+}
+
+Vp_histograms = {
+    "whole_hippo": [],
+    "whole_phg": [],
+    "whole_putamen": [],
+    "whole_pallidum": [],
+    "whole_thalamus": [],
+    "whole_caudate": [],
+    "whole_amygdala": [],
+    "whole_entorhinal_cortex": [],
+    "whole_fusiform_gyrus_cortex": [],
+    "whole_fusiform_gyrus_WM": [],
+    "whole_insula_WM": [],
+    "whole_superior_temporal_cortex": [],
+    "whole_inferior_temporal_cortex": [],
+    "whole_posterior_cingulate_cortex": [],
+    "whole_medial_temporal_cortex": []
+}
+for entry in population_data.keys():
+    Ktrans_histograms["whole_hippo"].append(population_data[entry]["Ktrans_Hippo_median"])
+    Ktrans_histograms["whole_phg"].append(population_data[entry]["Ktrans_PhG_median"])
+    Ktrans_histograms["whole_putamen"].append(population_data[entry]["Ktrans_Putamen_median"])
+    Ktrans_histograms["whole_pallidum"].append(population_data[entry]["Ktrans_Pallidum_median"])
+    Ktrans_histograms["whole_thalamus"].append(population_data[entry]["Ktrans_Thalamus_median"])
+    Ktrans_histograms["whole_caudate"].append(population_data[entry]["Ktrans_Caudate_median"])
+    Ktrans_histograms["whole_amygdala"].append(population_data[entry]["Ktrans_Amygdala_median"])
+    Ktrans_histograms["whole_entorhinal_cortex"].append(population_data[entry]["Ktrans_Entorhinal_cortex_median"])
+    Ktrans_histograms["whole_fusiform_gyrus_cortex"].append(population_data[entry]["Ktrans_Fusiform_gyrus_cortex_median"])
+    Ktrans_histograms["whole_fusiform_gyrus_WM"].append(population_data[entry]["Ktrans_Fusiform_gyrus_WM_median"])
+    Ktrans_histograms["whole_insula_WM"].append(population_data[entry]["Ktrans_Insula_WM_median"])
+    Ktrans_histograms["whole_superior_temporal_cortex"].append(population_data[entry]["Ktrans_Superior_temporal_cortex_median"])
+    Ktrans_histograms["whole_inferior_temporal_cortex"].append(population_data[entry]["Ktrans_Inferior_temporal_cortex_median"])
+    Ktrans_histograms["whole_posterior_cingulate_cortex"].append(population_data[entry]["Ktrans_Posterior_cingulate_cortex_median"])
+    Ktrans_histograms["whole_medial_temporal_cortex"].append(population_data[entry]["Ktrans_Medial_temporal_cortex_median"])
+    Vp_histograms["whole_hippo"].append(population_data[entry]["Vp_Hippo_median"])
+    Vp_histograms["whole_phg"].append(population_data[entry]["Vp_PhG_median"])
+    Vp_histograms["whole_putamen"].append(population_data[entry]["Vp_Putamen_median"])
+    Vp_histograms["whole_pallidum"].append(population_data[entry]["Vp_Pallidum_median"])
+    Vp_histograms["whole_thalamus"].append(population_data[entry]["Vp_Thalamus_median"])
+    Vp_histograms["whole_caudate"].append(population_data[entry]["Vp_Caudate_median"])
+    Vp_histograms["whole_amygdala"].append(population_data[entry]["Vp_Amygdala_median"])
+    Vp_histograms["whole_entorhinal_cortex"].append(population_data[entry]["Vp_Entorhinal_cortex_median"])
+    Vp_histograms["whole_fusiform_gyrus_cortex"].append(population_data[entry]["Vp_Fusiform_gyrus_cortex_median"])
+    Vp_histograms["whole_fusiform_gyrus_WM"].append(population_data[entry]["Vp_Fusiform_gyrus_WM_median"])
+    Vp_histograms["whole_insula_WM"].append(population_data[entry]["Vp_Insula_WM_median"])
+    Vp_histograms["whole_superior_temporal_cortex"].append(population_data[entry]["Vp_Superior_temporal_cortex_median"])
+    Vp_histograms["whole_inferior_temporal_cortex"].append(population_data[entry]["Vp_Inferior_temporal_cortex_median"])
+    Vp_histograms["whole_posterior_cingulate_cortex"].append(population_data[entry]["Vp_Posterior_cingulate_cortex_median"])
+    Vp_histograms["whole_medial_temporal_cortex"].append(population_data[entry]["Vp_Medial_temporal_cortex_median"])
+
+whole_hippo_histogram_exclude = []
+whole_phg_histogram_exclude = []
+whole_putamen_histogram_exclude = []
+whole_pallidum_histogram_exclude = []
+whole_thalamus_histogram_exclude = []
+whole_caudate_histogram_exclude = []
+whole_amygdala_histogram_exclude = []
+whole_entorhinal_cortex_histogram_exclude = []
+whole_fusiform_gyrus_cortex_histogram_exclude = []
+whole_fusiform_gyrus_WM_histogram_exclude = []
+whole_insula_WM_histogram_exclude = []
+whole_superior_temporal_cortex_histogram_exclude = []
+whole_inferior_temporal_cortex_histogram_exclude = []
+whole_posterior_cingulate_cortex_histogram_exclude = []
+whole_medial_temporal_cortex_histogram_exclude = []
+for entry in population_data_exclude.keys():
+    whole_hippo_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Hippo_median"])
+    whole_phg_histogram_exclude.append(population_data_exclude[entry]["Ktrans_PhG_median"])
+    whole_putamen_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Putamen_median"])
+    whole_pallidum_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Pallidum_median"])
+    whole_thalamus_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Thalamus_median"])
+    whole_caudate_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Caudate_median"])
+    whole_amygdala_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Amygdala_median"])
+    whole_entorhinal_cortex_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Entorhinal_cortex_median"])
+    whole_fusiform_gyrus_cortex_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Fusiform_gyrus_cortex_median"])
+    whole_fusiform_gyrus_WM_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Fusiform_gyrus_WM_median"])
+    whole_insula_WM_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Insula_WM_median"])
+    whole_superior_temporal_cortex_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Superior_temporal_cortex_median"])
+    whole_inferior_temporal_cortex_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Inferior_temporal_cortex_median"])
+    whole_posterior_cingulate_cortex_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Posterior_cingulate_cortex_median"])
+    whole_medial_temporal_cortex_histogram_exclude.append(population_data_exclude[entry]["Ktrans_Medial_temporal_cortex_median"])
+
+plt.hist(Ktrans_histograms["whole_hippo"], bins=50, range=(0, 5))
+plt.title("Whole Hippocampus Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_hippo_histogram_path = os.path.join("figures/", "Ktrans_whole_hippo_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_hippo_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_phg"], bins=50, range=(0, 5))
+plt.title("Whole Parahippocampal Gyrus Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_phg_histogram_path = os.path.join("figures/", "Ktrans_whole_phg_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_phg_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_putamen"], bins=50, range=(0, 5))
+plt.title("Whole Putamen Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_putamen_histogram_path = os.path.join("figures/", "Ktrans_whole_putamen_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_putamen_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_pallidum"], bins=50, range=(0, 5))
+plt.title("Whole Pallidum Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_pallidum_histogram_path = os.path.join("figures/", "Ktrans_whole_pallidum_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_pallidum_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_thalamus"], bins=50, range=(0, 5))
+plt.title("Whole Thalamus Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_thalamus_histogram_path = os.path.join("figures/", "Ktrans_whole_thalamus_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_thalamus_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_caudate"], bins=50, range=(0, 5))
+plt.title("Whole Caudate Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_caudate_histogram_path = os.path.join("figures/", "Ktrans_whole_caudate_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_caudate_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_amygdala"], bins=50, range=(0, 5))
+plt.title("Whole Amygdala Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_amygdala_histogram_path = os.path.join("figures/", "Ktrans_whole_amygdala_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_amygdala_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_entorhinal_cortex"], bins=50, range=(0, 5))
+plt.title("Whole Entorhinal Cortex Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_entorhinal_cortex_histogram_path = os.path.join("figures/", "Ktrans_whole_entorhinal_cortex_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_entorhinal_cortex_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_fusiform_gyrus_cortex"], bins=50, range=(0, 5))
+plt.title("Whole Fusiform Gyrus Cortex Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_fusiform_gyrus_cortex_histogram_path = os.path.join("figures/", "Ktrans_whole_fusiform_gyrus_cortex_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_fusiform_gyrus_cortex_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_fusiform_gyrus_WM"], bins=50, range=(0, 5))
+plt.title("Whole Fusiform Gyrus WM Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_fusiform_gyrus_WM_histogram_path = os.path.join("figures/", "Ktrans_whole_fusiform_gyrus_WM_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_fusiform_gyrus_WM_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_insula_WM"], bins=50, range=(0, 5))
+plt.title("Whole Insula WM Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_insula_WM_histogram_path = os.path.join("figures/", "Ktrans_whole_insula_WM_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_insula_WM_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_superior_temporal_cortex"], bins=50, range=(0, 5))
+plt.title("Whole Superior Temporal Cortex Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_superior_temporal_cortex_histogram_path = os.path.join("figures/", "Ktrans_whole_superior_temporal_cortex_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_superior_temporal_cortex_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_inferior_temporal_cortex"], bins=50, range=(0, 5))
+plt.title("Whole Inferior Temporal Cortex Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_inferior_temporal_cortex_histogram_path = os.path.join("figures/", "Ktrans_whole_inferior_temporal_cortex_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_inferior_temporal_cortex_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_posterior_cingulate_cortex"], bins=50, range=(0, 5))
+plt.title("Whole Posterior Cingulate Cortex Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_posterior_cingulate_cortex_histogram_path = os.path.join("figures/", "Ktrans_whole_posterior_cingulate_cortex_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_posterior_cingulate_cortex_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(Ktrans_histograms["whole_medial_temporal_cortex"], bins=50, range=(0, 5))
+plt.title("Whole Medial Temporal Cortex Median Ktrans")
+plt.xlabel("Ktrans (10^-3/min)")
+Ktrans_whole_medial_temporal_cortex_histogram_path = os.path.join("figures/", "Ktrans_whole_medial_temporal_cortex_histogram" + output_dir + "_" + date_filename + ".png")
+plt.savefig(Ktrans_whole_medial_temporal_cortex_histogram_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_hippo_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Hippocampus Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_hippo_histogram_exclude_path = os.path.join("figures/", "whole_hippo_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_hippo_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_phg_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Parahippocampal Gyrus Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_phg_histogram_exclude_path = os.path.join("figures/", "whole_phg_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_phg_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_putamen_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Putamen Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_putamen_histogram_exclude_path = os.path.join("figures/", "whole_putamen_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_putamen_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_pallidum_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Pallidum Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_pallidum_histogram_exclude_path = os.path.join("figures/", "whole_pallidum_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_pallidum_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_thalamus_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Thalamus Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_thalamus_histogram_exclude_path = os.path.join("figures/", "whole_thalamus_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_thalamus_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_caudate_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Caudate Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_caudate_histogram_exclude_path = os.path.join("figures/", "whole_caudate_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_caudate_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_amygdala_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Amygdala Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_amygdala_histogram_exclude_path = os.path.join("figures/", "whole_amygdala_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_amygdala_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_entorhinal_cortex_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Entorhinal Cortex Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_entorhinal_cortex_histogram_exclude_path = os.path.join("figures/", "whole_entorhinal_cortex_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_entorhinal_cortex_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_fusiform_gyrus_cortex_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Fusiform Gyrus Cortex Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_fusiform_gyrus_cortex_histogram_exclude_path = os.path.join("figures/", "whole_fusiform_gyrus_cortex_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_fusiform_gyrus_cortex_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_fusiform_gyrus_WM_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Fusiform Gyrus WM Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_fusiform_gyrus_WM_histogram_exclude_path = os.path.join("figures/", "whole_fusiform_gyrus_WM_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_fusiform_gyrus_WM_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_insula_WM_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Insula WM Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_insula_WM_histogram_exclude_path = os.path.join("figures/", "whole_insula_WM_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_insula_WM_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_superior_temporal_cortex_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Superior Temporal Cortex Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_superior_temporal_cortex_histogram_exclude_path = os.path.join("figures/", "whole_superior_temporal_cortex_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_superior_temporal_cortex_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_inferior_temporal_cortex_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Inferior Temporal Cortex Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_inferior_temporal_cortex_histogram_exclude_path = os.path.join("figures/", "whole_inferior_temporal_cortex_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_inferior_temporal_cortex_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_posterior_cingulate_cortex_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Posterior Cingulate Cortex Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_posterior_cingulate_cortex_histogram_exclude_path = os.path.join("figures/", "whole_posterior_cingulate_cortex_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_posterior_cingulate_cortex_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+plt.hist(whole_medial_temporal_cortex_histogram_exclude, bins=50, range=(0, 5))
+plt.title("Whole Medial Temporal Cortex Median Ktrans (Exclude)")
+plt.xlabel("Ktrans (10^-3/min)")
+whole_medial_temporal_cortex_histogram_exclude_path = os.path.join("figures/", "whole_medial_temporal_cortex_histogram_exclude" + output_dir + "_" + date_filename + ".png")
+plt.savefig(whole_medial_temporal_cortex_histogram_exclude_path, bbox_inches='tight')
+plt.close()
+
+# round to 4 decimal places
+Ktrans_wm_mean = round(Ktrans_wm_mean, 4)
+Ktrans_wm_median = round(Ktrans_wm_median, 4)
+Ktrans_wm_std = round(Ktrans_wm_std, 4)
+Ktrans_gm_mean = round(Ktrans_gm_mean, 4)
+Ktrans_gm_median = round(Ktrans_gm_median, 4)
+Ktrans_gm_std = round(Ktrans_gm_std, 4)
+
+wm_mean_exclude = round(wm_mean_exclude, 4)
+wm_median_exclude = round(wm_median_exclude, 4)
+wm_std_exclude = round(wm_std_exclude, 4)
+gm_mean_exclude = round(gm_mean_exclude, 4)
+gm_median_exclude = round(gm_median_exclude, 4)
+gm_std_exclude = round(gm_std_exclude, 4)
+
+# use jinja2 to generate html
+env = jinja2.Environment(loader=jinja2.FileSystemLoader(os.path.dirname(os.path.realpath(__file__))))
+template = env.get_template('population_template.html')
+
+# get date
+date = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+# get commit hash for this repo
+try:
+    commit_hash = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=os.path.dirname(os.path.realpath(__file__))).decode('ascii').strip()
+except Exception as e:
+    print("Git didn't work correctly. Trying a different way of getting latest dev branch commit hash...")
+    command = ['cat', '.git/refs/heads/dev']
+    commit_hash = subprocess.check_output(command, cwd=os.path.dirname(os.path.realpath(__file__))).decode('ascii').strip()
+
+# get commit hash for ROCKETSHIP repo
+try:
+    ROCKETSHIP_commit_hash = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=ROCKETSHIP_dir).decode('ascii').strip()
+except Exception as e:
+    print("Could not get ROCKETSHIP commit hash:", e)
+    ROCKETSHIP_commit_hash = "N/A"
+
+# make dict of manufacturer, field strength, machine, and institution
+manufacturers = {}
+field_strengths = {}
+machines = {}
+institutions = {}
+try:
+    for entry in population_data.keys():
+        manufacturer = population_data[entry]["Manufacturer"]
+        field_strength = population_data[entry]["Field_strength"]
+        machine = population_data[entry]["Machine"]
+        institution = population_data[entry]["Institution"]
+        if manufacturer in manufacturers.keys():
+            manufacturers[manufacturer] += 1
+        else:
+            manufacturers[manufacturer] = 1
+        if field_strength in field_strengths.keys():
+            field_strengths[field_strength] += 1
+        else:
+            field_strengths[field_strength] = 1
+        if machine in machines.keys():
+            machines[machine] += 1
+        else:
+            machines[machine] = 1
+        if institution in institutions.keys():
+            institutions[institution] += 1
+        else:
+            institutions[institution] = 1
+except Exception as e:
+    print("Error in getting manufacturer, field strength, machine, and institution data.")
+    print(e)
+
+manufacturers_exclude = {}
+field_strengths_exclude = {}
+machines_exclude = {}
+institutions_exclude = {}
+try:
+    for entry in population_data_exclude.keys():
+        manufacturer = population_data_exclude[entry]["Manufacturer"]
+        field_strength = population_data_exclude[entry]["Field_strength"]
+        machine = population_data_exclude[entry]["Machine"]
+        institution = population_data_exclude[entry]["Institution"]
+        if manufacturer in manufacturers_exclude.keys():
+            manufacturers_exclude[manufacturer] += 1
+        else:
+            manufacturers_exclude[manufacturer] = 1
+        if field_strength in field_strengths_exclude.keys():
+            field_strengths_exclude[field_strength] += 1
+        else:
+            field_strengths_exclude[field_strength] = 1
+        if machine in machines_exclude.keys():
+            machines_exclude[machine] += 1
+        else:
+            machines_exclude[machine] = 1
+        if institution in institutions_exclude.keys():
+            institutions_exclude[institution] += 1
+        else:
+            institutions_exclude[institution] = 1
+except Exception as e:
+    print("Error in getting manufacturer, field strength, machine, and institution data.")
+    print(e)
+
+successful_timepoints = list(set(successful_timepoints))
+successful_timepoints.sort()
+# num_timepoints = len(successful_timepoints)
+# remove output_dir from successful_timepoints
+cases = [timepoint for timepoint in successful_timepoints]
+successful_links = []
+for timepoint in successful_timepoints:
+    subject = timepoint.split('/')[0]
+    session = timepoint.split('/')[1]
+    successful_links.append(f"dceprep{output_dir}/{subject}/{session}/reports/{subject}_{session}_desc-casereport.html")
+# get failed cases from total_timepoints not in successful_timepoints
+failed_cases = [timepoint for timepoint in total_timepoints if timepoint not in successful_timepoints]
+# get links for each failed case's directory
+failed_links = [os.path.join(dceprep_dir, case) for case in failed_cases]
+
+# read ROCKETSHIP preference file
+with open(ROCKETSHIP_dir + "/script_preferences.txt", "r") as f:
+    lines = f.readlines()
+    for line in lines:
+        if "tr =" in line:
+            pref_tr = line.split("= ")[1].strip()
+        elif "fa =" in line:
+            pref_fa = line.split("= ")[1].strip()
+        elif "hematocrit =" in line:
+            pref_hematocrit = line.split("= ")[1].strip()
+        elif "snr_filter =" in line:
+            pref_SNR = line.split("= ")[1].strip()
+        elif "relaxivity =" in line and "pref_relaxivity" not in locals():
+            pref_relaxivity = line.split("= ")[1].strip()
+        elif "blood_t1 =" in line:
+            pref_t1blood = line.split("= ")[1].strip()
+        elif "start_t =" in line:
+            pref_start_t = line.split("= ")[1].strip()
+        elif "end_t =" in line:
+            pref_end_t = line.split("= ")[1].strip()
+        elif "time_resolution =" in line:
+            pref_timeres = line.split("= ")[1].strip()
+        elif "tofts = 1" in line:
+            DCE_model = "Tofts"
+        elif "ex_tofts = 1" in line:
+            DCE_model = "Tofts Extended"
+        elif "patlak = 1" in line:
+            DCE_model = "Patlak"
+        elif "tissue_uptake = 1" in line:
+            DCE_model = "Tissue Uptake"
+        elif "two_cxm = 1" in line:
+            DCE_model = "Two Compartment Exchange"
+
+data = {
+    'Subjects' : subjects,
+    # 'base_url': dceprep_dir,
+    'Links': successful_links,
+    'Failed_links': failed_links,
+    'Cases': cases,
+    'Failed_cases': failed_cases,
+    'Flagged_cases': flagged_cases,
+    'Combo': zip(successful_links, cases),
+    'Failed_combo': zip(failed_links, failed_cases),
+    'Motion_threshold': MOTION_THRESHOLD,
+    'AIFitness_threshold': AIFITNESS_THRESHOLD,
+    'Flagged_combo': zip(flagged_links, flagged_cases),
+    'Subject_count': len(subjects),
+    'Successes': str(len(population_data)) + '/' + str(len(total_timepoints)) + ' (' + str(round((len(population_data) / len(total_timepoints)) * 100, 2)) + '%)',
+    'Excludes': str(len(population_data_exclude)) + '/' + str(len(total_timepoints)) + ' (' + str(round((len(population_data_exclude) / len(total_timepoints)) * 100, 2)) + '%)',
+    'Date': date,
+    'Commit': commit_hash,
+    'ROCKETSHIP_commit': ROCKETSHIP_commit_hash,
+    'Manufacturers': manufacturers,
+    'Field_strengths': field_strengths,
+    'Machines': machines,
+    'Institutions': institutions,
+    'pref_tr': pref_tr,
+    'pref_fa': pref_fa,
+    'pref_hematocrit': pref_hematocrit,
+    'pref_SNR': pref_SNR,
+    'pref_relaxivity': pref_relaxivity,
+    'pref_t1blood': pref_t1blood,
+    'pref_start_t': pref_start_t,
+    'pref_end_t': pref_end_t,
+    'pref_timeres': pref_timeres,
+    'pref_model': DCE_model,
+    'T1_wm_mean': round(T1_wm_mean, 4),
+    'T1_wm_median': round(T1_wm_median, 4),
+    'T1_wm_std': round(T1_wm_std, 4),
+    'T1_wm_5th_percentile': round(T1_wm_5th_percentile, 4),
+    'T1_wm_95th_percentile': round(T1_wm_95th_percentile, 4),
+    'T1_gm_mean': round(T1_gm_mean, 4),
+    'T1_gm_median': round(T1_gm_median, 4),
+    'T1_gm_std': round(T1_gm_std, 4),
+    'T1_gm_5th_percentile': round(T1_gm_5th_percentile, 4),
+    'T1_gm_95th_percentile': round(T1_gm_95th_percentile, 4),
+    'T1_blood_mean': round(T1_blood_mean, 4),
+    'T1_blood_median': round(T1_blood_median, 4),
+    'T1_blood_std': round(T1_blood_std, 4),
+    'T1_blood_5th_percentile': round(T1_blood_5th_percentile, 4),
+    'T1_blood_95th_percentile': round(T1_blood_95th_percentile, 4),
+    'AIFitness_mean': round(AIFitness_mean, 4),
+    'AIFitness_median': round(AIFitness_median, 4),
+    'AIFitness_std': round(AIFitness_std, 4),
+    'AIFitness_5th_percentile': round(AIFitness_5th_percentile, 4),
+    'aif_mmol_mean': round(aif_mmol_mean, 4),
+    'aif_mmol_median': round(aif_mmol_median, 4),
+    'aif_mmol_std': round(aif_mmol_std, 4),
+    'aif_mmol_5th_percentile': round(aif_mmol_5th_percentile, 4),
+    'aif_mmol_95th_percentile': round(aif_mmol_95th_percentile, 4),
+    'AIFitness_histogram' : "../../" + aifitness_histogram_path,
+    'aif_mmol_histogram': "../../" + aif_mmol_histogram_path,
+    'aif_pop_avg_AIF': "../../" + aif_pop_avg_path,
+    'aif_curves': "../../" + aif_curves_path,
+    'Ktrans_wm_mean': Ktrans_wm_mean,
+    'Ktrans_wm_median': Ktrans_wm_median,
+    'Ktrans_wm_std': Ktrans_wm_std,
+    'Ktrans_gm_mean': Ktrans_gm_mean,
+    'Ktrans_gm_median': Ktrans_gm_median,
+    'Ktrans_gm_std': Ktrans_gm_std,
+    'whole_hippo_Ktrans_mean': round(whole_hippo_Ktrans_mean, 4),
+    'whole_hippo_Ktrans_median': round(whole_hippo_Ktrans_median, 4),
+    'whole_hippo_Ktrans_std': round(whole_hippo_Ktrans_std, 4),
+    'whole_phg_Ktrans_mean': round(whole_phg_Ktrans_mean, 4),
+    'whole_phg_Ktrans_median': round(whole_phg_Ktrans_median, 4),
+    'whole_phg_Ktrans_std': round(whole_phg_Ktrans_std, 4),
+    'whole_putamen_Ktrans_mean': round(whole_putamen_Ktrans_mean, 4),
+    'whole_putamen_Ktrans_median': round(whole_putamen_Ktrans_median, 4),
+    'whole_putamen_Ktrans_std': round(whole_putamen_Ktrans_std, 4),
+    'whole_pallidum_Ktrans_mean': round(whole_pallidum_Ktrans_mean, 4),
+    'whole_pallidum_Ktrans_median': round(whole_pallidum_Ktrans_median, 4),
+    'whole_pallidum_Ktrans_std': round(whole_pallidum_Ktrans_std, 4),
+    'whole_thalamus_Ktrans_mean': round(whole_thalamus_Ktrans_mean, 4),
+    'whole_thalamus_Ktrans_median': round(whole_thalamus_Ktrans_median, 4),
+    'whole_thalamus_Ktrans_std': round(whole_thalamus_Ktrans_std, 4),
+    'whole_caudate_Ktrans_mean': round(whole_caudate_Ktrans_mean, 4),
+    'whole_caudate_Ktrans_median': round(whole_caudate_Ktrans_median, 4),
+    'whole_caudate_Ktrans_std': round(whole_caudate_Ktrans_std, 4),
+    'whole_amygdala_Ktrans_mean': round(whole_amygdala_Ktrans_mean, 4),
+    'whole_amygdala_Ktrans_median': round(whole_amygdala_Ktrans_median, 4),
+    'whole_amygdala_Ktrans_std': round(whole_amygdala_Ktrans_std, 4),
+    'whole_entorhinal_cortex_Ktrans_mean': round(whole_entorhinal_cortex_Ktrans_mean, 4),
+    'whole_entorhinal_cortex_Ktrans_median': round(whole_entorhinal_cortex_Ktrans_median, 4),
+    'whole_entorhinal_cortex_Ktrans_std': round(whole_entorhinal_cortex_Ktrans_std, 4),
+    'whole_fusiform_gyrus_cortex_Ktrans_mean': round(whole_fusiform_gyrus_cortex_Ktrans_mean, 4),
+    'whole_fusiform_gyrus_cortex_Ktrans_median': round(whole_fusiform_gyrus_cortex_Ktrans_median, 4),
+    'whole_fusiform_gyrus_cortex_Ktrans_std': round(whole_fusiform_gyrus_cortex_Ktrans_std, 4),
+    'whole_fusiform_gyrus_WM_Ktrans_mean': round(whole_fusiform_gyrus_WM_Ktrans_mean, 4),
+    'whole_fusiform_gyrus_WM_Ktrans_median': round(whole_fusiform_gyrus_WM_Ktrans_median, 4),
+    'whole_fusiform_gyrus_WM_Ktrans_std': round(whole_fusiform_gyrus_WM_Ktrans_std, 4),
+    'whole_insula_WM_Ktrans_mean': round(whole_insula_WM_Ktrans_mean, 4),
+    'whole_insula_WM_Ktrans_median': round(whole_insula_WM_Ktrans_median, 4),
+    'whole_insula_WM_Ktrans_std': round(whole_insula_WM_Ktrans_std, 4),
+    'whole_superior_temporal_cortex_Ktrans_mean': round(whole_superior_temporal_cortex_Ktrans_mean, 4),
+    'whole_superior_temporal_cortex_Ktrans_median': round(whole_superior_temporal_cortex_Ktrans_median, 4),
+    'whole_superior_temporal_cortex_Ktrans_std': round(whole_superior_temporal_cortex_Ktrans_std, 4),
+    'whole_inferior_temporal_cortex_Ktrans_mean': round(whole_inferior_temporal_cortex_Ktrans_mean, 4),
+    'whole_inferior_temporal_cortex_Ktrans_median': round(whole_inferior_temporal_cortex_Ktrans_median, 4),
+    'whole_inferior_temporal_cortex_Ktrans_std': round(whole_inferior_temporal_cortex_Ktrans_std, 4),
+    'whole_posterior_cingulate_cortex_Ktrans_mean': round(whole_posterior_cingulate_cortex_Ktrans_mean, 4),
+    'whole_posterior_cingulate_cortex_Ktrans_median': round(whole_posterior_cingulate_cortex_Ktrans_median, 4),
+    'whole_posterior_cingulate_cortex_Ktrans_std': round(whole_posterior_cingulate_cortex_Ktrans_std, 4),
+    'whole_medial_temporal_cortex_Ktrans_mean': round(whole_medial_temporal_cortex_Ktrans_mean, 4),
+    'whole_medial_temporal_cortex_Ktrans_median': round(whole_medial_temporal_cortex_Ktrans_median, 4),
+    'whole_medial_temporal_cortex_Ktrans_std': round(whole_medial_temporal_cortex_Ktrans_std, 4),
+    'ktrans_wm_outliers': Ktrans_wm_outliers,
+    'ktrans_gm_outliers': Ktrans_gm_outliers,
+    'T1_blood_histogram': "../../" + T1_blood_histogram_path,
+    'wm_histogram': "../../" + ktrans_wm_histogram_path,
+    'gm_histogram': "../../" + ktrans_gm_histogram_path,
+    'whole_hippo_outliers': whole_hippo_outliers,
+    'whole_phg_outliers': whole_phg_outliers,
+    'whole_putamen_outliers': whole_putamen_outliers,
+    'whole_pallidum_outliers': whole_pallidum_outliers,
+    'whole_thalamus_outliers': whole_thalamus_outliers,
+    'whole_caudate_outliers': whole_caudate_outliers,
+    'whole_amygdala_outliers': whole_amygdala_outliers,
+    'whole_entorhinal_cortex_outliers': whole_entorhinal_cortex_outliers,
+    'whole_fusiform_gyrus_cortex_outliers': whole_fusiform_gyrus_cortex_outliers,
+    'whole_fusiform_gyrus_WM_outliers': whole_fusiform_gyrus_WM_outliers,
+    'whole_insula_WM_outliers': whole_insula_WM_outliers,
+    'whole_superior_temporal_cortex_outliers': whole_superior_temporal_cortex_outliers,
+    'whole_inferior_temporal_cortex_outliers': whole_inferior_temporal_cortex_outliers,
+    'whole_posterior_cingulate_cortex_outliers': whole_posterior_cingulate_cortex_outliers,
+    'whole_medial_temporal_cortex_outliers': whole_medial_temporal_cortex_outliers,
+    'Ktrans_whole_hippo_histogram': "../../" + Ktrans_whole_hippo_histogram_path,
+    'Ktrans_whole_phg_histogram': "../../" + Ktrans_whole_phg_histogram_path,
+    'Ktrans_whole_putamen_histogram': "../../" + Ktrans_whole_putamen_histogram_path,
+    'Ktrans_whole_pallidum_histogram': "../../" + Ktrans_whole_pallidum_histogram_path,
+    'Ktrans_whole_thalamus_histogram': "../../" + Ktrans_whole_thalamus_histogram_path,
+    'Ktrans_whole_caudate_histogram': "../../" + Ktrans_whole_caudate_histogram_path,
+    'Ktrans_whole_amygdala_histogram': "../../" + Ktrans_whole_amygdala_histogram_path,
+    'Ktrans_whole_entorhinal_cortex_histogram': "../../" + Ktrans_whole_entorhinal_cortex_histogram_path,
+    'Ktrans_whole_fusiform_gyrus_cortex_histogram': "../../" + Ktrans_whole_fusiform_gyrus_cortex_histogram_path,
+    'Ktrans_whole_fusiform_gyrus_WM_histogram': "../../" + Ktrans_whole_fusiform_gyrus_WM_histogram_path,
+    'Ktrans_whole_insula_WM_histogram': "../../" + Ktrans_whole_insula_WM_histogram_path,
+    'Ktrans_whole_superior_temporal_cortex_histogram': "../../" + Ktrans_whole_superior_temporal_cortex_histogram_path,
+    'Ktrans_whole_inferior_temporal_cortex_histogram': "../../" + Ktrans_whole_inferior_temporal_cortex_histogram_path,
+    'Ktrans_whole_posterior_cingulate_cortex_histogram': "../../" + Ktrans_whole_posterior_cingulate_cortex_histogram_path,
+    'Ktrans_whole_medial_temporal_cortex_histogram': "../../" + Ktrans_whole_medial_temporal_cortex_histogram_path
+}
+
+output = template.render(data)
+
+data = {
+    'Subjects' : subjects,
+    # 'base_url': dceprep_dir,
+    'Links': successful_links,
+    'Failed_links': failed_links,
+    'Cases': cases,
+    'Failed_cases': failed_cases,
+    'Flagged_cases': flagged_cases,
+    'Combo': zip(successful_links, cases),
+    'Failed_combo': zip(failed_links, failed_cases),
+    'Motion_threshold': MOTION_THRESHOLD,
+    'AIFitness_threshold': AIFITNESS_THRESHOLD,
+    'Flagged_combo': zip(flagged_links, flagged_cases),
+    'Subject_count': len(subjects),
+    'Successes': str(len(population_data)) + '/' + str(len(total_timepoints)) + ' (' + str(round((len(population_data) / len(total_timepoints)) * 100, 2)) + '%)',
+    'Excludes': str(len(population_data_exclude)) + '/' + str(len(total_timepoints)) + ' (' + str(round((len(population_data_exclude) / len(total_timepoints)) * 100, 2)) + '%)',
+    'Date': date,
+    'Commit': commit_hash,
+    'ROCKETSHIP_commit': ROCKETSHIP_commit_hash,
+    'Manufacturers': manufacturers_exclude,
+    'Field_strengths': field_strengths_exclude,
+    'Machines': machines,
+    'Institutions': institutions_exclude,
+    'pref_tr': pref_tr,
+    'pref_fa': pref_fa,
+    'pref_hematocrit': pref_hematocrit,
+    'pref_SNR': pref_SNR,
+    'pref_relaxivity': pref_relaxivity,
+    'pref_t1blood': pref_t1blood,
+    'pref_start_t': pref_start_t,
+    'pref_end_t': pref_end_t,
+    'pref_timeres': pref_timeres,
+    'pref_model': DCE_model,
+    'T1_wm_mean': round(T1_wm_mean_exclude, 4),
+    'T1_wm_median': round(T1_wm_median_exclude, 4),
+    'T1_wm_std': round(T1_wm_std_exclude, 4),
+    'T1_wm_5th_percentile': round(T1_wm_5th_percentile_exclude, 4),
+    'T1_wm_95th_percentile': round(T1_wm_95th_percentile_exclude, 4),
+    'T1_gm_mean': round(T1_gm_mean_exclude, 4),
+    'T1_gm_median': round(T1_gm_median_exclude, 4),
+    'T1_gm_std': round(T1_gm_std_exclude, 4),
+    'T1_gm_5th_percentile': round(T1_gm_5th_percentile_exclude, 4),
+    'T1_gm_95th_percentile': round(T1_gm_95th_percentile_exclude, 4),
+    'T1_blood_mean': round(T1_blood_mean_exclude, 4),
+    'T1_blood_median': round(T1_blood_median_exclude, 4),
+    'T1_blood_std': round(T1_blood_std_exclude, 4),
+    'T1_blood_5th_percentile': round(T1_blood_5th_percentile_exclude, 4),
+    'T1_blood_95th_percentile': round(T1_blood_95th_percentile_exclude, 4),
+    'AIFitness_mean': round(AIFitness_exclude_mean, 4),
+    'AIFitness_median': round(AIFitness_exclude_median, 4),
+    'AIFitness_std': round(AIFitness_exclude_std, 4),
+    'AIFitness_5th_percentile': round(AIFitness_exclude_5th_percentile, 4),
+    'aif_mmol_mean': round(aif_mmol_mean_exclude, 4),
+    'aif_mmol_median': round(aif_mmol_median_exclude, 4),
+    'aif_mmol_std': round(aif_mmol_std_exclude, 4),
+    'aif_mmol_5th_percentile': round(aif_mmol_5th_percentile_exclude, 4),
+    'aif_mmol_95th_percentile': round(aif_mmol_95th_percentile_exclude, 4),
+    'AIFitness_histogram' : "../../" + aifitness_histogram_exclude_path,
+    'aif_mmol_histogram': "../../" + aif_mmol_histogram_exclude_path,
+    'aif_pop_avg_AIF': "../../" + aif_pop_avg_path,
+    'aif_curves': "../../" + aif_curves_path,
+    'Ktrans_wm_mean': wm_mean_exclude,
+    'Ktrans_wm_median': wm_median_exclude,
+    'Ktrans_wm_std': wm_std_exclude,
+    'Ktrans_gm_mean': gm_mean_exclude,
+    'Ktrans_gm_median': gm_median_exclude,
+    'Ktrans_gm_std': gm_std_exclude,
+    'whole_hippo_Ktrans_mean': round(whole_hippo_Ktrans_mean_exclude, 4),
+    'whole_hippo_Ktrans_median': round(whole_hippo_Ktrans_median_exclude, 4),
+    'whole_hippo_Ktrans_std': round(whole_hippo_Ktrans_std_exclude, 4),
+    'whole_phg_Ktrans_mean': round(whole_phg_Ktrans_mean_exclude, 4),
+    'whole_phg_Ktrans_median': round(whole_phg_Ktrans_median_exclude, 4),
+    'whole_phg_Ktrans_std': round(whole_phg_Ktrans_std_exclude, 4),
+    'whole_putamen_Ktrans_mean': round(whole_putamen_Ktrans_mean_exclude, 4),
+    'whole_putamen_Ktrans_median': round(whole_putamen_Ktrans_median_exclude, 4),
+    'whole_putamen_Ktrans_std': round(whole_putamen_Ktrans_std_exclude, 4),
+    'whole_pallidum_Ktrans_mean': round(whole_pallidum_Ktrans_mean_exclude, 4),
+    'whole_pallidum_Ktrans_median': round(whole_pallidum_Ktrans_median_exclude, 4),
+    'whole_pallidum_Ktrans_std': round(whole_pallidum_Ktrans_std_exclude, 4),
+    'whole_thalamus_Ktrans_mean': round(whole_thalamus_Ktrans_mean_exclude, 4),
+    'whole_thalamus_Ktrans_median': round(whole_thalamus_Ktrans_median_exclude, 4),
+    'whole_thalamus_Ktrans_std': round(whole_thalamus_Ktrans_std_exclude, 4),
+    'whole_caudate_Ktrans_mean': round(whole_caudate_Ktrans_mean_exclude, 4),
+    'whole_caudate_Ktrans_median': round(whole_caudate_Ktrans_median_exclude, 4),
+    'whole_caudate_Ktrans_std': round(whole_caudate_Ktrans_std_exclude, 4),
+    'whole_amygdala_Ktrans_mean': round(whole_amygdala_Ktrans_mean_exclude, 4),
+    'whole_amygdala_Ktrans_median': round(whole_amygdala_Ktrans_median_exclude, 4),
+    'whole_amygdala_Ktrans_std': round(whole_amygdala_Ktrans_std_exclude, 4),
+    'whole_entorhinal_cortex_Ktrans_mean': round(whole_entorhinal_cortex_Ktrans_mean_exclude, 4),
+    'whole_entorhinal_cortex_Ktrans_median': round(whole_entorhinal_cortex_Ktrans_median_exclude, 4),
+    'whole_entorhinal_cortex_Ktrans_std': round(whole_entorhinal_cortex_Ktrans_std_exclude, 4),
+    'whole_fusiform_gyrus_cortex_Ktrans_mean': round(whole_fusiform_gyrus_cortex_Ktrans_mean_exclude, 4),
+    'whole_fusiform_gyrus_cortex_Ktrans_median': round(whole_fusiform_gyrus_cortex_Ktrans_median_exclude, 4),
+    'whole_fusiform_gyrus_cortex_Ktrans_std': round(whole_fusiform_gyrus_cortex_Ktrans_std_exclude, 4),
+    'whole_fusiform_gyrus_WM_Ktrans_mean': round(whole_fusiform_gyrus_WM_Ktrans_mean_exclude, 4),
+    'whole_fusiform_gyrus_WM_Ktrans_median': round(whole_fusiform_gyrus_WM_Ktrans_median_exclude, 4),
+    'whole_fusiform_gyrus_WM_Ktrans_std': round(whole_fusiform_gyrus_WM_Ktrans_std_exclude, 4),
+    'whole_insula_WM_Ktrans_mean': round(whole_insula_WM_Ktrans_mean_exclude, 4),
+    'whole_insula_WM_Ktrans_median': round(whole_insula_WM_Ktrans_median_exclude, 4),
+    'whole_insula_WM_Ktrans_std': round(whole_insula_WM_Ktrans_std_exclude, 4),
+    'whole_superior_temporal_cortex_Ktrans_mean': round(whole_superior_temporal_cortex_Ktrans_mean_exclude, 4),
+    'whole_superior_temporal_cortex_Ktrans_median': round(whole_superior_temporal_cortex_Ktrans_median_exclude, 4),
+    'whole_superior_temporal_cortex_Ktrans_std': round(whole_superior_temporal_cortex_Ktrans_std_exclude, 4),
+    'whole_inferior_temporal_cortex_Ktrans_mean': round(whole_inferior_temporal_cortex_Ktrans_mean_exclude, 4),
+    'whole_inferior_temporal_cortex_Ktrans_median': round(whole_inferior_temporal_cortex_Ktrans_median_exclude, 4),
+    'whole_inferior_temporal_cortex_Ktrans_std': round(whole_inferior_temporal_cortex_Ktrans_std_exclude, 4),
+    'whole_posterior_cingulate_cortex_Ktrans_mean': round(whole_posterior_cingulate_cortex_Ktrans_mean_exclude, 4),
+    'whole_posterior_cingulate_cortex_Ktrans_median': round(whole_posterior_cingulate_cortex_Ktrans_median_exclude, 4),
+    'whole_posterior_cingulate_cortex_Ktrans_std': round(whole_posterior_cingulate_cortex_Ktrans_std_exclude, 4),
+    'whole_medial_temporal_cortex_Ktrans_mean': round(whole_medial_temporal_cortex_Ktrans_mean_exclude, 4),
+    'whole_medial_temporal_cortex_Ktrans_median': round(whole_medial_temporal_cortex_Ktrans_median_exclude, 4),
+    'whole_medial_temporal_cortex_Ktrans_std': round(whole_medial_temporal_cortex_Ktrans_std_exclude, 4),
+    'ktrans_wm_outliers': Ktrans_wm_outliers,
+    'ktrans_gm_outliers': Ktrans_gm_outliers,
+    'T1_blood_histogram': "../../" + T1_blood_histogram_exclude_path,
+    'wm_histogram': "../../" + ktrans_wm_histogram_exclude_path,
+    'gm_histogram': "../../" + ktrans_gm_histogram_exclude_path,
+    'whole_hippo_outliers': whole_hippo_outliers,
+    'whole_phg_outliers': whole_phg_outliers,
+    'whole_putamen_outliers': whole_putamen_outliers,
+    'whole_pallidum_outliers': whole_pallidum_outliers,
+    'whole_thalamus_outliers': whole_thalamus_outliers,
+    'whole_caudate_outliers': whole_caudate_outliers,
+    'whole_amygdala_outliers': whole_amygdala_outliers,
+    'whole_entorhinal_cortex_outliers': whole_entorhinal_cortex_outliers,
+    'whole_fusiform_gyrus_cortex_outliers': whole_fusiform_gyrus_cortex_outliers,
+    'whole_fusiform_gyrus_WM_outliers': whole_fusiform_gyrus_WM_outliers,
+    'whole_insula_WM_outliers': whole_insula_WM_outliers,
+    'whole_superior_temporal_cortex_outliers': whole_superior_temporal_cortex_outliers,
+    'whole_inferior_temporal_cortex_outliers': whole_inferior_temporal_cortex_outliers,
+    'whole_posterior_cingulate_cortex_outliers': whole_posterior_cingulate_cortex_outliers,
+    'whole_medial_temporal_cortex_outliers': whole_medial_temporal_cortex_outliers,
+    'Ktrans_whole_hippo_histogram': "../../" + whole_hippo_histogram_exclude_path,
+    'Ktrans_whole_phg_histogram': "../../" + whole_phg_histogram_exclude_path,
+    'Ktrans_whole_putamen_histogram': "../../" + whole_putamen_histogram_exclude_path,
+    'Ktrans_whole_pallidum_histogram': "../../" + whole_pallidum_histogram_exclude_path,
+    'Ktrans_whole_thalamus_histogram': "../../" + whole_thalamus_histogram_exclude_path,
+    'Ktrans_whole_caudate_histogram': "../../" + whole_caudate_histogram_exclude_path,
+    'Ktrans_whole_amygdala_histogram': "../../" + whole_amygdala_histogram_exclude_path,
+    'Ktrans_whole_entorhinal_cortex_histogram': "../../" + whole_entorhinal_cortex_histogram_exclude_path,
+    'Ktrans_whole_fusiform_gyrus_cortex_histogram': "../../" + whole_fusiform_gyrus_cortex_histogram_exclude_path,
+    'Ktrans_whole_fusiform_gyrus_WM_histogram': "../../" + whole_fusiform_gyrus_WM_histogram_exclude_path,
+    'Ktrans_whole_insula_WM_histogram': "../../" + whole_insula_WM_histogram_exclude_path,
+    'Ktrans_whole_superior_temporal_cortex_histogram': "../../" + whole_superior_temporal_cortex_histogram_exclude_path,
+    'Ktrans_whole_inferior_temporal_cortex_histogram': "../../" + whole_inferior_temporal_cortex_histogram_exclude_path,
+    'Ktrans_whole_posterior_cingulate_cortex_histogram': "../../" + whole_posterior_cingulate_cortex_histogram_exclude_path,
+    'Ktrans_whole_medial_temporal_cortex_histogram': "../../" + whole_medial_temporal_cortex_histogram_exclude_path
+}
+
+output_exclude = template.render(data)
+
+# make reports directory if it doesn't exist
+if not os.path.exists(dir + '/reports'):
+    os.makedirs(dir + '/reports')
+
+run_folder = output_dir[1:]
+# make output_dir directory if it doesn't exist
+if not os.path.exists(dir + '/reports/' + run_folder):
+    os.makedirs(dir + '/reports/' + run_folder)
+
+# write html to file
+with open(dir + '/reports/' + run_folder + '/population_report' + output_dir + "_" + date_filename + '.html', 'w') as f:
+    f.write(output)
+
+with open(dir + '/reports/' + run_folder + '/population_report_exclude' + output_dir + "_" + date_filename + '.html', 'w') as f:
+    f.write(output_exclude)
+
+print('Report generated in ' + dir + '/reports/' + run_folder + '/population_report' + output_dir + "_" + date_filename + '.html')
+print('Excluded report generated in ' + dir + '/reports/' + run_folder + '/population_report_exclude' + output_dir + "_" + date_filename + '.html')
+
+if os.path.exists(os.path.join(dir, '../dce_available_3524_ac.xlsx')):
+    # add apoe and cdr fields to population_data
+    df = pd.read_excel(os.path.join(dir, '../dce_available_3524_ac.xlsx'), sheet_name="main")
+
+    # get apoe and cdr values for each subject
+    for subject in population_data.keys():
+        # get subject's ID
+        subject_id = subject.split("_")[0]
+        subject_id = subject_id.split("-")[1]
+        # get subject's timepoint
+        timepoint = subject.split("_")[1]
+        # get subject's apoe and cdr values
+        if subject_id.startswith("4") or subject_id.startswith("3"):
+            # insert underscore after 1st character
+            subject_id = subject_id[:1] + "_" + subject_id[1:]
+        try:
+            if int(subject_id) in df['Subject_ID'].values:
+                apoe = df.loc[df['Subject_ID'] == int(subject_id), 'APOE'].values[0]
+                # cdr = df.loc[df['Subject_ID'] == int(subject_id), 'CDR'].values[0]
+                # bmi = df.loc[df['Subject_ID'] == int(subject_id), 'BMI'].values[0]
+            else:
+                apoe = "N/A"
+                cdr = "N/A"
+                bmi = "N/A"
+            # add to population_data
+        except Exception as e:
+            print(e)
+            # print("Subject " + subject_id + " not found in dce_available_3524_ac.xlsx")
+            apoe = "N/A"
+            cdr = "N/A"
+            bmi = "N/A"
+        population_data[subject]["APOE"] = apoe
+        # population_data[subject]["CDR"] = cdr
+        # population_data[subject]["BMI"] = bmi
+
+    for subject in population_data_exclude.keys():
+        # get subject's ID
+        subject_id = subject.split("_")[0]
+        subject_id = subject_id.split("-")[1]
+        # get subject's timepoint
+        timepoint = subject.split("_")[1]
+        # get subject's apoe and cdr values
+        if subject_id.startswith("4") or subject_id.startswith("3"):
+            # insert underscore after 1st character
+            subject_id = subject_id[:1] + "_" + subject_id[1:]
+        try:
+            apoe = df.loc[df['Subject_ID'] == int(subject_id), 'APOE'].values[0]
+            # cdr = df.loc[df['Subject_ID'] == int(subject_id), 'CDR'].values[0]
+            # bmi = df.loc[df['Subject_ID'] == int(subject_id), 'BMI'].values[0]
+            # add to population_data
+        except Exception as e:
+            print(e)
+            print("Subject " + subject_id + " not found in dce_available_3524_ac.xlsx")
+            apoe = "N/A"
+            cdr = "N/A"
+            bmi = "N/A"
+        population_data_exclude[subject]["APOE"] = apoe
+        # population_data_exclude[subject]["CDR"] = cdr
+        # population_data_exclude[subject]["BMI"] = bmi
+
+    # for subject in population_data_exclude_signa.keys():
+    #     # get subject's ID
+    #     subject_id = subject.split("_")[0]
+    #     subject_id = subject_id.split("-")[1]
+    #     # get subject's timepoint
+    #     timepoint = subject.split("_")[1]
+    #     # get subject's apoe and cdr values
+    #     if subject_id.startswith("4") or subject_id.startswith("3"):
+    #         # insert underscore after 1st character
+    #         subject_id = subject_id[:1] + "_" + subject_id[1:]
+    #     try:
+    #         apoe = df.loc[df['Subject_ID'] == int(subject_id), 'APOE'].values[0]
+    #         cdr = df.loc[df['Subject_ID'] == int(subject_id), 'CDR'].values[0]
+    #         bmi = df.loc[df['Subject_ID'] == int(subject_id), 'BMI'].values[0]
+    #         # add to population_data
+    #     except Exception as e:
+    #         print(e)
+    #         print("Subject " + subject_id + " not found in dce_available_3524_ac.xlsx")
+    #         apoe = "N/A"
+    #         cdr = "N/A"
+    #         bmi = "N/A"
+    #     population_data_exclude_signa[subject]["APOE"] = apoe
+    #     population_data_exclude_signa[subject]["CDR"] = cdr
+    #     population_data_exclude_signa[subject]["BMI"] = bmi
+
+    for subject in population_data_failed.keys():
+        # get subject's ID
+        subject_id = subject.split("_")[0]
+        subject_id = subject_id.split("-")[1]
+        # get subject's timepoint
+        timepoint = subject.split("_")[1]
+        # get subject's apoe and cdr values
+        if subject_id.startswith("4") or subject_id.startswith("3"):
+            # insert underscore after 1st character
+            subject_id = subject_id[:1] + "_" + subject_id[1:]
+        try:
+            apoe = df.loc[df['Subject_ID'] == int(subject_id), 'APOE'].values[0]
+            # cdr = df.loc[df['Subject_ID'] == int(subject_id), 'CDR'].values[0]
+            # bmi = df.loc[df['Subject_ID'] == int(subject_id), 'BMI'].values[0]
+            # add to population_data
+        except Exception as e:
+            print(e)
+            print("Subject " + subject_id + " not found in dce_available_3524_ac.xlsx")
+            apoe = "N/A"
+            cdr = "N/A"
+            bmi = "N/A"
+        population_data_failed[subject]["APOE"] = apoe
+        # population_data_failed[subject]["CDR"] = cdr
+        # population_data_failed[subject]["BMI"] = bmi
+
+    # read EXCLUDED sheet from dce_available_3524_ac.xlsx and move subjects to population_data_exclude
+    df = pd.read_excel(os.path.join(dir, '../dce_available_3524_ac.xlsx'), sheet_name="EXCLUDED")
+    # get subject ID and timepoint (first and second columns)
+    subjects_excluded = df['Subject_ID']
+    timepoints_excluded = df['Timepoint']
+    # get exclusion reasons (third column)
+    exclusion_reasons = df['REASON']
+    dates_excluded = df['Study_Date']
+    # if excluded subject is in population_data, move to population_data_exclude
+    for subject, timepoint, exclusion_reason in zip(subjects_excluded, timepoints_excluded, exclusion_reasons):
+        # skip if reason includes "aliasing"
+        if "aliasing" in exclusion_reason.lower():
+            continue
+        if "no t1w" in exclusion_reason.lower():
+            continue
+        if "t1w only" in exclusion_reason.lower():
+            continue
+        if "no fas" in exclusion_reason.lower():
+            continue
+        # get subject's ID
+        subject_id = subject
+        # get subject's timepoint
+        # if NaN, set to 1
+        if pd.isnull(timepoint):
+            timepoint = 1
+        timepoint = int(timepoint)
+        entry = f"{subject_id}_ses-0{timepoint}"
+        if entry in population_data.keys():
+            # move to population_data_exclude
+            population_data_exclude[entry] = population_data.pop(entry)
+            # add exclusion reason if not already there
+            if "Reason" not in population_data_exclude[entry].keys():
+                population_data_exclude[entry]["Reason"] = exclusion_reason
+            else:
+                population_data_exclude[entry]["Reason"] += ", " + exclusion_reason
+            # add date
+            # population_data_exclude[entry]["Date"] = dates_excluded[subjects_excluded == subject].values[0]
+            # remove from population_data
+            # del population_data[entry]
+        elif entry not in population_data.keys() and entry not in population_data_failed.keys() and entry not in population_data_exclude.keys():# and entry not in population_data_exclude_signa.keys():
+            # read whole row from dce_available_3524_ac.xlsx
+            row = df.loc[df['Subject_ID'] == subject_id]
+            population_data_exclude[entry] = {}
+            population_data_exclude[entry]["Reason"] = exclusion_reason
+            population_data_exclude[entry]["Timepoint"] = row['Timepoint'].values[0]
+            population_data_exclude[entry]["APOE"] = row['APOE'].values[0]
+            population_data_exclude[entry]["Sex"] = row['Sex'].values[0]
+            population_data_exclude[entry]["Age"] = row['Age'].values[0]
+            population_data_exclude[entry]["Date"] = row['Study_Date'].values[0]
+            # population_data_exclude[entry]["CDR"] = row['CDR'].values[0]
+            # population_data_exclude[entry]["BMI"] = row['BMI'].values[0]
+            # Fill rest of fields with default values
+            # fields = [
+            #     "Machine", "Institution", "Coil", "TR", "Time_resolution", "TE", "Flip_angle", "n_reps",
+            #     "Approximate SNR", "AIFitness", "aif_fitted_r2", "manual_aif_status", "max_disp", "T1_blood", "T1_wm_median", "T1_gm_median",
+            #     "Ktrans_wm_median", "Ktrans_gm_median", "Ktrans_Hippo_median", "Ktrans_PhG_median", "Ktrans_Putamen_median", "Ktrans_Pallidum_median",
+            #     "Ktrans_Thalamus_median", "Ktrans_Caudate_median", "Ktrans_Amygdala_median", "Ktrans_Entorhinal_cortex_median",
+            #     "Ktrans_Fusiform_gyrus_cortex_median", "Ktrans_Fusiform_gyrus_WM_median", "Ktrans_Insula_WM_median",
+            #     "Ktrans_Superior_temporal_cortex_median", "Ktrans_Inferior_temporal_cortex_median", "Ktrans_Posterior_cingulate_cortex_median", "Ktrans_Medial_temporal_cortex_median",
+            #     "Vp_Hippo_median", "Vp_PhG_median", "Vp_Putamen_median", "Vp_Pallidum_median", "Vp_Thalamus_median",
+            #     "Vp_Caudate_median", "Vp_Amygdala_median", "Vp_Entorhinal_cortex_median", "Vp_Fusiform_gyrus_cortex_median",
+            #     "Vp_Fusiform_gyrus_WM_median", "Vp_Insula_WM_median", "Vp_Superior_temporal_cortex_median",
+            #     "Vp_Inferior_temporal_cortex_median", "Vp_Posterior_cingulate_cortex_median", "Vp_Medial_temporal_cortex_median",
+            #     "hippo_vol", "phg_vol", "putamen_vol", "pallidum_vol", "thalamus_vol", "caudate_vol", "amygdala_vol",
+            #     "entorhinal_cortex_vol", "fusiform_gyrus_cortex_vol", "fusiform_gyrus_wm_vol", "insula_wm_vol",
+            #     "superior_temporal_cortex_vol", "inferior_temporal_cortex_vol", "posterior_cingulate_cortex_vol", "medial_temporal_cortex_vol",
+            #     "bankssts_thickness_avg", "bankssts_thickness_std", "caudalanteriorcingulate_thickness_avg", "caudalanteriorcingulate_thickness_std",
+            #     "caudalmiddlefrontal_thickness_avg", "caudalmiddlefrontal_thickness_std", "cuneus_thickness_avg", "cuneus_thickness_std",
+            #     "entorhinal_thickness_avg", "entorhinal_thickness_std", "fusiform_thickness_avg", "fusiform_thickness_std",
+            #     "inferiorparietal_thickness_avg", "inferiorparietal_thickness_std", "inferiortemporal_thickness_avg", "inferiortemporal_thickness_std",
+            #     "isthmuscingulate_thickness_avg", "isthmuscingulate_thickness_std", "lateraloccipital_thickness_avg", "lateraloccipital_thickness_std",
+            #     "lateralorbitofrontal_thickness_avg", "lateralorbitofrontal_thickness_std", "lingual_thickness_avg", "lingual_thickness_std",
+            #     "medialorbitofrontal_thickness_avg", "medialorbitofrontal_thickness_std", "middletemporal_thickness_avg", "middletemporal_thickness_std",
+            #     "parahippocampal_thickness_avg", "parahippocampal_thickness_std", "paracentral_thickness_avg", "paracentral_thickness_std",
+            #     "parsopercularis_thickness_avg", "parsopercularis_thickness_std", "parsorbitalis_thickness_avg", "parsorbitalis_thickness_std",
+            #     "parstriangularis_thickness_avg", "parstriangularis_thickness_std", "pericalcarine_thickness_avg", "pericalcarine_thickness_std",
+            #     "postcentral_thickness_avg", "postcentral_thickness_std", "posteriorcingulate_thickness_avg", "posteriorcingulate_thickness_std",
+            #     "precentral_thickness_avg", "precentral_thickness_std", "precuneus_thickness_avg", "precuneus_thickness_std",
+            #     "rostralanteriorcingulate_thickness_avg", "rostralanteriorcingulate_thickness_std", "rostralmiddlefrontal_thickness_avg",
+            #     "rostralmiddlefrontal_thickness_std", "superiorfrontal_thickness_avg", "superiorfrontal_thickness_std",
+            #     "superiorparietal_thickness_avg", "superiorparietal_thickness_std", "superiortemporal_thickness_avg", "superiortemporal_thickness_std",
+            #     "supramarginal_thickness_avg", "supramarginal_thickness_std", "frontalpole_thickness_avg", "frontalpole_thickness_std",
+            #     "temporalpole_thickness_avg", "temporalpole_thickness_std", "transversetemporal_thickness_avg", "transversetemporal_thickness_std",
+            #     "insula_thickness_avg", "insula_thickness_std"
+            # ]
+            # for field in fields:
+            #     if field not in population_data_exclude[entry]:
+            #         population_data_exclude[entry][field] = -1
+
+# make excel file
+if not os.path.exists(os.path.join(dir, "spreadsheets", output_dir[1:])):
+    os.makedirs(os.path.join(dir, "spreadsheets", output_dir[1:]))
+writer = pd.ExcelWriter(
+    os.path.join(dir, "spreadsheets", output_dir[1:], "dataset_ktrans" + output_dir + "_" + date_filename + ".xlsx"),
+    date_format='YYYY/MM/DD',
+    datetime_format='YYYY/MM/DD',
+    engine='xlsxwriter'
+)
+# make dataframe
+df_success = pd.DataFrame(population_data)
+# df_exclude = pd.DataFrame(population_data_exclude)
+
+order = ["Date", "APOE", "Sex", "Age", "Machine", "Institution", "Coil", "TR", "Time_resolution", "TE", "Flip_angle", "n_reps",
+        "Approximate SNR", "AIFitness", "aif_fitted_r2", "manual_aif_status", "max_disp", "T1_blood", "T1_wm_median", "T1_gm_median",
+        "Ktrans_wm_median", "Ktrans_gm_median", "Ktrans_Hippo_median", "Ktrans_PhG_median", "Ktrans_Putamen_median", "Ktrans_Pallidum_median",
+        "Ktrans_Thalamus_median", "Ktrans_Caudate_median", "Ktrans_Amygdala_median", "Ktrans_Entorhinal_cortex_median",
+        "Ktrans_Fusiform_gyrus_cortex_median", "Ktrans_Fusiform_gyrus_WM_median", "Ktrans_Insula_WM_median",
+        "Ktrans_Superior_temporal_cortex_median", "Ktrans_Inferior_temporal_cortex_median", "Ktrans_Posterior_cingulate_cortex_median", "Ktrans_Medial_temporal_cortex_median",
+        "Vp_Hippo_median", "Vp_PhG_median", "Vp_Putamen_median", "Vp_Pallidum_median", "Vp_Thalamus_median",
+        "Vp_Caudate_median", "Vp_Amygdala_median", "Vp_Entorhinal_cortex_median", "Vp_Fusiform_gyrus_cortex_median",
+        "Vp_Fusiform_gyrus_WM_median", "Vp_Insula_WM_median", "Vp_Superior_temporal_cortex_median",
+        "Vp_Inferior_temporal_cortex_median", "Vp_Posterior_cingulate_cortex_median", "Vp_Medial_temporal_cortex_median",
+        "hippo_vol", "phg_vol", "putamen_vol", "pallidum_vol", "thalamus_vol", "caudate_vol", "amygdala_vol",
+        "entorhinal_cortex_vol", "fusiform_gyrus_cortex_vol", "fusiform_gyrus_wm_vol", "insula_wm_vol",
+        "superior_temporal_cortex_vol", "inferior_temporal_cortex_vol", "posterior_cingulate_cortex_vol", "medial_temporal_cortex_vol",
+        "bankssts_thickness_avg", "bankssts_thickness_std", "caudalanteriorcingulate_thickness_avg", "caudalanteriorcingulate_thickness_std",
+        "caudalmiddlefrontal_thickness_avg", "caudalmiddlefrontal_thickness_std", "cuneus_thickness_avg", "cuneus_thickness_std",
+        "entorhinal_thickness_avg", "entorhinal_thickness_std", "fusiform_thickness_avg", "fusiform_thickness_std",
+        "inferiorparietal_thickness_avg", "inferiorparietal_thickness_std", "inferiortemporal_thickness_avg", "inferiortemporal_thickness_std",
+        "isthmuscingulate_thickness_avg", "isthmuscingulate_thickness_std", "lateraloccipital_thickness_avg", "lateraloccipital_thickness_std",
+        "lateralorbitofrontal_thickness_avg", "lateralorbitofrontal_thickness_std", "lingual_thickness_avg", "lingual_thickness_std",
+        "medialorbitofrontal_thickness_avg", "medialorbitofrontal_thickness_std", "middletemporal_thickness_avg", "middletemporal_thickness_std",
+        "parahippocampal_thickness_avg", "parahippocampal_thickness_std", "paracentral_thickness_avg", "paracentral_thickness_std",
+        "parsopercularis_thickness_avg", "parsopercularis_thickness_std", "parsorbitalis_thickness_avg", "parsorbitalis_thickness_std",
+        "parstriangularis_thickness_avg", "parstriangularis_thickness_std", "pericalcarine_thickness_avg", "pericalcarine_thickness_std",
+        "postcentral_thickness_avg", "postcentral_thickness_std", "posteriorcingulate_thickness_avg", "posteriorcingulate_thickness_std",
+        "precentral_thickness_avg", "precentral_thickness_std", "precuneus_thickness_avg", "precuneus_thickness_std",
+        "rostralanteriorcingulate_thickness_avg", "rostralanteriorcingulate_thickness_std", "rostralmiddlefrontal_thickness_avg",
+        "rostralmiddlefrontal_thickness_std", "superiorfrontal_thickness_avg", "superiorfrontal_thickness_std",
+        "superiorparietal_thickness_avg", "superiorparietal_thickness_std", "superiortemporal_thickness_avg", "superiortemporal_thickness_std",
+        "supramarginal_thickness_avg", "supramarginal_thickness_std", "frontalpole_thickness_avg", "frontalpole_thickness_std",
+        "temporalpole_thickness_avg", "temporalpole_thickness_std", "transversetemporal_thickness_avg", "transversetemporal_thickness_std",
+        "insula_thickness_avg", "insula_thickness_std"]
+
+df_success = df_success.T
+df_success = df_success[order]
+
+
+order_exclude = order.copy()
+order_exclude.insert(0, "Reason")
+
+# name first column
+df_success.index.name = "Subject_ID"
+
+# write to excel
+df_success.to_excel(writer, sheet_name='Success')
+cell_format = writer.book.add_format()
+cell_format.set_text_wrap()
+cell_format.set_align('center')
+cell_format.set_align('vcenter')
+index_cell_format = writer.book.add_format()
+index_cell_format.set_text_wrap()
+index_cell_format.set_align('center')
+index_cell_format.set_align('vcenter')
+# unbold index
+index_cell_format.set_bold(False)
+if len(population_data_exclude) > 0:
+    df_exclude = pd.DataFrame(population_data_exclude)
+    df_exclude = df_exclude.T
+    df_exclude = df_exclude[order_exclude]
+    df_exclude.index.name = "Subject_ID"
+    df_exclude.to_excel(writer, sheet_name='Pre-Exclude')
+    for column in df_exclude.columns:
+        max_length = df_exclude[column].map(str).map(len).max()
+        max_length = max(max_length, len(column))
+        if column == "Date":
+            writer.sheets['Pre-Exclude'].set_column(df_exclude.columns.get_loc(column)+1, df_exclude.columns.get_loc(column)+1, 10, cell_format)
+        else:
+            writer.sheets['Pre-Exclude'].set_column(df_exclude.columns.get_loc(column)+1, df_exclude.columns.get_loc(column)+1, max_length+2, cell_format)
+
+    writer.sheets['Pre-Exclude'].set_column(0, 0, 20, index_cell_format)
+    writer.sheets['Pre-Exclude'].autofilter(0, 0, len(df_exclude), len(df_exclude.columns))
+
+if len(population_data_failed) > 0:
+    df_fail = pd.DataFrame(population_data_failed)
+    df_fail = df_fail.T
+    df_fail = df_fail[order_exclude]
+    df_fail.index.name = "Subject_ID"
+    df_fail.to_excel(writer, sheet_name='Fail')
+
+if len(population_data_missing) > 0:
+    df_missing = pd.DataFrame(population_data_missing)
+    df_missing = df_missing.T
+    df_missing.index.name = "Subject_ID"
+    df_missing.to_excel(writer, sheet_name='Missing')
+
+# if len(population_data_exclude_signa) > 0:
+#     df_exclude_signa = pd.DataFrame(population_data_exclude_signa)
+#     df_exclude_signa = df_exclude_signa.T
+#     df_exclude_signa = df_exclude_signa[order_exclude]
+#     df_exclude_signa.index.name = "Subject_ID"
+#     df_exclude_signa.to_excel(writer, sheet_name='Crazy GE Data')
+
+for column in df_success.columns:
+    max_length = df_success[column].map(str).map(len).max()
+    max_length = max(max_length, len(column))
+    if column == "Date":
+        writer.sheets['Success'].set_column(df_success.columns.get_loc(column)+1, df_success.columns.get_loc(column)+1, 10, cell_format)
+    else:
+        writer.sheets['Success'].set_column(df_success.columns.get_loc(column)+1, df_success.columns.get_loc(column)+1, max_length+2, cell_format)
+
+writer.sheets['Success'].set_column(0, 0, 20, index_cell_format)
+# autofilter
+writer.sheets['Success'].autofilter(0, 0, len(df_success), len(df_success.columns))
+# change date column data format to MM/DD/YYYY
+writer.close()
+
+# now backfill each subject's placement in the population
+imgs = []
+imgs_exclude = []
+for subject_id in subjects:
+# for subject_id, timepoint in zip(subjects, timepoints):
+    # list _timepoint directories in subject directory
+    for timepoint in subject_timepoints[subject_id]:
+        if timepoint.startswith("ses-"):
+            placement_wm_histogram_path = os.path.join(dceprep_dir, subject_id, timepoint, "figures/placement_wm_histogram.png")
+            placement_gm_histogram_path = os.path.join(dceprep_dir, subject_id, timepoint, "figures/placement_gm_histogram.png")
+            # get subject's Ktrans_wm_mean
+            try:
+                if f"{subject_id}_{timepoint}" in population_data.keys():
+                    case_wm_median = population_data[subject_id + "_" + timepoint]["Ktrans_wm_median"]
+                    case_gm_median = population_data[subject_id + "_" + timepoint]["Ktrans_gm_median"]
+                elif f"{subject_id}_{timepoint}" in population_data_exclude.keys():
+                    case_wm_median = population_data_exclude[subject_id + "_" + timepoint]["Ktrans_wm_median"]
+                    case_gm_median = population_data_exclude[subject_id + "_" + timepoint]["Ktrans_gm_median"]
+                # elif f"{subject_id}_{timepoint}" in population_data_exclude_signa.keys():
+                #     case_wm_median = population_data_exclude_signa[subject_id + "_" + timepoint]["Ktrans_wm_median"]
+                #     case_gm_median = population_data_exclude_signa[subject_id + "_" + timepoint]["Ktrans_gm_median"]
+                elif f"{subject_id}_{timepoint}" in population_data_failed.keys():
+                    case_wm_median = population_data_failed[subject_id + "_" + timepoint]["Ktrans_wm_median"]
+                    case_gm_median = population_data_failed[subject_id + "_" + timepoint]["Ktrans_gm_median"]
+                else:
+                    case_wm_median = -1
+                    case_gm_median = -1
+            except Exception as e:
+                print(f"Error getting {subject_id} {timepoint} wm or gm median.")
+                print(e)
+                continue
+
+            if os.path.exists(placement_wm_histogram_path) and os.path.exists(placement_gm_histogram_path):
+                # plot histograms
+                plt.hist(wm_histogram, bins=30)
+                plt.title("Ktrans White Matter Median")
+                plt.xlabel("Ktrans (10^-3/min)")
+                plt.axvline(x=case_wm_median, color='black')
+                # put percentile text in top right corner
+                plt.text(0.9, 0.95, "Percentile: " + str(round((len([x for x in wm_histogram if x < case_wm_median]) / len(wm_histogram)) * 100, 2)) + "%", horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes)
+                plt.savefig(placement_wm_histogram_path, bbox_inches='tight')
+                plt.close()
+
+                plt.hist(gm_histogram, bins=30)
+                plt.title("Ktrans Gray Matter Median")
+                plt.xlabel("Ktrans (10^-3/min)")
+                plt.axvline(x=case_gm_median, color='black')
+                # put percentile text in top right corner
+                plt.text(0.9, 0.95, "Percentile: " + str(round((len([x for x in gm_histogram if x < case_gm_median]) / len(gm_histogram)) * 100, 2)) + "%", horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes)
+                plt.savefig(placement_gm_histogram_path, bbox_inches='tight')
+                plt.close()
+            
+            # append to html file
+            try:
+                filename = os.path.join(dceprep_dir, subject_id, timepoint, f"reports/{subject_id}_{timepoint}_desc-casereport.html")
+                if os.path.exists(filename):
+                    with open(filename, "r") as f:
+                        report_content = f.read()
+
+                    # replace placeholder with histogram path
+                    report_content = report_content.replace("placeholder_wm", "../../figures/placement_wm_histogram.png")
+                    report_content = report_content.replace("placeholder_gm", "../../figures/placement_gm_histogram.png")
+                    # write html to file
+                    with open(filename, 'w') as f:
+                        f.write(report_content)
+
+                    report_path = os.path.join(dceprep_dir, subject_id, timepoint, f"reports/{subject_id}_{timepoint}_desc-report.png")
+
+                    if f"{subject_id}_{timepoint}" in population_data.keys():
+                        imgs.append(report_path)
+                    elif f"{subject_id}_{timepoint}" in population_data_exclude.keys():
+                        imgs_exclude.append(report_path)
+            except Exception as e:
+                print(f"Error appending {subject_id} {timepoint} placement histograms.")
+                print(e)
+
+# make scrollable report
+# Function to add images to PDF
+def add_image_to_pdf(pdf, image_path):
+    pdf.drawImage(image_path, 50, 50, width=letter[0]-85, height=letter[1]-50)  # Adjust coordinates and dimensions as needed
+    pdf.showPage()
+
+# Create a PDF canvas
+pdf_file = f"{dir}/reports/EZQCreport" + output_dir + "_" + date_filename + ".pdf"
+pdf = canvas.Canvas(pdf_file, pagesize=letter)
+
+# Add images to the PDF
+for report in imgs:
+    try:
+        add_image_to_pdf(pdf, report)
+    except Exception as e:
+        print(f"Error adding {report} to PDF.")
+        print(e)
+
+# Save the PDF to dir/reports
+pdf.save()
+
+pdf_file = f"{dir}/reports/EZQCreport_exclude" + output_dir + "_" + date_filename + ".pdf"
+pdf = canvas.Canvas(pdf_file, pagesize=letter)
+
+# Add images to the PDF
+for report in imgs_exclude:
+    try:
+        add_image_to_pdf(pdf, report)
+    except Exception as e:
+        print(f"Error adding {report} to PDF.")
+        print(e)
+
+pdf.save()
diff --git a/population_template.html b/population_template.html
new file mode 100644
index 0000000..7a2fc12
--- /dev/null
+++ b/population_template.html
@@ -0,0 +1,353 @@
+<html>
+<head>
+    <title>{{ title }}</title>
+    <style>
+        body {
+            padding-top: 35px;
+        }
+
+        /* Styles for the navbar */
+        .navbar {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            background-color: #333;
+            height: 50px;
+            display: flex;
+            justify-content: flex-start;
+            align-items: center;
+            z-index: 999;
+        }
+
+        .navbar-nav {
+            list-style-type: none;
+            display: flex;
+            margin: 0;
+            padding: 0;
+        }
+
+        .navbar-nav li {
+        position: relative;
+        }
+
+        .navbar-nav ul.dropdown {
+            position: absolute;
+            top: 30px;
+            display: none;
+            background-color: #fff;
+            padding: 0px;
+            box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+            list-style-type: none;
+            width: 100%;
+        }
+
+        .navbar-nav li:hover ul.dropdown {
+            display: block;
+        }
+
+        .navbar-nav ul.dropdown li {
+            margin: 10px 0;
+        }
+
+        .navbar-nav ul.dropdown li a {
+            color: #000;
+            text-decoration: none;
+        }
+
+        .navbar-nav ul.dropdown li:hover a {
+            background-color: #f1f1f1;
+        }
+
+        .nav-item {
+            margin-right: 10px;
+        }
+
+        .nav-item a {
+            color: #ffffff;
+            text-decoration: none;
+            padding: 10px;
+        }
+
+        .nav-item a:hover {
+            background-color: #555;
+        }        
+    </style>
+    </head>
+<body>
+    <nav class="navbar">
+        <ul class="navbar-nav">
+            <li class="nav-item"><a href="#">Summary</a></li>
+            <li class="nav-item">
+                <a href="#T1">T1 stats</a>
+                <!-- <ul class="dropdown">
+                    <li><a href="#T1w_masks">T1w Masks</a></li>
+                    <li><a href="#T1w_to_VFA">T1w->VFA</a></li>
+                    <li><a href="#VFA_Z-norm">VFA Z</a></li>
+                    <li><a href="#T1_map">T1 Map</a></li>
+                    <li><a href="#MCFLIRT">MCFLIRT</a></li>
+                    <li><a href="#AIF">AIF</a></li>
+                    <li><a href="#T1w_dyn">T1w->dyn</a></li>
+                    <li><a href="#DCE_Z-norm">DCE Z</a></li>
+                </ul> -->
+            </li>
+            
+            <li class="nav-item">
+                <a href="#AIF">AIF stats</a>
+            </li>
+
+            <li class="nav-item">
+                <a href="#Ktrans">Ktrans stats</a>
+            </li>
+
+            <li class="nav-item">
+                <a href="#MNI">MNI Ktrans</a>
+                <ul class="dropdown">
+                    <li><a href="#PHG">PHG</a></li>
+                    <li><a href="#Hippo">Hippo</a></li>
+                    <li><a href="#Putamen">Putamen</a></li>
+                    <li><a href="#Pallidum">Pallidum</a></li>
+                    <li><a href="#Thalamus">Thalamus</a></li>
+                    <li><a href="#Caudate">Caudate</a></li>
+                    <li><a href="#Amygdala">Amygdala</a></li>
+                    <li><a href="#Entorhinal">Entorhinal</a></li>
+                    <li><a href="#Fusiform">Fusiform Gyrus</a></li>
+                    <li><a href="#Insula_wm">Insula</a></li>
+                    <li><a href="#Sup_temp_c">Sup-Temp-ctx</a></li>
+                </ul>
+            </li>
+            <li class="nav-item">
+                <a href="#Flagged">Flagged Cases</a>
+            </li>
+        </ul>
+    </nav>
+    <!-- <p></p> -->
+    <h1 id="Summary">Population Summary</h1>
+    <ul>
+        <li>Subjects: {{ Subjects }}</li>
+        <!-- <li>Cases: {{ Cases }}</li> -->
+        <li>
+            Successful Cases:
+            {% for link, case in Combo %}
+                <a href="../{{ link }}">{{ case }}</a>{% if not loop.last %}, {% endif %}
+            {% endfor %}
+        </li>
+        <li>
+            Failed Cases:
+            {% for link, case in Failed_combo %}
+                <a href="{{ link }}">{{ case }}</a>{% if not loop.last %}, {% endif %}
+            {% endfor %}
+        </li>
+        
+        <li>Successes: {{ Successes }}</li>
+        <li>Excludes: {{ Excludes }}</li>
+        <!-- <li>{{ Timepoint }}</li> -->
+        <li>Date: {{ Date }}</li>
+        <li>Commit: {{ Commit }}</li>
+        <li>ROCKETSHIP Commit: {{ ROCKETSHIP_commit }}</li>
+        <li>Manufacturer: {{ Manufacturers }}</li>
+        <li>Model: {{ Machines }}</li>
+        <li>Field Strength: {{ Field_strengths }}</li>
+        <li>Institute: {{ Institutions }}</li>
+    </ul>
+    <h2 id="ROCKETSHIP">ROCKETSHIP Script Preferences (defaults overridden by JSONs)</h2>
+    <ul>
+        <li>Default TR: {{ pref_tr }}</li>
+        <li>Default FA: {{ pref_fa }}</li>
+        <li>Default Hematocrit: {{ pref_hematocrit }}</li>
+        <li>SNR Threshold: {{ pref_SNR }}</li>
+        <li>Relaxivity: {{ pref_relaxivity }}</li>
+        <li>Default T1 blood: {{ pref_t1blood }}</li>
+        <li>Starting repetition: {{ pref_start_t }}</li>
+        <li>Ending repetition: {{ pref_end_t }}</li>
+        <li>Time Resolution: {{ pref_timeres }}</li>
+        <li>Model: {{ pref_model }}</li>
+
+    </ul>
+    <h2 id="T1">T1 stats</h2>
+    <ul>
+        <li>White matter mean: {{ T1_wm_mean }}</li>
+        <li>White matter median: {{ T1_wm_median }}</li>
+        <li>White matter stdev: {{ T1_wm_std }}</li>
+        <li>White matter 5th%: {{ T1_wm_5th_percentile }}</li>
+        <li>White matter 95th% {{ T1_wm_95th_percentile }} </li>
+        <br>
+        <li>Gray matter mean: {{ T1_gm_mean }}</li>
+        <li>Gray matter median: {{ T1_gm_median }}</li>
+        <li>Gray matter stdev: {{ T1_gm_std }}</li>
+        <li>Gray matter 5th%: {{ T1_gm_5th_percentile }}</li>
+        <li>Gray matter 95th% {{ T1_gm_95th_percentile }} </li>
+    </ul>
+    <h2 id="AIF">AIF stats</h2>
+    <ul>
+        <li>AIFitness mean: {{ AIFitness_mean }}</li>
+        <li>AIFitness median: {{ AIFitness_median }}</li>
+        <li>AIFitness stdev: {{ AIFitness_std }}</li>
+        <li>AIFitness 5th%: {{ AIFitness_5th_percentile }}</li>
+        <br>
+        <li>mmol mean: {{ aif_mmol_mean }}</li>
+        <li>mmol median: {{ aif_mmol_median }}</li>
+        <li>mmol stdev: {{ aif_mmol_std }}</li>
+        <li>mmol 5th%: {{ aif_mmol_5th_percentile }}</li>
+        <li>mmol 95th% {{ aif_mmol_95th_percentile }}</li>
+    </ul>
+    <div class="image-container">
+        <img src="{{ AIFitness_histogram }}" alt="missing AIFitness histogram"/>
+        <img src="{{ aif_mmol_histogram }}" alt="missing aif mmol histogram"/>
+    </div>
+    <img src="{{ aif_curves }}" alt="missing aif curves" width="50%"/>
+    <li>T1 blood mean median: {{ T1_blood_mean }}</li>
+    <li>T1 blood median median: {{ T1_blood_median }}</li>
+    <li>T1 blood stdev median: {{ T1_blood_std }}</li>
+    <li>T1 blood 5th%: {{ T1_blood_5th_percentile }}</li>
+    <li>T1 blood 95th% {{ T1_blood_95th_percentile }} </li>
+    <img src="{{ T1_blood_histogram }}" alt="missing T1_blood histogram"/>
+    <h2 id="Ktrans">Ktrans stats (10^-3/min, >10^-5)</h2>
+    <ul>
+        <li>White matter mean median Ktrans: {{ Ktrans_wm_mean }}</li>
+        <li>White matter median median Ktrans: {{ Ktrans_wm_median }}</li>
+        <li>White matter stdev median Ktrans: {{ Ktrans_wm_std }}</li>
+        <li>Gray matter mean median Ktrans: {{ Ktrans_gm_mean }}</li>
+        <li>Gray matter median median Ktrans: {{ Ktrans_gm_median }}</li>
+        <li>Gray matter stdev median Ktrans: {{ Ktrans_gm_std }}</li>
+    </ul>
+    <h2>Ktrans Histogram</h2>
+    <ul>
+        <li>wm outliers (> 5): {{ ktrans_wm_outliers }}</li>
+        <li>gm outliers (> 5): {{ ktrans_gm_outliers }}</li>
+    </ul>
+    <div class="image-container">
+        <img src="{{ wm_histogram }}" alt="missing white matter histogram"/>
+        <img src="{{ gm_histogram }}" alt="missing gray matter histogram"/>
+    </div>
+    <h2 id="MNI">recon-all Ktrans (10^-3/min, >10^-5)</h2>
+    <h3 id="PHG">Parahippocampal Gyrus</h3>
+    <ul>
+        <li>Whole PHG mean median: {{ whole_phg_Ktrans_mean }}</li>
+        <li>Whole PHG median median: {{ whole_phg_Ktrans_median }}</li>
+        <li>Whole PHG stdev median: {{ whole_phg_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_phg_outliers }}</li>
+        <img src="{{ Ktrans_whole_phg_histogram }}" alt="missing whole PHG histogram"/>
+    </ul>
+    <h3 id="Hippo">Hippocampus</h3>
+    <ul>
+        <li>Whole hippocampus mean median: {{ whole_hippo_Ktrans_mean }}</li>
+        <li>Whole hippocampus median median: {{ whole_hippo_Ktrans_median }}</li>
+        <li>Whole hippocampus stdev median: {{ whole_hippo_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_hippo_outliers }}</li>
+        <img src="{{ Ktrans_whole_hippo_histogram }}" alt="missing whole hippocampus histogram"/>
+    </ul>
+    <h3 id="Putamen">Putamen</h3>
+    <ul>
+        <li>Whole putamen mean median: {{ whole_putamen_Ktrans_mean }}</li>
+        <li>Whole putamen median median: {{ whole_putamen_Ktrans_median }}</li>
+        <li>Whole putamen stdev median: {{ whole_putamen_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_putamen_outliers }}</li>
+        <img src="{{ Ktrans_whole_putamen_histogram }}" alt="missing whole putamen histogram"/>
+    </ul>
+    <h3 id="Pallidum">Pallidum</h3>
+    <ul>
+        <li>Whole pallidum mean median: {{ whole_pallidum_Ktrans_mean }}</li>
+        <li>Whole pallidum median median: {{ whole_pallidum_Ktrans_median }}</li>
+        <li>Whole pallidum stdev median: {{ whole_pallidum_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_pallidum_outliers }}</li>
+        <img src="{{ Ktrans_whole_pallidum_histogram }}" alt="missing whole pallidum histogram"/>
+    </ul>
+    <h3 id="Thalamus">Thalamus</h3>
+    <ul>
+        <li>Whole thalamus mean median: {{ whole_thalamus_Ktrans_mean }}</li>
+        <li>Whole thalamus median median: {{ whole_thalamus_Ktrans_median }}</li>
+        <li>Whole thalamus stdev median: {{ whole_thalamus_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_thalamus_outliers }}</li>
+        <img src="{{ Ktrans_whole_thalamus_histogram }}" alt="missing whole thalamus histogram"/>
+    </ul>
+    <h3 id="Caudate">Caudate</h3>
+    <ul>
+        <li>Whole caudate mean median: {{ whole_caudate_Ktrans_mean }}</li>
+        <li>Whole caudate median median: {{ whole_caudate_Ktrans_median }}</li>
+        <li>Whole caudate stdev median: {{ whole_caudate_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_caudate_outliers }}</li>
+        <img src="{{ Ktrans_whole_caudate_histogram }}" alt="missing whole caudate histogram"/>
+    </ul>
+    <h3 id="Amygdala">Amygdala</h3>
+    <ul>
+        <li>Whole amygdala mean median: {{ whole_amygdala_Ktrans_mean }}</li>
+        <li>Whole amygdala median median: {{ whole_amygdala_Ktrans_median }}</li>
+        <li>Whole amygdala stdev median: {{ whole_amygdala_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_amygdala_outliers }}</li>
+        <img src="{{ Ktrans_whole_amygdala_histogram }}" alt="missing whole amygdala histogram"/>
+    </ul>
+
+    <h3 id="Entorhinal">Entorhinal Cortex</h3>
+    <ul>
+        <li>Whole entorhinal cortex mean median: {{ whole_entorhinal_cortex_Ktrans_mean }}</li>
+        <li>Whole entorhinal cortex median median: {{ whole_entorhinal_cortex_Ktrans_median }}</li>
+        <li>Whole entorhinal cortex stdev median: {{ whole_entorhinal_cortex_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_entorhinal_cortex_outliers }}</li>
+        <img src="{{ Ktrans_whole_entorhinal_cortex_histogram }}" alt="missing whole entorhinal cortex histogram"/>
+    </ul>
+    
+    <h3 id="Fusiform">Fusiform Gyrus</h3>
+    <h4 id="Fusiform_c">Fusiform Gyrus Cortex</h3>
+    <ul>
+        <li>Whole fusiform gyrus cortex mean median: {{ whole_fusiform_gyrus_cortex_Ktrans_mean }}</li>
+        <li>Whole fusiform gyrus cortex median median: {{ whole_fusiform_gyrus_cortex_Ktrans_median }}</li>
+        <li>Whole fusiform gyrus cortex stdev median: {{ whole_fusiform_gyrus_cortex_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_fusiform_gyrus_cortex_outliers }}</li>
+        <img src="{{ Ktrans_whole_fusiform_gyrus_cortex_histogram }}" alt="missing whole fusiform gyrus cortex histogram"/>
+    </ul>
+
+    <h4 id="Fusiform_wm">Fusiform Gyrus White Matter</h3>
+    <ul>
+        <li>Whole fusiform gyrus white matter mean median: {{ whole_fusiform_gyrus_WM_Ktrans_mean }}</li>
+        <li>Whole fusiform gyrus white matter median median: {{ whole_fusiform_gyrus_WM_Ktrans_median }}</li>
+        <li>Whole fusiform gyrus white matter stdev median: {{ whole_fusiform_gyrus_WM_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_fusiform_gyrus_WM_outliers }}</li>
+        <img src="{{ Ktrans_whole_fusiform_gyrus_WM_histogram }}" alt="missing whole fusiform gyrus white matter histogram"/>
+    </ul>
+
+    <h3 id="Insula_wm">Insula White Matter</h3>
+    <ul>
+        <li>Whole insula white matter mean median: {{ whole_insula_WM_Ktrans_mean }}</li>
+        <li>Whole insula white matter median median: {{ whole_insula_WM_Ktrans_median }}</li>
+        <li>Whole insula white matter stdev median: {{ whole_insula_WM_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_insula_WM_outliers }}</li>
+        <img src="{{ Ktrans_whole_insula_WM_histogram }}" alt="missing whole insula white matter histogram"/>
+    </ul>
+
+    <h3 id="Sup_temp_c">Superior Temporal Cortex</h3>
+    <ul>
+        <li>Whole superior temporal cortex mean median: {{ whole_superior_temporal_cortex_Ktrans_mean }}</li>
+        <li>Whole superior temporal cortex median median: {{ whole_superior_temporal_cortex_Ktrans_median }}</li>
+        <li>Whole superior temporal cortex stdev median: {{ whole_superior_temporal_cortex_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_superior_temporal_cortex_outliers }}</li>
+        <img src="{{ Ktrans_whole_superior_temporal_cortex_histogram }}" alt="missing whole superior temporal cortex histogram"/>
+    </ul>
+    <h3 id="Post_cing_c">Posterior Cingulate Cortex</h3>
+    <ul>
+        <li>Whole posterior cingulate cortex mean median: {{ whole_posterior_cingulate_cortex_Ktrans_mean }}</li>
+        <li>Whole posterior cingulate cortex median median: {{ whole_posterior_cingulate_cortex_Ktrans_median }}</li>
+        <li>Whole posterior cingulate cortex stdev median: {{ whole_posterior_cingulate_cortex_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_posterior_cingulate_cortex_outliers }}</li>
+        <img src="{{ Ktrans_whole_posterior_cingulate_cortex_histogram }}" alt="missing whole posterior cingulate cortex histogram"/>
+    </ul>
+    <h3 id="Med_temp_c">Medial Temporal Cortex</h3>
+    <ul>
+        <li>Whole medial temporal cortex mean median: {{ whole_medial_temporal_cortex_Ktrans_mean }}</li>
+        <li>Whole medial temporal cortex median median: {{ whole_medial_temporal_cortex_Ktrans_median }}</li>
+        <li>Whole medial temporal cortex stdev median: {{ whole_medial_temporal_cortex_Ktrans_std }}</li>
+        <li>outliers (> 5): {{ whole_medial_temporal_cortex_outliers }}</li>
+        <img src="{{ Ktrans_whole_medial_temporal_cortex_histogram }}" alt="missing whole medial temporal cortex histogram"/>
+    </ul>
+    <h2 id="Flagged">Flagged Cases (motion > {{ Motion_threshold }}mm, AIFitness < {{ AIFitness_threshold }})</h4>
+    <li>
+        {% for link, case in Flagged_combo %}
+            {% if "dceprep-" in link %}
+            <a href="../../dceprep{{ link }}">{{ case }}</a>{% if not loop.last %}, {% endif %}
+            {% else %}
+            <a href="../dceprep{{ link }}">{{ case }}</a>{% if not loop.last %}, {% endif %}
+            {% endif %}
+        {% endfor %}
+    </li>
+</body>
+</html>
diff --git a/preprocess_all.sh b/preprocess_all.sh
index bad4c65..626c84f 100755
--- a/preprocess_all.sh
+++ b/preprocess_all.sh
@@ -1,48 +1,131 @@
 #!/bin/bash
 shopt -s extglob
-# FSL, AFNI, Matlab, ROCKETSHIP + parametric_scripts, and Python are required
-# Within parametric_scripts should be a custom scripts folder with T1mapping_fit.m
-# control variables
+# FSL, Matlab, ROCKETSHIP, ANTS, Python, and a BIDS compliant dataset are required
+# variables
+COMPARISON_MODE=0
 EN_Z_NORM=0
 EN_BIAS1=0
 EN_BIAS2=0
-ff=0
+EN_MOTION_CORR=0
+T1_ONLY=0
+USE_AUTO_AIF=0
+AIF_SUFFIX="desc-AIF_mask"
+AIF_TRAINING_SUFFIX="desc-trainingAIF_mask"
+SKIP_IF_SUCCESS=0
+SCRIPT_LOOP_DIRS=sub-*/ses-*
+AUTOAIF_WEIGHT_PATH="docker/files/model_weight_huber1.h5"
+AUTOAIF_MODEL="best"
+
+# internal vars (don't change)
 fail=0
+failures=0
 clean=0
-#EN_MOTION_CORR=1
-
-# make this your main data directory or pass it as an option to -d
-#DATA_DIR=/media/network_mriphysics/USC-PPG/data
+count=0
+current=0
+ETA=0
+mETA=0
+prog=0
+successes=0
 
 # options
-while getopts ":d:bBZFhc" options; do
+while getopts ":d:bBa:A:ZfhcC:mMstl:T:w:" options; do
 	case "${options}" in
+		a)
+			AIF_SUFFIX=${OPTARG}
+			;;
+		A)
+			AUTO_AIF_PATH=$(find $HOME -wholename '*main_vif.py' -printf '%h\n' -quit || find / -name '*main_vif.py' -printf '%h\n' -quit) &> /dev/null
+			case "${OPTARG}" in
+				M)
+					USE_AUTO_AIF=0
+					;;
+				A)
+					USE_AUTO_AIF=1
+					;;
+				T)
+					USE_AUTO_AIF=2
+					;;
+				*)
+					echo "Invalid argument for -A. Use A, M, or T."
+					exit 1
+					;;
+			esac
+			;;
 		b)
 			EN_BIAS1=1
 			;;
-		B)	EN_BIAS2=1
+		B)	
+			EN_BIAS2=1
+			;;
+		C)
+			COMPARISON_MODE=1
+			OUTPUT_DIR=${OPTARG}
 			;;
 		c)
 			clean=1
 			;;
 		d)
 			DATA_DIR=${OPTARG}
-			;;
-		F)
-			ff=1
+			if [ ${DATA_DIR::-1} == "/" ]
+				then
+				DATA_DIR=${DATA_DIR::-1}
+			fi
+			DATE=$(date +%Y-%m-%d)
+			# derivatives dir is up 1 level from data dir
+			DERIV_DIR=$(dirname $DATA_DIR)/derivatives
+			if [ ! -d "$DERIV_DIR" ]
+				then
+				mkdir -p "$DERIV_DIR"
+			fi
+			# make log directory if it doesn't exist
+			if [ ! -d "$DERIV_DIR/logs" ]
+				then
+				mkdir -p "$DERIV_DIR/logs"
+			fi
+			LOG_FILE=$DERIV_DIR/logs/preprocessing_log_$DATE.txt
+			# write command to log file
+			echo "Command: $0 $@" > $LOG_FILE
 			;;
 		h)
 			echo "This script runs through all subject folders of a specified main data directory, preprocessing every folder ending in '_timepoint'."
 			echo "The output is the DCE input, which are the corrected dynamic images, brain mask, T1 maps."
+			echo "-a: specify AIF suffix (default is 'desc-AIF_mask'). .nii.gz will be appended to the suffix."
+			echo "-A: enable AutoAIF with argument A (All automatic), M (Manual if available), or T (Manual + Training if available)"
 			echo "-b: enable first round of bias field corrections"
 			echo "-B: enable second round of bias field corrections, post-Z-norm if enabled"
-			echo "-c: clean generated files prior to processing"
-			echo "-Z: enable Z-slice normalization"
-			echo "-d: specify main data directory containing all subject folders"
-			echo "-F: fail fast, any command failures will end the script"
+			echo "-c: clean case's derivative folder prior to processing, ensures \"fresh\" runs but cannot use skips"
+			echo "-C [name]: enable comparison mode, which will output all files to the specified directory within each timepoint"
+			echo "-d [dir_path]: specify BIDS compliant data directory containing all subject folders (sub-*/ses-*/anat|dce/*.nii|*.json)"
 			echo "-h: display this message"
+			echo "-m: enable motion correction"
+			echo "-s: skip preprocessing if DCE input file already exists"
+			echo "-T [dir_path]: target the subject(s)/session(s) to run (default is 'sub-*/ses-*/')"
+			echo "-t: only run up to T1 mapping"
+			echo "-w [path]: specify the path to the AutoAIF weights file"
+			echo "-Z: enable Z-slice normalization"
 			exit 0
 			;;
+		l)
+			INPUT_LIST=$DATA_DIR/../code/${OPTARG}
+			;;
+		m)
+			EN_MOTION_CORR=1
+			;;
+		M)
+			AUTOAIF_MODEL=${OPTARG}
+			;;
+		s)
+			SKIP_IF_SUCCESS=1
+			;;
+		S)
+			SCRIPT_LOOP_DIRS=${OPTARG}
+			;;
+		t)
+			T1_ONLY=1
+			;;
+		w)
+			AUTOAIF_WEIGHT_PATH=${OPTARG}
+			;;
 		Z)
 			EN_Z_NORM=1
 			;;
@@ -55,467 +138,719 @@ done
 
 if [ -z "$DATA_DIR" ]
 	then
-		echo "ERROR: Please use '-d [dir_path]' to pass the path to your main data directory to this script."
+		echo "ERROR: Please use '-d [dir_path]' to pass the path to your BIDS compliant data directory to this script."
 		exit 1
 fi
-cd $DATA_DIR
+
 if [[ "$OSTYPE" == "linux-gnu" ]]; then
-	ROCKETSHIP_PATH=$(find $HOME -name '*run_dce_auto.m' -printf '%h\n' -quit)
-	SCRIPT_PATH=$(find $HOME -name '*auto_analysis.py' -printf '%h\n' -quit)
-	GPUFIT_PATH=$(find $HOME -name 'GpufitConstrainedMex.mexa64' -printf '%h\n' -quit || find / -name 'GpufitConstrainedMex.mexa64' -printf '%h\n' -quit)
-	GPUFIT_M_PATH=$(find $HOME -name 'ModelID.m' -printf '%h\n' -quit || find / -name 'ModelID.m' -printf '%h\n' -quit)
+	ROCKETSHIP_PATH=$(find $HOME -name '*run_dce_cli.m' -printf '%h\n' -quit || find / -name '*run_dce_cli.m' -printf '%h\n' -quit) &> /dev/null
+	SCRIPT_PATH=$(dirname "$(realpath $0)")
+	GPUFIT_PATH=$(find $HOME -name 'GpufitCudaAvailableMex.mexa64' -printf '%h\n' -quit || find / -name 'GpufitCudaAvailableMex.mexa64' -printf '%h\n' -quit) &> /dev/null
+	GPUFIT_M_PATH=$(find $HOME -name 'ModelID.m' -printf '%h\n' -quit || find / -name 'ModelID.m' -printf '%h\n' -quit) &> /dev/null
 else
 	ROCKETSHIP_PATH=$(find $HOME -type d -name ROCKETSHIP)
 	SCRIPT_PATH=$(find $HOME -type d -name in-house_toolbox)
-	GPUFIT_PATH=$(find $HOME -type d -name Gpufit-build)/matlab
+	GPUFIT_PATH=$(find $HOME -type d -name Gpufit-build)
 fi
+cd $DATA_DIR || exit 1
+
+# count timepoints
+for source_dir in $DATA_DIR/$SCRIPT_LOOP_DIRS; do
+	((count++))
+done
+# Function to calculate and display progress and estimated remaining time
+function show_progress {
+    local current_iteration=$1
+    local start_time=$2
+    local total_iterations=$3
+	local runtime=$4
+
+    # Calculate elapsed time
+    current_time=$(date +%s)
+    elapsed_time=$((current_time - start_time))
 
-# Run bias correction on VFA data 
+    # Calculate estimated total time
+    estimated_total_time=$((runtime * total_iterations))
+
+    # Calculate remaining time
+    remaining_time=$((estimated_total_time - elapsed_time))
+
+    # Display progress and estimated remaining time
+    echo -ne "Progress: $((elapsed_time * 100 / estimated_total_time))% - "
+    echo -ne "Elapsed time: $(($elapsed_time / 60))m $(($elapsed_time % 60))s - "
+	if [ $remaining_time -lt 0 ]
+		then
+		echo -ne "Estimated remaining time: calculating...\r"
+	else
+		echo -ne "Estimated remaining time: $(($remaining_time / 60))m $(($remaining_time % 60))s\r"
+	fi
+}
+# Run bias correction on VFA data
 # ------------------------------
-for dir in */*_timepoint/; do
-	date
-	echo Preprocessing ${dir}...
-	SUBJECT_TP_PATH=$(realpath $dir)
-	cd $dir
+for source_dir in $DATA_DIR/$SCRIPT_LOOP_DIRS; do
+	start_time=$(date +%s)
+	if [ ${source_dir::-1} == "/" ]; then
+		source_dir=$DATA_DIR/${source_dir::-1}
+	fi
+	date >> $LOG_FILE
+	echo "Preprocessing ${source_dir}..."
+	((current++))
+
+	# get subject ID and session
+	SUBJECT=$(echo $source_dir | grep -o 'sub-[^/]*')
+	SESSION=$(echo $source_dir | grep -o 'ses-[0-9]*')
+	PREFIX=${SUBJECT}_${SESSION}
+
+	if [ $COMPARISON_MODE -eq 1 ]
+		then
+		if [ ! -d "$DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION" ]
+			then
+			echo "Comparison mode enabled. Creating output directory $OUTPUT_DIR..." >> $LOG_FILE
+			mkdir -p $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce
+		fi
+		if [ ! $USE_AUTO_AIF -eq 1 ]
+			then
+			mask_copied=0
+			cp $DERIV_DIR/dceprep-manualAIF_refresh/$SUBJECT/$SESSION/dce/*$AIF_SUFFIX* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/ && mask_copied=1
+			# cp $DERIV_DIR/dceprep/$SUBJECT/$SESSION/dce/*$AIF_TRAINING_SUFFIX* $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/ && mask_copied=1
+			# Extract the session number from the session string
+			session_num=$(echo $SESSION | grep -o '[0-9]\+')
+			# turn sub-* into *
+			pat=${SUBJECT#sub-}
+			# Map the session number to the corresponding session string
+			case $session_num in
+				01) session_str="1st" ;;
+				02) session_str="2nd" ;;
+				03) session_str="3rd" ;;
+				*) session_str="" ;;
+			esac
+			# If the session string is not empty, copy the masks
+			if [ $mask_copied -eq 0 ] && [[ -n $session_str ]]; then
+				echo "Copying masks for $SUBJECT $SESSION..." >> $LOG_FILE
+				cp /media/network_mriphysics/USC-PPG/AI_training/loos_model/test/masks/sub-${pat}_ses-${session_num}_desc-AIF_mask.nii.gz $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${SUBJECT}_${SESSION}_${AIF_TRAINING_SUFFIX}.nii.gz && mask_copied=1
+				cp /media/network_mriphysics/USC-PPG/AI_training/loos_model/test/masks/${pat}_${session_str}_timepoint.nii.gz $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${SUBJECT}_${SESSION}_${AIF_TRAINING_SUFFIX}.nii.gz && mask_copied=1
+				cp /media/network_mriphysics/USC-PPG/AI_training/loos_model/train/masks/sub-${pat}_ses-${session_num}_desc-AIF_mask.nii.gz $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${SUBJECT}_${SESSION}_${AIF_TRAINING_SUFFIX}.nii.gz && mask_copied=1
+				cp /media/network_mriphysics/USC-PPG/AI_training/loos_model/train/masks/${pat}_${session_str}_timepoint.nii.gz $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${SUBJECT}_${SESSION}_${AIF_TRAINING_SUFFIX}.nii.gz && mask_copied=1
+				cp /media/network_mriphysics/USC-PPG/AI_training/loos_model/val/masks/sub-${pat}_ses-${session_num}_desc-AIF_mask.nii.gz $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${SUBJECT}_${SESSION}_${AIF_TRAINING_SUFFIX}.nii.gz && mask_copied=1
+				cp /media/network_mriphysics/USC-PPG/AI_training/loos_model/val/masks/${pat}_${session_str}_timepoint.nii.gz $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${SUBJECT}_${SESSION}_${AIF_TRAINING_SUFFIX}.nii.gz && mask_copied=1
+			fi
+			if [ $mask_copied -eq 0 ] && [ ! -f $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${PREFIX}_${AIF_SUFFIX}.nii.gz ] && [ ! -f $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/dce/${PREFIX}_${AIF_TRAINING_SUFFIX}.nii.gz ] && [ $USE_AUTO_AIF -eq 2 ]
+				then
+				echo "No AIF file found for $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION/. Skipping timepoint..." >> $LOG_FILE
+				rm -rf $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION
+				if [ -z "$(ls -A $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT)" ]
+					then
+					rm -rf $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT
+				fi
+				cd $DATA_DIR
+				continue
+			fi
+		fi
+		cd $DERIV_DIR/dceprep-$OUTPUT_DIR/$SUBJECT/$SESSION || exit 1
+	else
+		mkdir -p $DERIV_DIR/dceprep/$SUBJECT/$SESSION
+		cd $DERIV_DIR/dceprep/$SUBJECT/$SESSION || exit 1
+	fi
+	SUBJECT_TP_PATH=$(pwd)
 
-	if [ ! -f "2.nii" ] || [ ! -f "5.nii" ] || [ ! -f "10.nii" ] || [ ! -f "12.nii" ] || [ ! -f "15.nii" ] || [ ! -f "DCE.nii" ]
+	# --- LOCKING FOR MULTI-MACHINE PROCESSING ---
+	LOCKFILE="preprocessing_lock.txt"
+	LOCKDIR=$(pwd)
+	LOCKPATH="$LOCKDIR/$LOCKFILE"
+	LOCKHOST=$(hostname)
+	LOCKPID=$$
+	LOCKLIST="$DERIV_DIR/locks_${LOCKHOST}.txt"
+	echo "Attempting to lock $LOCKPATH on $LOCKHOST with PID $LOCKPID"
+
+	# Try to create lock file atomically
+	if ( set -o noclobber; echo "$LOCKHOST:$LOCKPID" > "$LOCKPATH" ) 2> /dev/null; then
+		echo "$LOCKPATH" >> "$LOCKLIST"
+		trap 'for f in $(cat "$LOCKLIST" 2>/dev/null); do rm -f "$f"; done; rm -f "$LOCKLIST"; exit $?' INT TERM EXIT
+	else
+		echo "Skipping $dir because it is currently being processed by $(cat $LOCKPATH)." >> $LOG_FILE
+		cd $DERIV_DIR
+		continue
+	fi
+
+	if [ ! -f $DERIV_DIR/dceprep/$SUBJECT/$SESSION/dce/${PREFIX}_${AIF_SUFFIX}.nii.gz ] && [ ! -f $DERIV_DIR/dceprep/$SUBJECT/$SESSION/dce/${PREFIX}_${AIF_SUFFIX}.nii ] && [ ! -f $DERIV_DIR/dceprep-manualAIF/$SUBJECT/$SESSION/dce/${PREFIX}_${AIF_TRAINING_SUFFIX}.nii.gz ] && [ $USE_AUTO_AIF -eq 0 ]
 		then
-		echo Base files missing! Skipping timepoint...
-		cd ../..
+		echo "No ${PREFIX}_${AIF_SUFFIX} file found for $DERIV_DIR/dceprep/$SUBJECT/$SESSION/. Skipping timepoint..." >> $LOG_FILE
+		cd $DATA_DIR
 		continue
 	fi
-	
+
+	if [ $SKIP_IF_SUCCESS -eq 1 ]
+		then
+		if [ -f "dce/${PREFIX}_desc-bfcz_DCE.nii.gz" ] && [ -f "anat/${PREFIX}_space-DCEref_desc-brain_mask.nii.gz" ] && \
+			[ -f "dce/${PREFIX}_desc-AIF_T1map.nii.gz" ] && [ -f "anat/${PREFIX}_space-DCEref_T1map.nii" ] #&& [ -f "reports/${PREFIX}_desc-casereport.html" ]
+			then
+			echo "Skipping ${source_dir} because it has already been processed." >> $LOG_FILE
+			let successes++
+			cd $DATA_DIR
+			continue
+		fi
+	fi
+
+	# get list of VFAs and sort them
+	VFA_LIST=($(ls $source_dir/anat/*.nii* | grep -v "$source_dir/anat/*T1w.nii*" | grep -v "$source_dir/dce/*aif.nii*"))
+	VFA_LIST=($(printf '%s\n' "${VFA_LIST[@]}" | grep -o -E 'flip-[0-9]+' | sort -n))
+	echo "Found ${#VFA_LIST[@]} VFAs: ${VFA_LIST[@]}"
+	VFA_NUMS=($(printf '%s\n' "${VFA_LIST[@]}" | grep -o -E 'flip-[0-9]+' | grep -o -E '[0-9]+'))
+
+	if [ ${#VFA_LIST[@]} -eq 0 ]
+		then
+		echo "$source_dir No VFAs found! Skipping timepoint..." >> $LOG_FILE
+		cd $DATA_DIR
+		continue
+	fi
+
+	if [ ! -f "$source_dir/dce/${PREFIX}_DCE.nii.gz" ] || [ ! -f "$source_dir/anat/${PREFIX}_T1w.nii.gz" ]
+	then
+		missing_files=""
+		[ ! -f "$source_dir/dce/${PREFIX}_DCE.nii.gz" ] && missing_files+=" $source_dir/dce/${PREFIX}_DCE.nii.gz"
+		[ ! -f "$source_dir/anat/${PREFIX}_T1w.nii.gz" ] && missing_files+=" $source_dir/anat/${PREFIX}_T1w.nii.gz"
+
+		echo "$source_dir Base file(s) missing! Missing file(s):$missing_files. Skipping timepoint..." >> "$LOG_FILE"
+		cd $DATA_DIR
+		continue
+	fi
+
+	mkdir dce &> /dev/null
 	if [ $clean -eq 1 ]
 		then
-        rm !(2.nii|5.nii|10.nii|12.nii|15.nii|DCE.nii|aif.nii)
+		echo Cleaning folder... $PWD
+		# remove all files except for the AIF
+		rm -rf anat figures reports
+		cd dce
+		rm -f !(${PREFIX}_${AIF_SUFFIX}.nii.gz|${PREFIX}_${AIF_TRAINING_SUFFIX}.nii.gz)
+		cd $SUBJECT_TP_PATH
     fi
-	
-	# FSL brain mask extraction from VFA 2 image
-	bet 2.nii brain.nii -R -m -f 0.45 -g 0 -Z
-	fslcpgeom 2.nii brain_mask.nii
-			
-	# FAST documentation recommends brain masking first
-	#fslmaths 2.nii -mas brain_mask.nii.gz 2_masked.nii 
-	#fslmaths 5.nii -mas brain_mask.nii.gz 5_masked.nii
-	#fslmaths 10.nii -mas brain_mask.nii.gz 10_masked.nii
-	#fslmaths 12.nii -mas brain_mask.nii.gz 12_masked.nii
-	#fslmaths 15.nii -mas brain_mask.nii.gz 15_masked.nii
-	cp 2.nii 2_masked.nii
-	cp 5.nii 5_masked.nii
-	cp 10.nii 10_masked.nii
-	cp 12.nii 12_masked.nii
-	cp 15.nii 15_masked.nii
-	gzip -f *_masked.nii
-	
-	if [ $EN_BIAS1 -eq 1 ]
-		then
-		if [ ! -f "15_bfc.nii" ]
+	mkdir anat &> /dev/null
+
+	# HD-BET brain extraction & segmentations from MP-RAGE
+	SECONDS=0
+	echo -ne "HD-BET MP-RAGE [                                                  ] $prog% ($current/$count) Calculating runtime...   \r"
+
+	if [ ! -f "anat/${PREFIX}_desc-brain_mask.nii.gz" ] && [ -f "$source_dir/anat/${PREFIX}_T1w.nii.gz" ]
 		then
-			echo Bias field correction with FAST
-			# don't forget to remove all unnecessary images 
-			fast -t 3 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 2_masked.nii
-			rm 2_masked_mixeltype.nii.gz
-			rm 2_masked_pve_0.nii.gz
-			rm 2_masked_pve_1.nii.gz
-			rm 2_masked_pve_2.nii.gz
-			rm 2_masked_pveseg.nii.gz
-			rm 2_masked_seg.nii.gz
-			3dcalc -a 2_masked.nii -b 2_masked_bias.nii.gz -expr a/b -prefix 2_bfc.nii -overwrite
-			
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 5_masked.nii
-			rm 5_masked_mixeltype.nii.gz
-			rm 5_masked_pve_0.nii.gz
-			rm 5_masked_pve_1.nii.gz
-			rm 5_masked_pve_2.nii.gz
-			rm 5_masked_pveseg.nii.gz
-			rm 5_masked_seg.nii.gz
-			3dcalc -a 5_masked.nii -b 5_masked_bias.nii.gz -expr a/b -prefix 5_bfc.nii -overwrite
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 10_masked.nii
-			rm 10_masked_mixeltype.nii.gz
-			rm 10_masked_pve_0.nii.gz
-			rm 10_masked_pve_1.nii.gz
-			rm 10_masked_pve_2.nii.gz
-			rm 10_masked_pveseg.nii.gz
-			rm 10_masked_seg.nii.gz
-			3dcalc -a 10_masked.nii -b 10_masked_bias.nii.gz -expr a/b -prefix 10_bfc.nii -overwrite
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 12_masked.nii
-			rm 12_masked_mixeltype.nii.gz
-			rm 12_masked_pve_0.nii.gz
-			rm 12_masked_pve_1.nii.gz
-			rm 12_masked_pve_2.nii.gz
-			rm 12_masked_pveseg.nii.gz
-			rm 12_masked_seg.nii.gz
-			3dcalc -a 12_masked.nii -b 12_masked_bias.nii.gz -expr a/b -prefix 12_bfc.nii -overwrite
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 15_masked.nii
-			rm 15_masked_mixeltype.nii.gz
-			rm 15_masked_pve_0.nii.gz
-			rm 15_masked_pve_1.nii.gz
-			rm 15_masked_pve_2.nii.gz
-			rm 15_masked_pveseg.nii.gz
-			#rm 15_masked_seg.nii.gz
-			3dcalc -a 15_masked.nii -b 15_masked_bias.nii.gz -expr a/b -prefix 15_bfc.nii -overwrite
+		if [ nvidia-smi ]
+			then
+			hd-bet -i $source_dir/anat/${PREFIX}_T1w.nii.gz -o anat/${PREFIX}_desc-brain.nii.gz --save_bet_mask &> /dev/null
+			mETA=$(echo "scale=0;  $SECONDS * 34 * ($count - $current + 1) / 60" | bc -l)
 		else
-			echo Found BFC VFAs. Skipping BFC...
+			hd-bet -i $source_dir/anat/${PREFIX}_T1w.nii.gz -o anat/${PREFIX}_desc-brain.nii.gz -device cpu --save_bet_mask &> /dev/null
+			mETA=$(echo "scale=0;  $SECONDS * 2 * ($count - $current + 1) / 60" | bc -l)
 		fi
-			# threshold and binarize wm mask
-			fslmaths 15_masked_seg.nii.gz -thr 3 -uthr 3 15_wm.nii
-			
-			# apply wm mask to all VFAs
-			fslmaths 2_bfc.nii -mas 15_wm.nii.gz 2_bfc_wm.nii 
-			fslmaths 5_bfc.nii -mas 15_wm.nii.gz 5_bfc_wm.nii
-			fslmaths 10_bfc.nii -mas 15_wm.nii.gz 10_bfc_wm.nii
-			fslmaths 12_bfc.nii -mas 15_wm.nii.gz 12_bfc_wm.nii
-			fslmaths 15_bfc.nii -mas 15_wm.nii.gz 15_bfc_wm.nii
+		mv anat/${PREFIX}_desc-brain_bet.nii.gz anat/${PREFIX}_desc-brain_mask.nii.gz
+		mv anat/${PREFIX}_desc-brain.nii.gz anat/${PREFIX}_desc-brain_T1w.nii.gz
+	elif [ -f "$source_dir/anat/${PREFIX}_T2w.nii.gz" ]
+		then
+		# assume mouse
+		# we gotta do some orientation shenanigans
+		echo
+	fi
+	prog=$(echo "scale=2;  $prog + 3.33 / $count" | bc -l)
+
+	# Motion correction of dynamic images to 2nd rep using FSL
+	# ------------------------------
+	if [ $EN_MOTION_CORR -eq 1 ]
+		then
+		if [ ! -f "dce/${PREFIX}_desc-hmc_DCE.nii.gz" ]
+			then
+			mcflirt -in $source_dir/dce/${PREFIX}_DCE.nii.gz -refvol 1 -cost mutualinfo -report -plots -o dce/${PREFIX}_desc-hmc_DCE.nii &> /dev/null
+			if [ ! -f "dce/${PREFIX}_desc-hmc_DCE.nii.gz" ]
+				then
+				echo $SUBJECT_TP_PATH/dce "Missing motion corrected DCE file." >> $LOG_FILE
+				cd $DATA_DIR
+				fail=1
+				continue
+			fi
+		fi
+		# mkdir -p $source_dir/figures &> /dev/null
+		mkdir -p figures &> /dev/null
+		max=$(python3 $SCRIPT_PATH/max_disp.py $SUBJECT_TP_PATH/dce ${PREFIX})
+		echo -e "$max" > dce/${PREFIX}_desc-hmc_maxdisp.txt
+		fslmerge -n 1 dce/${PREFIX}_desc-hmc_DCEref.nii dce/${PREFIX}_desc-hmc_DCE.nii.gz &> /dev/null
+		DCE_REF_VOL=dce/${PREFIX}_desc-hmc_DCEref.nii.gz
+	else
+		# cp $source_dir/DCE.nii.gz DCE_mc.nii.gz
+		fslmerge -n 1 dce/${PREFIX}_DCEref.nii $source_dir/dce/${PREFIX}_DCE.nii &> /dev/null
+		DCE_REF_VOL=dce/${PREFIX}_DCEref.nii.gz
+		# gunzip -f $source_dir/ref_rep_noMC.nii.gz
+	fi
+
+	REF_SPACE=space-DCEref
+	if [ ! -f anat/${PREFIX}_from-T1w_to-DCEref.mat ] && [ $EN_MOTION_CORR -eq 1 ]
+		then
+		# MPRAGE -> dynamic registration
+		antsRegistration --verbose 0 --dimensionality 3 --float 0 \
+			--collapse-output-transforms 1 --output [ anat/${PREFIX}_${REF_SPACE}_T1w,anat/${PREFIX}_${REF_SPACE}_T1w.nii.gz ] \
+			--interpolation Linear --use-histogram-matching 0 --winsorize-image-intensities [ 0.005,0.995 ] \
+			--transform Rigid[ 0.1 ] --metric MI[ $DCE_REF_VOL,${source_dir}/anat/${PREFIX}_T1w.nii.gz,1,32,Regular,0.25 ] \
+			--convergence [ 1000x500x250x100,1e-6,10 ] --shrink-factors 12x8x4x2 --smoothing-sigmas 4x3x2x1vox
+		mv anat/${PREFIX}_${REF_SPACE}_T1w0GenericAffine.mat anat/${PREFIX}_from-T1w_to-DCEref.mat
+		structural_to_DCEref=anat/${PREFIX}_from-T1w_to-DCEref.mat
+	elif [ $EN_MOTION_CORR -eq 0 ]
+		then
+		antsRegistration --verbose 0 --dimensionality 3 --float 0 \
+			--collapse-output-transforms 1 --output [ anat/${PREFIX}_${REF_SPACE}_T1w,anat/${PREFIX}_${REF_SPACE}_T1w.nii.gz ] \
+			--interpolation Linear --use-histogram-matching 0 --winsorize-image-intensities [ 0.005,0.995 ] \
+			--transform Rigid[ 0.1 ] --metric MI[ $DCE_REF_VOL,${source_dir}/anat/${PREFIX}_T1w.nii.gz,1,32,Regular,0.25 ] \
+			--convergence [ 1000x500x250x100,1e-6,10 ] --shrink-factors 12x8x4x2 --smoothing-sigmas 4x3x2x1vox
+		mv anat/${PREFIX}_${REF_SPACE}_T1w0GenericAffine.mat anat/${PREFIX}_from-T1w_to-DCEref.mat
+		structural_to_DCEref=anat/${PREFIX}_from-T1w_to-DCEref.mat
+	fi
+	T1w_to_DCEref=anat/${PREFIX}_from-T1w_to-DCEref.mat
+	# VFA -> dynamic registration
+	VFA_reg() {
+		local VFA=$1
+		antsRegistration --verbose 0 --dimensionality 3 --float 0 \
+			--collapse-output-transforms 1 --output [ anat/${PREFIX}_flip-${VFA}_${REF_SPACE},anat/${PREFIX}_flip-${VFA}_${REF_SPACE}_VFA.nii.gz ] \
+			--interpolation Linear --use-histogram-matching 0 --winsorize-image-intensities [ 0.005,0.995 ] \
+			--transform Rigid[ 0.1 ] --metric MI[ $DCE_REF_VOL,$source_dir/anat/${PREFIX}_flip-${VFA}_VFA.nii.gz,1,32,Regular,0.25 ] \
+			--convergence [ 1000x500x250x100,1e-6,10 ] --shrink-factors 12x8x4x2 --smoothing-sigmas 4x3x2x1vox
+		# mv anat/${VFA}_dynWarped.nii.gz anat/${VFA}_dyn.nii.gz
+		# TODO: re-register with fewer iterations if any slice is empty or bad
+		mv anat/${PREFIX}_flip-${VFA}_${REF_SPACE}0GenericAffine.mat anat/${PREFIX}_from-VFA${VFA}_to-DCEref.mat
+	}
+
+	for VFA in "${VFA_NUMS[@]}"; do
+		if [ ! -f "anat/${PREFIX}_flip-${VFA}_VFA.nii.gz" ]
+			then
+			VFA_reg "$VFA" &
+		fi
+	done
+	wait
+	# make array of VFA dynamic images
+	VFA_DYN_LIST=($(ls -1 anat/${PREFIX}_flip-*.nii*))
+	VFA_DYN_LIST=($(printf '%s\n' "${VFA_DYN_LIST[@]}" | grep -o -E 'flip-[0-9]+*'| sort -n | uniq))
+
+	# logic for dealing with empty slices due to registration
+	# check slices of each VFA, discard empty slices from ALL images
+	for VFA in "${VFA_DYN_LIST[@]}"; do
+		fslslice anat/${PREFIX}_${VFA}_${REF_SPACE}_VFA.nii.gz anat/${PREFIX}_${VFA}_${REF_SPACE}_VFA
+	done
+
+	# check if any slices are empty
+	EMPTY_SLICES=0
+	problem_slice=0
+	for slice in $(ls -1 anat/${PREFIX}_${VFA}_${REF_SPACE}_VFA*.nii*); do
+		if [ $(fslstats $slice -V | awk '{print $1}') -lt 100 ]
+			then
+			rm $slice
+			# remove corresponding slice from all VFAs
+			slice=$(echo $slice | grep -o -E '_[0-9]+.nii' | grep -o -E '[0-9]+')
+			rm anat/${PREFIX}_flip-*_${REF_SPACE}_VFA_slice_$slice.nii*
+			problem_slice=$slice
+			EMPTY_SLICES=1
+			echo "Removing empty slice $slice from all images" >> $LOG_FILE
+		fi
+	done
+
+	if [ $EMPTY_SLICES -eq 1 ]
+		then
+		anat_files=$(ls anat/*${REF_SPACE}*.nii* | grep -v "slice")
+		echo $anat_files
+		for registered_img in $anat_files; do
+			echo "Re-merging $registered_img"
+			reg_img_no_ext=${registered_img%.nii*}
+			reg_img_no_ext=${reg_img_no_ext%_slice_*}
+			fslslice $registered_img $reg_img_no_ext
+			rm ${reg_img_no_ext}_slice_$problem_slice.nii*
+			fslmerge -z $registered_img ${reg_img_no_ext}_slice_*.nii* &> /dev/null
+		done
+		dce_files=$(ls dce/*.nii* | grep -v "slice" | grep -v ".par")
+		for img in $dce_files; do
+			img_no_ext=${img%.nii*}
+			img_no_ext=${img_no_ext%_slice_*}
+			fslslice $img $img_no_ext
+			rm ${img_no_ext}_slice_$problem_slice.nii*
+			fslmerge -z $img ${img_no_ext}_slice_*.nii* &> /dev/null
+		done
+		# re-merge VFAs
+		for VFA in "${VFA_DYN_LIST[@]}"; do
+			fslmerge -z anat/${PREFIX}_${VFA}_${REF_SPACE}_VFA.nii.gz anat/${PREFIX}_${VFA}_${REF_SPACE}_VFA_slice_*.nii* &> /dev/null
+		done
+		rm anat/*slice_*.nii* dce/*slice_*.nii*
+	else
+		rm anat/*slice_*.nii*
+	fi
+
+	if [ ! -f anat/${PREFIX}_label-WM_mask.nii.gz ]
+		then
+		echo -ne "T1 SEG w/ FAST [=>                                                ] $prog% ($current/$count) ~$mETA min remaining \r"
+		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b --nopve -g -o anat/${PREFIX}_label- anat/${PREFIX}_desc-brain_T1w.nii.gz
+		# rename segmented files
+		mv anat/${PREFIX}_label-_bias.nii.gz anat/${PREFIX}_desc-bias_T1w.nii.gz
+		mv anat/${PREFIX}_label-_seg_0.nii.gz anat/${PREFIX}_label-CSF_mask.nii.gz
+		mv anat/${PREFIX}_label-_seg_1.nii.gz anat/${PREFIX}_label-GM_mask.nii.gz
+		mv anat/${PREFIX}_label-_seg_2.nii.gz anat/${PREFIX}_label-WM_mask.nii.gz
+		rm anat/${PREFIX}_label-_seg.nii.gz
+		antsApplyTransforms -i anat/${PREFIX}_label-WM_mask.nii.gz -r $DCE_REF_VOL -t $structural_to_DCEref -o anat/${PREFIX}_${REF_SPACE}_label-WM_mask.nii.gz &> /dev/null
+		ETA=$(echo "scale=0;  $mETA - ($SECONDS/60)" | bc -l)
+		#ETA=$(echo "scale=0;  $mETA - $mETA * .0667" | bc -l)
+		prog=$(echo "scale=2;  $prog + 6.67 / $count" | bc -l)
+	fi
+
+	fslmaths anat/${PREFIX}_${REF_SPACE}_label-WM_mask.nii.gz -thr 0.9 -bin anat/${PREFIX}_${REF_SPACE}_label-WM_mask.nii.gz &> /dev/null
+	# copy VFA files to _masked.nii
+	for VFA in "${VFA_DYN_LIST[@]}"; do
+		# get VFA number
+		# VFA_NUM=$(echo $VFA | grep -o '[0-9]*')
+		# FAST documentation recommends brain masking first
+		# fslmaths $VFA -mas T1_bet_mask.nii.gz ${VFA_NUM}_masked.nii
+		cp anat/${PREFIX}_${VFA}_${REF_SPACE}_VFA.nii.gz anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-brain_VFA.nii.gz
+	done
+	# gzip -f *_masked.nii
+
+	if [ $EN_BIAS1 -eq 1 ] && [ ! -f "anat/${PREFIX}_${VFA_LIST[0]}_${REF_SPACE}_desc-bfc_VFA.nii.gz" ]
+		then
+			VFA_FAST () {
+				local VFA=$1
+				# VFA_NUM=$(echo $VFA | grep -o '[0-9]*')
+				fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -B --nopve -o anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-brain_VFA.nii.gz
+				# ETA=$(echo "scale=0;  $mETA - ($SECONDS)/60" | bc -l)
+				# prog=$(echo "scale=2;  $prog + 6 / $count" | bc -l)
+				# echo -ne "BFC FAST VFA${VFA_NUM}  [=======>                                          ] $prog% ($current/$count) ~$ETA min remaining \r"
+				mv anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-brain_VFA_restore* anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-bfc_VFA.nii.gz
+				# rm ${VFA}_masked_[mps]*
+
+				# apply wm mask to all VFAs
+				fslmaths anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-bfc_VFA.nii.gz -mas anat/${PREFIX}_${REF_SPACE}_label-WM_mask.nii.gz anat/${PREFIX}_${VFA}_${REF_SPACE}_seg-WM_VFA.nii.gz
+			}
+
+			#echo "Bias field correction with FAST"
+			# FAST every VFA
+			echo -ne "BFC FAST VFAS  [=======>                                          ] $prog% ($current/$count) ~$ETA min remaining \r"
+			for VFA in "${VFA_DYN_LIST[@]}"; do
+			echo "BFC FAST $VFA"
+				VFA_FAST "$VFA" &
+			done
+			wait
+			echo -ne "Z NORM VFAS    [================>                             ] $prog% ($current/$count) ~$ETA min remaining \r"
+			rm -f $source_dir/[0-9]*_masked_[mps]*
 	else
 		# dumb file name management for norm only runs
-		fslmaths 2.nii -mas brain_mask.nii.gz 2_bfc.nii 
-		fslmaths 5.nii -mas brain_mask.nii.gz 5_bfc.nii
-		fslmaths 10.nii -mas brain_mask.nii.gz 10_bfc.nii
-		fslmaths 12.nii -mas brain_mask.nii.gz 12_bfc.nii
-		fslmaths 15.nii -mas brain_mask.nii.gz 15_bfc.nii
-		
-		echo Skipping BFC... but still segmenting one VFA for matter masks
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 15_bfc.nii
-		rm 15_bfc_mixeltype.nii.gz
-		rm 15_bfc_pve_0.nii.gz
-		rm 15_bfc_pve_1.nii.gz
-		rm 15_bfc_pve_2.nii.gz
-		rm 15_bfc_pveseg.nii.gz
-		#rm 15_bfc_seg.nii.gz
-		
-		# threshold and binarize wm mask
-		fslmaths 15_bfc_seg.nii.gz -thr 3 -uthr 3 15_wm.nii
+		# fslmaths 2.nii -mas T1_bet_mask.nii.gz 2_bfc.nii &> /dev/null
+		# fslmaths 5.nii -mas T1_bet_mask.nii.gz 5_bfc.nii &> /dev/null
+		# fslmaths 10.nii -mas T1_bet_mask.nii.gz 10_bfc.nii &> /dev/null
+		# fslmaths 12.nii -mas T1_bet_mask.nii.gz 12_bfc.nii &> /dev/null
+		# fslmaths 15.nii -mas T1_bet_mask.nii.gz 15_bfc.nii &> /dev/null
 		
 		# apply wm mask to all VFAs
-		fslmaths 2_bfc.nii -mas 15_wm.nii.gz 2_bfc_wm.nii 
-		fslmaths 5_bfc.nii -mas 15_wm.nii.gz 5_bfc_wm.nii
-		fslmaths 10_bfc.nii -mas 15_wm.nii.gz 10_bfc_wm.nii
-		fslmaths 12_bfc.nii -mas 15_wm.nii.gz 12_bfc_wm.nii
-		fslmaths 15_bfc.nii -mas 15_wm.nii.gz 15_bfc_wm.nii
+		# fslmaths 2_bfc.nii -mas T1_wm_mask.nii.gz 2_bfc_wm.nii &> /dev/null
+		# fslmaths 5_bfc.nii -mas T1_wm_mask.nii.gz 5_bfc_wm.nii &> /dev/null
+		# fslmaths 10_bfc.nii -mas T1_wm_mask.nii.gz 10_bfc_wm.nii &> /dev/null
+		# fslmaths 12_bfc.nii -mas T1_wm_mask.nii.gz 12_bfc_wm.nii &> /dev/null
+		# fslmaths 15_bfc.nii -mas T1_wm_mask.nii.gz 15_bfc_wm.nii &> /dev/null
+		
+		# apply MP-RAGE wm mask
+		for VFA in "${VFA_LIST[@]}"; do
+			# VFA_NUM=$(echo $VFA | grep -o '[0-9]*')
+			fslmaths anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-brain_VFA.nii.gz -mas anat/${PREFIX}_${REF_SPACE}_label-WM_mask.nii.gz anat/${PREFIX}_${VFA}_${REF_SPACE}_seg-WM_VFA.nii.gz &> /dev/null
+		done
 	fi
 
 	# Run Z-axis normalization VFA data
 	# ------------------------------
-	if [ $EN_Z_NORM -eq 1 ] 
-		then
-		echo begin slice normalization
-		python3 $SCRIPT_PATH/VFA_norm.py $SUBJECT_TP_PATH
-	fi
-
-	if [ $ff -eq 1 ]
+	if [ $EN_Z_NORM -eq 1 ]
 		then
-			if [ ! -f "15_BFC_Z.nii" ]
-				then
-					echo "Missing Z-normalized files. Z-norm likely failed due to non-existent inputs."
-					fail=1
-					cd ../..
-					continue
-			fi
+		if [ ! -f "anat/${PREFIX}_${VFA_LIST[0]}_${REF_SPACE}_desc-bfcz_VFA.nii.gz" ]
+			then
+			#echo begin slice normalization
+			python3 $SCRIPT_PATH/VFA_norm.py $SUBJECT_TP_PATH/anat $PREFIX $EN_BIAS1 &> /dev/null
+			prog=$(echo "scale=2;  $prog + .33 / $count" | bc -l)
+			echo -ne "VFA MOTIONCORR [===================>                              ] $prog% ($current/$count) ~$ETA min remaining \r"
+		fi
+		if [ ! -f "anat/${PREFIX}_${VFA_LIST[0]}_${REF_SPACE}_desc-bfcz_VFA.nii.gz" ]
+			then
+				echo $source_dir "Missing Z-normalized VFA files. Z-norm likely failed due to non-existent inputs." >> $LOG_FILE
+				cd $DATA_DIR
+				fail=1
+				continue
+		fi
+	else
+		mkdir -p figures &> /dev/null
 	fi
-
+	
+	VFA_INPUT=""
 	if [ $EN_BIAS2 -eq 1 ]
 		then
 		# 2nd Bias correction VFA data
 		# ------------------------------
 		echo Begin second round of BFC
 		# Bias field correction with FAST
-		# don't forget to remove all unnecessary images 
-		fast -t 3 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 2_BFC_Z.nii
-		rm 2_BFC_Z_mixeltype.nii.gz
-		rm 2_BFC_Z_pve_0.nii.gz
-		rm 2_BFC_Z_pve_1.nii.gz
-		rm 2_BFC_Z_pve_2.nii.gz
-		rm 2_BFC_Z_pveseg.nii.gz
-		rm 2_BFC_Z_seg.nii.gz
-		3dcalc -a 2_BFC_Z.nii -b 2_BFC_Z_bias.nii.gz -expr a/b -prefix 2_b2corr.nii -overwrite
-		
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 5_BFC_Z.nii
-		rm 5_BFC_Z_mixeltype.nii.gz
-		rm 5_BFC_Z_pve_0.nii.gz
-		rm 5_BFC_Z_pve_1.nii.gz
-		rm 5_BFC_Z_pve_2.nii.gz
-		rm 5_BFC_Z_pveseg.nii.gz
-		rm 5_BFC_Z_seg.nii.gz
-		3dcalc -a 5_BFC_Z.nii -b 5_BFC_Z_bias.nii.gz -expr a/b -prefix 5_b2corr.nii -overwrite
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 10_BFC_Z.nii
-		rm 10_BFC_Z_mixeltype.nii.gz
-		rm 10_BFC_Z_pve_0.nii.gz
-		rm 10_BFC_Z_pve_1.nii.gz
-		rm 10_BFC_Z_pve_2.nii.gz
-		rm 10_BFC_Z_pveseg.nii.gz
-		rm 10_BFC_Z_seg.nii.gz
-		3dcalc -a 10_BFC_Z.nii -b 10_BFC_Z_bias.nii.gz -expr a/b -prefix 10_b2corr.nii -overwrite
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 12_BFC_Z.nii
-		rm 12_BFC_Z_mixeltype.nii.gz
-		rm 12_BFC_Z_pve_0.nii.gz
-		rm 12_BFC_Z_pve_1.nii.gz
-		rm 12_BFC_Z_pve_2.nii.gz
-		rm 12_BFC_Z_pveseg.nii.gz
-		rm 12_BFC_Z_seg.nii.gz
-		3dcalc -a 12_BFC_Z.nii -b 12_BFC_Z_bias.nii.gz -expr a/b -prefix 12_b2corr.nii -overwrite
-
-		fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 15_BFC_Z.nii
-		rm 15_BFC_Z_mixeltype.nii.gz
-		rm 15_BFC_Z_pve_0.nii.gz
-		rm 15_BFC_Z_pve_1.nii.gz
-		rm 15_BFC_Z_pve_2.nii.gz
-		rm 15_BFC_Z_pveseg.nii.gz
-		rm 15_BFC_Z_seg.nii.gz
-		3dcalc -a 15_BFC_Z.nii -b 15_BFC_Z_bias.nii.gz -expr a/b -prefix 15_b2corr.nii -overwrite
-		
-		# concatenates 5 images in one VFA.nii image  
-		3dTcat -prefix VFA.nii 2_b2corr.nii 5_b2corr.nii 10_b2corr.nii 12_b2corr.nii 15_b2corr.nii -overwrite
+		# don't forget to remove all unnecessary images
+		for VFA in "${VFA_LIST[@]}"; do
+			VFA_NUM=$(echo $VFA | grep -o '[0-9]*')
+			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -B --nopve -o ${VFA_NUM}_BFC_Z.nii ${VFA_NUM}_BFC_Z.nii &> /dev/null
+			# fslmaths ${VFA_NUM}_BFC_Z.nii -div ${VFA_NUM}_BFC_Z_bias.nii.gz ${VFA_NUM}_b2corr.nii &> /dev/null
+			mv ${VFA_NUM}_BFC_Z_restore* ${VFA_NUM}_bfc2.nii.gz
+			rm -f ${VFA_NUM}_BFC_Z_[mps]* &> /dev/null
+		done
+		# concatenates all VFA images in one 4D VFA.nii.gz image
+		fslmerge -t VFA.nii.gz ${VFA_NUMS[@]/%/_bfc2.nii.gz} &> /dev/null
+
+		# remove all unnecessary images
+		rm [0-9]*_BFC_Z_*
 		
 	elif [ $EN_Z_NORM -eq 1 ] 
 		then
-		echo Concatenating Z-norm\'d images
-		# concatenates 5 images in one VFA.nii image
-		3dTcat -prefix VFA.nii 2_BFC_Z.nii 5_BFC_Z.nii 10_BFC_Z.nii 12_BFC_Z.nii 15_BFC_Z.nii -overwrite
-
+		#echo Concatenating Z-norm\'d images
+		# concatenates VFA images in one 4D VFA.nii.gz image
+		# fslmerge -t $source_dir/VFA_BFC_Z.nii.gz "${VFA_NUMS[@]/#/\/$source_dir\/}"_BFC_Z.nii.gz
+		for VFA in "${VFA_DYN_LIST[@]}"; do
+			VFA_INPUT+="anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-bfcz_VFA.nii.gz "
+		done
+		fslmerge -t anat/${PREFIX}_${REF_SPACE}_desc-bfczunified_VFA.nii.gz $VFA_INPUT
+		VFA_INPUT="desc-bfczunified_VFA"
 	elif [ $EN_BIAS1 -eq 1 ]
 		then
-		echo Concatenating non Z\'d images
-		3dTcat -prefix VFA.nii 2_bfc.nii 5_bfc.nii 10_bfc.nii 12_bfc.nii 15_bfc.nii -overwrite
+		# echo Concatenating non Z\'d images
+		for VFA in "${VFA_DYN_LIST[@]}"; do
+			VFA_INPUT+="anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-bfc_VFA.nii.gz "
+		done
+		fslmerge -t anat/${PREFIX}_${REF_SPACE}_desc-bfcunified_VFA.nii.gz $VFA_INPUT &> /dev/null
+		VFA_INPUT="desc-bfcunified_VFA"
 	else
 		echo Concatenating raw images
-		3dTcat -prefix VFA.nii 2_masked.nii 5_masked.nii 10_masked.nii 12_masked.nii 15_masked.nii -overwrite
+		for VFA in "${VFA_DYN_LIST[@]}"; do
+			VFA_INPUT+="anat/${PREFIX}_${VFA}_${REF_SPACE}_desc-brain_VFA.nii.gz "
+		done
+		fslmerge -t anat/${PREFIX}_${REF_SPACE}_desc-unified_VFA.nii.gz $VFA_INPUT
+		VFA_INPUT="desc-unified_VFA"
 	fi
+	gunzip -f "anat/${PREFIX}_${REF_SPACE}_$VFA_INPUT.nii.gz"
 	
-	if [ $ff -eq 1 ]
+	if [ ! -f "anat/${PREFIX}_${REF_SPACE}_$VFA_INPUT.nii" ]
 		then
-			if [ ! -f "VFA.nii" ]
-				then
-					echo "Missing VFA file. Component files may have failed."
-					fail=1
-					cd ../..
-					continue
-			fi
+			echo "$source_dir missing VFA file. Component files may have failed." >> $LOG_FILE
+			cd $DATA_DIR
+			fail=1
+			continue
 	fi
 	
-	# motion correction of VFA
-	# ------------------------------
-	mcflirt -in VFA.nii -refvol 'VFA.nii[0]' -cost mutualinfo -report -verbose -plots -o VFA_mc.nii
-	gunzip -f VFA_mc.nii.gz
-	
-	if [ $ff -eq 1 ]
-		then
-			if [ ! -f "VFA_mc.nii" ]
-				then
-					echo "Missing VFA_mc file. Motion correction may have failed."
-					fail=1
-					cd ../..
-					continue
-			fi
-	fi
-	# smooth
-	#3dBlurToFWHM -input VFA_mc.nii -FWHM 5 -prefix VFA_mc_blurred.nii
+	prog=$(echo "scale=2;  $prog + .5 / $count" | bc -l)
+	echo -ne "MAKE T1 MAPS   [===================>                              ] $prog% ($current/$count) ~$ETA min remaining \r"
 	
-	# T1 mapping where the input image is 'VFA.motioncorrected.nii'
+	# T1 mapping where the input image is 'VFA.nii'
 	# ------------------------------
-	matlab -nodisplay -r "cd('$ROCKETSHIP_PATH/parametric_scripts/custom_scripts'); addpath '$ROCKETSHIP_PATH'; addpath '$ROCKETSHIP_PATH/dce'; addpath '$ROCKETSHIP_PATH/external_programs'; addpath '$ROCKETSHIP_PATH/external_programs/niftitools'; addpath '$ROCKETSHIP_PATH/parametric_scripts'; addpath '$GPUFIT_PATH'; addpath '$GPUFIT_M_PATH'; T1mapping_fit('$SUBJECT_TP_PATH/'); exit;"
-	if [ $ff -eq 1 ]
+	matlab -nodisplay -r "cd('$ROCKETSHIP_PATH/parametric_scripts/custom_scripts'); addpath '$ROCKETSHIP_PATH'; \
+		addpath '$ROCKETSHIP_PATH/dce'; addpath '$ROCKETSHIP_PATH/external_programs'; \
+		addpath '$ROCKETSHIP_PATH/external_programs/niftitools'; addpath '$ROCKETSHIP_PATH/parametric_scripts';	\
+		addpath '$GPUFIT_PATH'; addpath '$GPUFIT_M_PATH'; T1mapping_fit('$source_dir/anat', '$SUBJECT_TP_PATH/anat', '${PREFIX}_${REF_SPACE}_${VFA_INPUT}.nii'); exit;" &> /dev/null
+	mv anat/T1_map_t1_fa_fit_${PREFIX}_${REF_SPACE}_${VFA_INPUT}.nii anat/${PREFIX}_${REF_SPACE}_T1map.nii
+	mv anat/T1_map_t1_fa_fit_${PREFIX}_${REF_SPACE}_${VFA_INPUT}.mat anat/${PREFIX}_${REF_SPACE}_T1map.mat
+	mv anat/T1_map_t1_fa_fit_${PREFIX}_${REF_SPACE}_${VFA_INPUT}.txt anat/${PREFIX}_${REF_SPACE}_T1map.txt
+	mv anat/Rsquared_t1_fa_fit_${PREFIX}_${REF_SPACE}_${VFA_INPUT}.nii anat/${PREFIX}_${REF_SPACE}_desc-rsquared_T1map.nii
+	mv anat/CI_low_t1_fa_fit_${PREFIX}_${REF_SPACE}_${VFA_INPUT}.nii anat/${PREFIX}_${REF_SPACE}_desc-CIlow_T1map.nii
+	mv anat/CI_high_t1_fa_fit_${PREFIX}_${REF_SPACE}_${VFA_INPUT}.nii anat/${PREFIX}_${REF_SPACE}_desc-CIhigh_T1map.nii
+	((diff = SECONDS - diff))
+	ETA=$(echo "scale=0;  $mETA - ($SECONDS)/60" | bc -l)
+	prog=$(echo "scale=2;  $prog + 1.33 / $count" | bc -l)
+	echo -ne "DCE MOTIONCORR [====================>                             ] $prog% ($current/$count) ~$ETA min remaining \r"
+	if [ ! -f anat/${PREFIX}_${REF_SPACE}_T1map.nii ]
 		then
-			if [ ! -f "T1_map_t1_fa_fit_VFA_mc.nii" ]
-				then
-					echo "Missing T1 map file. T1 mapping may have failed."
-					fail=1
-					cd ../..
-					continue
-			fi
+			echo $source_dir "Missing T1 map file. T1 mapping may have failed." >> $LOG_FILE
+			cd $DATA_DIR
+			fail=1
+			continue
 	fi
-	# Motion correction of dynamic images using AFNI
-	# ------------------------------
-	echo Motion correcting dynamic images...
-	mcflirt -in DCE.nii -refvol 'DCE.nii[1]' -cost mutualinfo -report -plots -o DCE_mc.nii
-	max=$(python3 $SCRIPT_PATH/max_disp.py $SUBJECT_TP_PATH)
-	echo -e "\e[1;33m$max\e[0m"
-	if [ $ff -eq 1 ]
+
+	if [ $T1_ONLY -eq 1 ]
 		then
-			if [ ! -f "DCE_mc.nii.gz" ]
-				then
-					echo "Missing motion corrected DCE file."
-					fail=1
-					cd ../..
-					continue
-			fi
+		cd $DATA_DIR
+		continue
 	fi
+	
 	# Align T1 map with Dynamic data
-	# ------------------------------
-	# MC or no?
-	3dTcat -prefix ref_rep.nii DCE_mc.nii'[1]' -overwrite
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii T1_map_t1_fa_fit_VFA_mc.nii t1_map_fixed_use_me.nii.gz
-	if [ $ff -eq 1 ]
+	# -----------------------------	
+	prog=$(echo "scale=2;  $prog + 2.77 / $count" | bc -l)
+	echo -ne "REG BET MASK   [========================>                         ] $prog% ($current/$count) ~$ETA min remaining \r"
+	antsApplyTransforms -i anat/${PREFIX}_desc-brain_mask.nii.gz -r $DCE_REF_VOL -t $structural_to_DCEref -o anat/${PREFIX}_${REF_SPACE}_desc-brain_mask_pv.nii.gz &> /dev/null
+	fslmaths anat/${PREFIX}_${REF_SPACE}_desc-brain_mask_pv.nii.gz -thr 1 -bin anat/${PREFIX}_${REF_SPACE}_desc-brain_mask.nii.gz &> /dev/null
+	rm anat/${PREFIX}_${REF_SPACE}_desc-brain_mask_pv.nii.gz
+	prog=$(echo "scale=2;  $prog + 0.55 / $count" | bc -l)
+	ETA=$(echo "scale=0;  $mETA - ($SECONDS)/60" | bc -l)
+	echo -ne "FAST DCE REP 1 [========================>                         ] $prog% ($current/$count) ~$ETA min remaining \r"
+	
+	mkdir -p figures &> /dev/null
+	if [ $USE_AUTO_AIF -eq 1 ] || [ ! -f "dce/${PREFIX}_${AIF_SUFFIX}.nii.gz" ] && [ ! -f "dce/${PREFIX}_${AIF_SUFFIX}.nii" ] && [ ! -f "dce/${PREFIX}_${AIF_TRAINING_SUFFIX}.nii.gz" ]
 		then
-			if [ ! -f "t1_map_fixed_use_me.nii.gz" ]
-				then
-					echo "Missing registered T1 map."
-					fail=1
-					cd ../..
-					continue
-			fi
+		# run AutoAIF
+		if [ $EN_MOTION_CORR -eq 1 ]
+			then
+			python3 $AUTO_AIF_PATH/main_vif.py --mode inference --input_path dce/${PREFIX}_desc-hmc_DCE.nii.gz --save_output_path $PWD/dce \
+				--model_weight_path $SCRIPT_PATH/$AUTOAIF_WEIGHT_PATH \
+				--model_name $AUTOAIF_MODEL \
+				--save_image 1 &> /dev/null
+			# rename output
+			mv dce/${PREFIX}_desc-hmc_DCE_float_mask.nii dce/${PREFIX}_desc-AIFfloat_mask.nii
+			mv dce/${PREFIX}_desc-hmc_DCE_mask.nii dce/${PREFIX}_desc-AIFtopvoxels_mask.nii
+			mv dce/${PREFIX}_desc-hmc_DCE_curve.svg figures/${PREFIX}_desc-AIF_resampledcurve.svg
+			mv dce/${PREFIX}_desc-hmc_DCE_mask.svg figures/${PREFIX}_desc-AIF_mask.svg
+			# fslmaths aif_floats.nii -thr 0.95 aif_mask.nii
+			fslmaths anat/${PREFIX}_${REF_SPACE}_T1map.nii.gz -mas dce/${PREFIX}_desc-AIFtopvoxels_mask.nii dce/${PREFIX}_desc-AIF_T1map.nii
+		else
+			python3 $AUTO_AIF_PATH/main_vif.py --mode inference --input_path $source_dir/dce/${PREFIX}_DCE.nii.gz --save_output_path $PWD/dce \
+				--model_weight_path $SCRIPT_PATH/$AUTOAIF_WEIGHT_PATH \
+				--model_name $AUTOAIF_MODEL \
+				--save_image 1 &> /dev/null
+			mv dce/${PREFIX}_DCE_float_mask.nii dce/${PREFIX}_AIFfloat_mask.nii
+			mv dce/${PREFIX}_DCE_mask.nii dce/${PREFIX}_AIFtopvoxels_mask.nii
+			mv dce/${PREFIX}_DCE_curve.svg figures/${PREFIX}_AIF_resampledcurve.svg
+			mv dce/${PREFIX}_DCE_mask.svg figures/${PREFIX}_AIF_mask.svg
+			fslmaths anat/${PREFIX}_${REF_SPACE}_T1map.nii.gz -mas dce/${PREFIX}_AIFtopvoxels_mask.nii dce/${PREFIX}_desc-AIF_T1map.nii
+		fi
+	elif [ $USE_AUTO_AIF -eq 2 ]
+		then
+		# use all available manual AIFs, including those reserved for training
+		if [ -f "dce/${PREFIX}_${AIF_SUFFIX}.nii.gz" ]
+			then
+			# use manual AIF
+			fslmaths anat/${PREFIX}_${REF_SPACE}_T1map.nii.gz -mas dce/${PREFIX}_${AIF_SUFFIX}.nii.gz dce/${PREFIX}_desc-AIF_T1map.nii.gz
+		elif [ -f "dce/${PREFIX}_${AIF_TRAINING_SUFFIX}.nii.gz" ]
+			then
+			# use training manual AIF
+			fslmaths anat/${PREFIX}_${REF_SPACE}_T1map.nii.gz -mas dce/${PREFIX}_${AIF_TRAINING_SUFFIX}.nii.gz dce/${PREFIX}_desc-AIF_T1map.nii.gz
+		fi
+	else
+		# use manual AIF
+		fslmaths anat/${PREFIX}_${REF_SPACE}_T1map.nii.gz -mas dce/${PREFIX}_${AIF_SUFFIX}.nii.gz dce/${PREFIX}_desc-AIF_T1map.nii.gz
 	fi
-	# align and apply brain mask
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii brain_mask.nii.gz brain_mask_dyn.nii.gz
-	
 	# ensure AIF is included in mask
-	fslcpgeom 2.nii brain_mask_dyn.nii
-	cp aif.nii aif_aligned.nii
-	fslcpgeom brain_mask_dyn.nii aif_aligned.nii
-	fslmaths aif_aligned.nii -thr 0 aif_pos.nii
-	rm aif_aligned.nii
-	fslmaths brain_mask_dyn.nii -add aif_pos.nii -thr 1 -bin brain_mask_dyn_aif.nii
-	fslmaths DCE_mc.nii -mas brain_mask_dyn_aif.nii.gz DCE_mc_masked.nii
-		
+	# fslcpgeom 2.nii T1_bet_mask_dyn.nii.gz
+	cp dce/${PREFIX}_desc-AIF_T1map.nii.gz dce/${PREFIX}_desc-AIFaligned_T1map.nii.gz
+	fslcpgeom anat/${PREFIX}_${REF_SPACE}_desc-brain_mask.nii.gz dce/${PREFIX}_desc-AIFaligned_T1map.nii
+	fslmaths dce/${PREFIX}_desc-AIFaligned_T1map.nii.gz -thr 0 dce/${PREFIX}_desc-AIFpos_T1map.nii &> /dev/null
+	rm dce/${PREFIX}_desc-AIFaligned_T1map.nii.gz
+	fslmaths anat/${PREFIX}_${REF_SPACE}_desc-brain_mask.nii.gz -add dce/${PREFIX}_desc-AIFpos_T1map.nii -thr 1 -bin anat/${PREFIX}_${REF_SPACE}_desc-brainAIF_mask.nii.gz &> /dev/null
+	# apply AIF mask to all DCE images
+	if [ $EN_MOTION_CORR -eq 1 ]
+		then
+		fslmaths dce/${PREFIX}_desc-hmc_DCE.nii.gz -mas anat/${PREFIX}_${REF_SPACE}_desc-brainAIF_mask.nii.gz dce/${PREFIX}_desc-AIFincluded_DCE.nii.gz &> /dev/null
+	else
+		fslmaths $source_dir/dce/${PREFIX}_DCE.nii.gz -mas anat/${PREFIX}_${REF_SPACE}_desc-brainAIF_mask.nii.gz dce/${PREFIX}_desc-AIFincluded_DCE.nii.gz &> /dev/null
+	fi
 	if [ $EN_BIAS1 -eq 1 ]
 		then
-		if [ ! -f "DCE_mc_bfc.nii" ]
+		if [ ! -f "dce/${PREFIX}_desc-bfc_DCE.nii.gz" ]
 			then
 			# Applying bias field correction on dynamic images
 			# ------------------------------
-			echo Applying BFC to dynamic images...
-			3dTcat -prefix 1st_rep.nii DCE_mc_masked.nii'[0]' -overwrite # extract images from different DCE repetitions
-			3dTcat -prefix 5th_rep.nii DCE_mc_masked.nii'[4]' -overwrite
-			3dTcat -prefix 10th_rep.nii DCE_mc_masked.nii'[9]' -overwrite
-			3dTcat -prefix 20th_rep.nii DCE_mc_masked.nii'[19]' -overwrite
-			3dTcat -prefix 30th_rep.nii DCE_mc_masked.nii'[29]' -overwrite
-			3dTcat -prefix 40th_rep.nii DCE_mc_masked.nii'[39]' -overwrite
-			3dTcat -prefix 50th_rep.nii DCE_mc_masked.nii'[49]' -overwrite
-			3dTcat -prefix 60th_rep.nii DCE_mc_masked.nii'[59]' -overwrite
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 1st_rep.nii
-			rm 1st_rep_mixeltype.nii.gz
-			rm 1st_rep_pve_0.nii.gz
-			rm 1st_rep_pve_1.nii.gz
-			rm 1st_rep_pve_2.nii.gz
-			rm 1st_rep_pveseg.nii.gz
-			rm 1st_rep_seg.nii.gz
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 5th_rep.nii
-			rm 5th_rep_mixeltype.nii.gz
-			rm 5th_rep_pve_0.nii.gz
-			rm 5th_rep_pve_1.nii.gz
-			rm 5th_rep_pve_2.nii.gz
-			rm 5th_rep_pveseg.nii.gz
-			rm 5th_rep_seg.nii.gz
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 10th_rep.nii
-			rm 10th_rep_mixeltype.nii.gz
-			rm 10th_rep_pve_0.nii.gz
-			rm 10th_rep_pve_1.nii.gz
-			rm 10th_rep_pve_2.nii.gz
-			rm 10th_rep_pveseg.nii.gz
-			rm 10th_rep_seg.nii.gz
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 20th_rep.nii
-			rm 20th_rep_mixeltype.nii.gz
-			rm 20th_rep_pve_0.nii.gz
-			rm 20th_rep_pve_1.nii.gz
-			rm 20th_rep_pve_2.nii.gz
-			rm 20th_rep_pveseg.nii.gz
-			rm 20th_rep_seg.nii.gz
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 30th_rep.nii
-			rm 30th_rep_mixeltype.nii.gz
-			rm 30th_rep_pve_0.nii.gz
-			rm 30th_rep_pve_1.nii.gz
-			rm 30th_rep_pve_2.nii.gz
-			rm 30th_rep_pveseg.nii.gz
-			rm 30th_rep_seg.nii.gz
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 40th_rep.nii
-			rm 40th_rep_mixeltype.nii.gz
-			rm 40th_rep_pve_0.nii.gz
-			rm 40th_rep_pve_1.nii.gz
-			rm 40th_rep_pve_2.nii.gz
-			rm 40th_rep_pveseg.nii.gz
-			rm 40th_rep_seg.nii.gz
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 50th_rep.nii
-			rm 50th_rep_mixeltype.nii.gz
-			rm 50th_rep_pve_0.nii.gz
-			rm 50th_rep_pve_1.nii.gz
-			rm 50th_rep_pve_2.nii.gz
-			rm 50th_rep_pveseg.nii.gz
-			rm 50th_rep_seg.nii.gz
-	
-			fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b -o 60th_rep.nii
-			rm 60th_rep_mixeltype.nii.gz
-			rm 60th_rep_pve_0.nii.gz
-			rm 60th_rep_pve_1.nii.gz
-			rm 60th_rep_pve_2.nii.gz
-			rm 60th_rep_pveseg.nii.gz
-			rm 60th_rep_seg.nii.gz
-	
+			#echo Applying BFC to dynamic images...
+			reps=$(fslnvols dce/${PREFIX}_desc-AIFincluded_DCE.nii.gz)
+			rep_interval=$((reps / 8))
+			# round rep_interval up
+			rep_interval=$(echo "scale=0; ($rep_interval + 0.5) / 1" | bc -l)
+
+			# take 9 repetitions with rep_interval from DCE_mc_masked.nii
+			fslmerge -n 0 rep_0.nii dce/${PREFIX}_desc-AIFincluded_DCE.nii.gz &> /dev/null
+			for i in {1..8}
+			do
+				# name file with rep_interval*i
+				fslmerge -n $((rep_interval*i-1)) rep_$((rep_interval*i-1)).nii dce/${PREFIX}_desc-AIFincluded_DCE.nii.gz &> /dev/null
+			done
+			wait
+
+			DCE_FAST () {
+				local i=$1
+				local rep_interval=$2
+				# if i is 0, then we're on the first repetition
+				if [ ! $i -eq 0 ]
+					then
+					# run FAST on rep_interval*i
+					fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b --nopve -o rep_$((rep_interval*i-1)).nii
+				else
+					# run FAST on first repetition
+					fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -b --nopve -o rep_0.nii
+				fi
+			}
+			
+			# BFC each repetition
+			ETA=$(echo "scale=0;  $mETA - ($SECONDS)/60" | bc -l)
+			prog=$(echo "scale=2;  $prog + 6 / $count" | bc -l)
+			echo -ne "FAST DCE 8REPS [===========================>                      ] $prog% ($current/$count) ~$ETA min remaining \r"
+			DCE_FAST "0" "$rep_interval" &
+			for i in {1..8}
+			do
+				DCE_FAST "$i" "$rep_interval" &
+				# echo -ne "FAST DCE REP $((rep_interval*i-1)) [====================================>             ] $prog% ($current/$count) ~$ETA min remaining \r"
+			done
+			wait
+			ETA=$(echo "scale=0;  $mETA - ($SECONDS)/60" | bc -l)
+			prog=$(echo "scale=2;  $prog + 48 / $count" | bc -l)
+			echo -ne "DCE BFC + NORM [================================================> ] $prog% ($current/$count) ~$ETA min remaining \r"
+			
 			# Concatenation1
-			3dTcat -prefix dyn_bias.nii 1st_rep_bias.nii.gz 5th_rep_bias.nii.gz 10th_rep_bias.nii.gz 20th_rep_bias.nii.gz 30th_rep_bias.nii.gz 40th_rep_bias.nii.gz 50th_rep_bias.nii.gz 60th_rep_bias.nii.gz -overwrite
-	
+			fslmerge -t dce/dyn_bias.nii.gz rep_*_bias.nii.gz &> /dev/null
+
 			# Computing average across 8 bias field that have been sampled
-			3dTstat -mean -prefix mean_dyn_bias_map.nii dyn_bias.nii'[0..7]' -overwrite
+			fslmaths dce/dyn_bias.nii.gz -Tmean dce/mean_dyn_bias_map.nii.gz &> /dev/null
 	
 			# Normalizing motion corrected DCE image with mean bias field 
-			3dcalc -a DCE_mc_masked.nii -b mean_dyn_bias_map.nii -expr a/b -prefix DCE_mc_bfc.nii -overwrite
-	
-			# don't forget to remove all unnecessary images 
-			rm 1st_rep.nii
-			rm 1st_rep_bias.nii.gz
-			rm 5th_rep.nii
-			rm 5th_rep_bias.nii.gz
-			rm 10th_rep.nii
-			rm 10th_rep_bias.nii.gz
-			rm 20th_rep.nii
-			rm 20th_rep_bias.nii.gz
-			rm 30th_rep.nii
-			rm 30th_rep_bias.nii.gz
-			rm 40th_rep.nii
-			rm 40th_rep_bias.nii.gz
-			rm 50th_rep.nii
-			rm 50th_rep_bias.nii.gz
-			rm 60th_rep.nii
-			rm 60th_rep_bias.nii.gz
+			fslmaths dce/${PREFIX}_desc-AIFincluded_DCE.nii.gz -div dce/mean_dyn_bias_map.nii.gz dce/${PREFIX}_desc-bfc_DCE.nii.gz &> /dev/null
+
+			mv dce/dyn_bias.nii.gz dce/${PREFIX}_desc-biases_DCE.nii.gz
+			mv dce/mean_dyn_bias_map.nii.gz dce/${PREFIX}_desc-meanbias_DCE.nii.gz
+			# remove sampled files
+			rm rep_*.nii.gz
 		else
-			echo Skipping DCE BFC...
+			echo Skipping DCE BFC because it already exists...
 		fi
 	else
 		#echo Motion correcting dynamic set
-		#3dvolreg -heptic -verbose -base 'DCE.nii[1]' -dfile DCE_motion.txt -prefix DCE_mc_bfc.nii DCE.nii
-		#3dTcat -prefix ref_rep.nii dce_mc_bfc'[1]'
-		gunzip -f DCE_mc.nii.gz
-		mv DCE_mc.nii DCE_mc_bfc.nii
+		cp dce/${PREFIX}_desc-AIFincluded_DCE.nii.gz dce/${PREFIX}_desc-bfc_DCE.nii.gz
+		# gunzip -f DCE_mc_bfc.nii.gz
 	fi
 	
 	# align existing white matter mask to dynamic images and re-binarize
-	bash $SCRIPT_PATH/tktregistration.sh ref_rep.nii 15_wm.nii.gz 15_wm_mask_dyn.nii.gz
-	fslmaths 15_wm_mask_dyn.nii.gz -thr 1.7 -bin 15_wm_mask_dyn.nii
+	# antsApplyTransforms -i T1_wm_mask.nii.gz -r ref_rep.nii -t T1_dyn0GenericAffine.mat -o T1_wm_mask_dyn_pv.nii &> /dev/null
 	
 	# apply wm mask to all DCE images
-	fslmaths DCE_mc_bfc.nii -mas 15_wm_mask_dyn.nii.gz DCE_mc_bfc_wm.nii.gz
+	if [ $EN_BIAS1 -eq 1 ]
+		then
+		fslmaths dce/${PREFIX}_desc-bfc_DCE.nii.gz -mas anat/${PREFIX}_${VFA}_${REF_SPACE}_seg-WM_VFA.nii.gz dce/${PREFIX}_seg-WM_DCE.nii.gz &> /dev/null
+	else
+		fslmaths $source_dir/dce/${PREFIX}_DCE.nii.gz -mas anat/${PREFIX}_${REF_SPACE}_label-WM_mask.nii.gz dce/${PREFIX}_seg-WM_DCE.nii.gz &> /dev/null
+	fi
 
 	# normalize dynamic images
 	# ------------------------------
-	echo Normalizing dynamic images...
-	python3 $SCRIPT_PATH/DCE_norm.py $SUBJECT_TP_PATH
-	if [ $ff -eq 1 ]
+	#echo Normalizing dynamic images...
+	if [ $EN_Z_NORM -eq 1 ]
 		then
-			if [ ! -f "DCE_mc_bfc_norm.nii" ]
-				then
-					echo "Missing normalized DCE file."
-					fail=1
-					cd ../..
-					continue
-			fi
+		python3 $SCRIPT_PATH/DCE_norm.py $SUBJECT_TP_PATH/dce &> /dev/null
+	else
+		cp dce/${PREFIX}_desc-bfc_DCE.nii.gz dce/${PREFIX}_desc-bfcz_DCE.nii.gz
 	fi
-	# smooth dynamic set
-	#3dBlurToFWHM -input DCE_mc_bfc_norm.nii -FWHM 4 -prefix DCE_mc_bfc_norm_blurred.nii
+	gzip -f dce/${PREFIX}_desc-bfcz_DCE.nii
 
-	cd ../../
-	echo $dir preprocessing complete! Be sure to have an AIF drawn \(aif.nii\) for DCE.
+	if [ ! -f "dce/${PREFIX}_desc-bfcz_DCE.nii.gz" ]
+		then
+			echo $source_dir "Missing normalized DCE file." >> $LOG_FILE
+			cd $DATA_DIR
+			fail=1
+			continue
+	fi
+
+	ETA=$(echo "scale=0;  $mETA - ($SECONDS)/60" | bc -l)
+	prog=$(echo "scale=2;  $prog + .6 / $count" | bc -l)
+	echo -ne "SUBJ COMPLETED [==================================================] $prog% ($current/$count) ~$ETA min remaining \r"
+
+	cd $DATA_DIR
+	echo $source_dir preprocessing complete! >> $LOG_FILE
+	let successes++
 done
 
+	prog=$(echo "scale=2;  100.00" | bc -l)
+	echo -ne "PREP COMPLETED [==================================================] $prog% ($current/$count)"
+
+((failures=count-successes))
+echo Completed preprocessing for $count cases. >> $LOG_FILE
+echo $successes subjects succeeded >> $LOG_FILE
+echo $failures subjects failed >> $LOG_FILE
+
 if [ $fail -eq 1 ]
 	then
 	exit 1
diff --git a/report.py b/report.py
deleted file mode 100644
index 4731c8f..0000000
--- a/report.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
-import matplotlib.gridspec as gridspec
-from matplotlib.axes import _secondary_axes
-from matplotlib.animation import adjusted_figsize
-from matplotlib.pyplot import subplots_adjust
-from mpl_toolkits.axes_grid1 import ImageGrid
-import numpy as np
-import nibabel as nib
-from pathlib import Path
-import sys
-from nibabel import orientations
-
-
-dir = Path(sys.argv[1])
-try:
-    cmap = str(Path(sys.argv[2]))
-except:
-    cmap = 'gnuplot'
-
-
-analysis = mpimg.imread(str(dir) + '/T1_Ktrans_analysis.png')
-zeros = mpimg.imread(str(dir) + '/T1_Ktrans_zeros.png')
-aif_curve = mpimg.imread(str(dir) + '/dceAIF_fitting.png')
-timecurves = mpimg.imread(str(dir) + '/dce_timecurves.png')
-curves = []
-curves.append(mpimg.imread(str(dir) + '/dceAIF_fitting.png'))
-curves.append(mpimg.imread(str(dir) + '/dce_timecurves.png'))
-ktrans = nib.load(str(dir) + '/dce_patlak_fit_Ktrans.nii')
-
-dim = {0,1,2}
-ktrans_data = ktrans.get_fdata()
-ktrans_shape = ktrans_data.shape
-slice_num = min(ktrans_shape[0], ktrans_shape[1], ktrans_shape[2])
-slice_loc = ktrans_shape.index(slice_num)
-ktrans_data = np.reshape(ktrans_data, (ktrans_shape[min(dim-set([slice_loc]))], ktrans_shape[max(dim-set([slice_loc]))], slice_num))
-slices = []
-for i in range(slice_num):
-    slices.append(ktrans_data[:,:,i].T)
-
-fig, axs = plt.subplots(4, 1, figsize=(8.5,11))
-subject = str(dir).split('/')[5]
-axs[0].set_title(subject, y=1.02) #+ " (" + str(dir) + "")
-plt.suptitle(str(dir), fontsize='small', y=1)
-axs[0].axis('off')
-axs[0].imshow(analysis)
-axs[1].axis('off')
-x = axs[1].imshow(zeros, cmap='gnuplot', vmin=0, vmax=.009)
-axs[2].axis('off')
-axs[3].axis('off')
-
-gridspec = axs[2].get_subplotspec().get_gridspec()
-gridspec2 = axs[3].get_subplotspec().get_gridspec()
-subfig = fig.add_subfigure(gridspec[2,:])
-subfig2 = fig.add_subfigure(gridspec[3,:])
-row = subfig.subplots(2,int(slice_num/2))
-curve_rows = subfig2.subplots(1,2)
-
-# cmap = 'gnuplot'
-i = 0
-for ax in row.flat:
-    ax.axis('off')
-    ax.set_xlim(30, 290)
-    ax.set_ylim(20, 310)
-    # ax.pcolormesh(slices[i], cmap=cmap, vmin=0, vmax=.009)
-    ax.imshow(slices[i], cmap=cmap, vmin=0, vmax=0.009)
-    i+=1
-
-i = 0
-for ax in curve_rows:
-    ax.axis('off')
-    ax.imshow(curves[i])
-    i+=1
-
-# fig.tight_layout(pad=-.7)
-subplots_adjust(top=0.99, bottom=0.0, left=-0.0, right=1.0, hspace=0, wspace=-.0)
-# cax = fig.add_axes([0.0, 0.23, 1, .02])
-bozo = fig.colorbar(x, orientation='horizontal', label='Ktrans (/min)', pad=.02, aspect = 60)
-bozo.set_label('Ktrans (/min)', labelpad=-34.5, fontsize = 'x-small')
-
-# plt.show()
-plt.savefig(str(dir) + '/report.png', bbox_inches='tight')
diff --git a/requirements.txt b/requirements.txt
index 7857774..881ea65 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,17 @@
-matplotlib==3.6.0
-nibabel==3.1.1
-numpy==1.23.2
-Pillow==9.2.0
-PySimpleGUI==4.60.3
-scipy==1.9.0
+CairoSVG
+imageio
+Jinja2
+lmfit
+matplotlib
+nibabel
+nilearn
+numpy
+pandas
+Pillow
+pydicom
+PySimpleGUI
+reportlab
+scikit_learn
+scipy
+openpyxl
+xlsxwriter
diff --git a/run_docker.sh b/run_docker.sh
new file mode 100755
index 0000000..ac5ce5c
--- /dev/null
+++ b/run_docker.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+
+# MAKE SURE MATLAB & FREESURFER LICENSE FILES, DATA DIRECTORY, SCRIPT PREFERENCE FOLDER (docker/files/) AND /etc/ ARE SHARED WITH DOCKER
+
+bozo=$(cat /home/*/.matlab/R*_licenses/*lic* || cat /usr/local/MATLAB/*/licenses/*lic* | grep _HOSTID)
+
+MAC=${bozo#*MATLAB_HOSTID=}
+MAC=${MAC%%:*}
+MAC=${MAC:0:2}:${MAC:2:2}:${MAC:4:2}:${MAC:6:2}:${MAC:8:2}:${MAC:10:2}
+# echo $MAC
+
+MATLAB_PATH=/usr/local/MATLAB/$(ls /usr/local/MATLAB/ | sort -V | tail -n 1)
+# echo $MATLAB_PATH
+MATLAB_VERSION=$(ls /usr/local/MATLAB/ | sort -V | tail -n 1)-dev
+
+LICENSE=$(ls $MATLAB_PATH/licenses)
+# echo $LICENSE
+
+export UID=$(id -u)
+export GID=$(id -g)
+
+sudo docker run --rm -it -e MLM_LICENSE_FILE=/opt/matlab/licenses/$LICENSE \
+    -v /media/network_mriphysics/USC-PPG/docker_test:/data/ \
+    -v $FREESURFER_HOME/license.txt:/usr/local/freesurfer/8.1.0/license.txt \
+    -v $MATLAB_PATH/licenses:/opt/matlab/licenses \
+    -v /etc/passwd:/etc/passwd:ro \
+    -v $PWD/docker/files/script_preferences.txt:/opt/ROCKETSHIP/ROCKETSHIP-dev/script_preferences.txt \
+    -v $PWD/docker/files/model_weight_huber1.h5:/opt/vascular_function/docker/files/model_weight_huber1.h5 \
+    --shm-size=512M --mac-address $MAC \
+    --user $UID:$GID \
+    --gpus all \
+    lsaca05/dce:$MATLAB_VERSION
diff --git a/sort_dicom.py b/sort_dicom.py
new file mode 100755
index 0000000..aed57b5
--- /dev/null
+++ b/sort_dicom.py
@@ -0,0 +1,117 @@
+#!/usr/bin/python3
+
+
+from datetime import time, timedelta, datetime
+from statistics import mean
+import json
+import pydicom
+import subprocess
+import os
+import shutil
+import sys
+
+sort_dir = "/media/network_mriphysics/USC-PPG/bids_ppg/sourcedata/dicom"
+output_dir = "/media/network_mriphysics/USC-PPG/bids_ppg/derivatives"
+
+print(sort_dir)
+directory = os.fsencode(sort_dir)
+dir_str = os.fsdecode(directory)
+
+subfolders = [ f for f in os.scandir(directory) if f.is_dir() ]
+for subfolder in subfolders:  
+    new_dir = os.fsdecode(subfolder.path)
+    subname = os.fsdecode(subfolder.name)
+    new_7z = os.path.join(dir_str,subname+".7z")
+    if not "_s2" in subname.lower():
+        session_id = '01'
+        subject_id = subname
+    else:
+        session_id = '02'
+        subject_id = subname.replace("_s2","")
+        subject_id = subject_id.replace("_S2","") #cover both cases
+
+    print("Processing ", subject_id, " session ", session_id)
+    
+    
+    #7zip
+    try:
+        #output = subprocess.check_output(['7z',"a",new_7z,new_dir+"/*"])
+        print("7z successful")
+    except:
+        print(subname + " failed 7zip")
+    
+    #find dicom and log directories
+    file_list = os.listdir(new_dir)
+    #folder should only have a "dicom" folder and a "log" folder
+    if not len(file_list)==2:
+        sys.exit("warning: folder has wrong structure (doesn't have only 2 subdirectories), exiting")
+    dicom_dir = ""
+    log_dir = ""
+    if os.path.isdir(os.path.join(new_dir,file_list[0])):
+        if file_list[0].lower()=='dicom':
+            dicom_dir = os.path.join(new_dir,file_list[0])
+        if file_list[0].lower()=='log':
+            log_dir = os.path.join(new_dir,file_list[0])
+    if os.path.isdir(os.path.join(new_dir,file_list[1])):
+        if file_list[1].lower()=='dicom':
+            dicom_dir = os.path.join(new_dir,file_list[1])
+        if file_list[1].lower()=='log':
+            log_dir = os.path.join(new_dir,file_list[1])
+    if dicom_dir=="" or log_dir=="":
+        sys.exit("warning: cannot find DICOM or LOG folder, exiting")
+
+    # Calculate temporal resolution from difference between acquisition times
+    # dce_dir = dicom_dir + "/I1164341/dicom"
+    
+    # ac_time = []
+    # for i in range(1,2000,40):
+    #     file = dce_dir + "/15-" + str(i) + ".dcm"
+    #     dataset = pydicom.dcmread(file, specific_tags=["AcquisitionTime"])
+    #     formatted_time = dataset.AcquisitionTime
+    #     formatted_time = formatted_time[0:2] + ':' + formatted_time[2:4] + ':' + formatted_time[4:]
+    #     struct = time.fromisoformat(formatted_time)
+    #     bozo = timedelta(hours=struct.hour, minutes=struct.minute, seconds=struct.second, microseconds=struct.microsecond)
+    #     ac_time.append(datetime.strptime(formatted_time, '%H:%M:%S.%f'))
+    # # diff = []
+    # # for i in range(1,50):
+    # #     diff.append(float(str(ac_time[i] - ac_time[i-1])[5:]))
+    # diff = ac_time[1] - ac_time[0]
+    # TemporalResolution = str(diff)[5:]
+    #
+    # with open('config.json', 'r+') as f:
+    #     data = json.load(f)
+    #     data['descriptions'][1]['sidecarChanges']['TemporalResolution'] = str(TemporalResolution)
+    #     f.seek(0)
+    #     json.dump(data,f,indent=4)
+    #     f.truncate()
+    
+    #DICOM to BIDS conversion
+    try:
+        
+        output = subprocess.check_output(['dcm2bids',
+                                          "-d", dicom_dir,
+                                          "-p", subject_id,
+                                          "-s", session_id,
+                                          "-c", "config.json",
+                                          "-o", output_dir])
+        print("dcm2bids successful")
+    except:
+        print(subname+" failed dcm2bids")
+    print("Files sorted")
+    #remove unsorted files
+    #shutil.rmtree(dicom_dir)
+    
+    #move log files
+    bids_dir = os.path.join(output_dir,"sub-"+subject_id,"ses-"+session_id,"logs")
+    os.makedirs(bids_dir,exist_ok=True)
+    for root, dirs, files in os.walk(log_dir):  
+        for f in files:  
+            shutil.copy(os.path.join(root,f),bids_dir)
+    
+    
+    #move unzipped files
+    move_dir = new_dir
+    dest_dir = os.path.join(dir_str,"..")
+    shutil.move(move_dir,dest_dir)
+
+    print("Completed")
diff --git a/template.html b/template.html
new file mode 100644
index 0000000..40580dd
--- /dev/null
+++ b/template.html
@@ -0,0 +1,285 @@
+<html>
+<head>
+    <title>{{ title }}</title>
+    <style>
+        body {
+            padding-top: 35px;
+        }
+
+        /* Styles for the navbar */
+        .navbar {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            background-color: #333;
+            height: 50px;
+            display: flex;
+            justify-content: flex-start;
+            align-items: center;
+            z-index: 999;
+        }
+
+        .navbar-nav {
+            list-style-type: none;
+            display: flex;
+            margin: 0;
+            padding: 0;
+        }
+
+        .navbar-nav li {
+        position: relative;
+        }
+
+        .navbar-nav ul.dropdown {
+            position: absolute;
+            top: 30px;
+            display: none;
+            background-color: #fff;
+            padding: 0px;
+            box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+            list-style-type: none;
+            width: 100%;
+        }
+
+        .navbar-nav li:hover ul.dropdown {
+            display: block;
+        }
+
+        .navbar-nav ul.dropdown li {
+            margin: 10px 0;
+        }
+
+        .navbar-nav ul.dropdown li a {
+            color: #000;
+            text-decoration: none;
+        }
+
+        .navbar-nav ul.dropdown li:hover a {
+            background-color: #f1f1f1;
+        }
+
+        .nav-item {
+            margin-right: 10px;
+        }
+
+        .nav-item a {
+            color: #ffffff;
+            text-decoration: none;
+            padding: 10px;
+        }
+
+        .nav-item a:hover {
+            background-color: #555;
+        }
+
+        .image-container {
+            position: relative;
+            width: 75%;
+            height: 40%;
+            background-color: rgba(0, 0, 0, 0);
+        }
+
+        .image-container img {
+            position: absolute;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+            transition: opacity 1s ease-in-out;
+        }
+
+        .image-container img.fade {
+            opacity: 0;
+        }
+
+        .image-container:hover img {
+            opacity: 0;
+        }
+
+        .dce-image-container {
+            display: flex;
+        }
+
+        .dce-image-container img {
+            flex: 1;
+            width: 100%;
+            height: auto;
+        }
+
+        
+    </style>
+    </head>
+<body>
+    <nav class="navbar">
+        <ul class="navbar-nav">
+            <li class="nav-item"><a href="#">Summary</a></li>
+            <li class="nav-item">
+                <a href="#Preprocessing">Preprocessing</a>
+                <ul class="dropdown">
+                    <li><a href="#T1w_masks">T1w Masks</a></li>
+                    <li><a href="#T1w_to_DCEref">T1w->DCEref</a></li>
+                    <li><a href="#VFA_Z-norm">VFA Z</a></li>
+                    <li><a href="#T1_map">T1 Map</a></li>
+                    <li><a href="#MCFLIRT">MCFLIRT</a></li>
+                    <li><a href="#AIF">AIF</a></li>
+                    <li><a href="#T1w_dyn">T1w->dyn</a></li>
+                    <li><a href="#DCE_Z-norm">DCE Z</a></li>
+                </ul>
+            </li>
+            
+            <li class="nav-item">
+                <a href="#DCE">DCE</a>
+                <ul class="dropdown">
+                    <li><a href="#RUN_A">RUNA</a></li>
+                    <li><a href="#RUN_B">RUNB</a></li>
+                    <li><a href="#RUN_D">RUND</a></li>
+                    <li><a href="#Ktrans">Ktrans</a></li>
+                    <li><a href="#MNI">MNI</a></li>
+                </ul>
+            </li>
+        </ul>
+    </nav>
+    <p></p>
+    <h1 id="Summary">Summary</h1>
+    <ul>
+        <li>{{ Subject }}</li>
+        <li>{{ Timepoint }}</li>
+        <li>{{ Date }}</li>
+        <li>{{ Commit }}</li>
+        <li>{{ ROCKETSHIP_commit }}</li>
+        <li>{{ Institute }}</li>
+        <li>{{ Machine }}</li>
+    </ul>
+    <h2>Ktrans (/min)</h2>
+    <img src="{{ ktrans }}" alt="{{ ELYOUEL }}" width="90%" alt="{{ image_alt1 }}"></img>
+    <h1 id="Preprocessing">Preprocessing</h1>
+    <h2>Anatomical Info</h2>
+    <li>{{ Dimensions }}</li>
+    <li>{{ Voxel_Size }}</li>
+    <h2 id="T1w_masks">T1w Segmentation and Brain Mask</h2>
+    <div id="image-container" class="image-container">
+        <img id="image1_mask" src="{{ T1w }}" alt="{{ image_alt1 }}">
+        <img id="image2_mask" src="{{ T1w_mask }}" alt="{{ image_alt2 }}">
+    </div>
+    <div id="image-container-1" class="image-container">
+        <img id="image1wm" src="{{ T1w }}" alt="{{ image_alt1 }}">
+        <img id="image2wm" src="{{ T1w_wm }}" alt="{{ image_alt2 }}">
+    </div>
+    <div id="image-container-2" class="image-container">
+        <img id="image1gm" src="{{ T1w }}" alt="{{ image_alt1 }}">
+        <img id="image2gm" src="{{ T1w_gm }}" alt="{{ image_alt2 }}">
+    </div>
+    <h2 id="T1w_to_DCEref">T1w Registration to DCEref Space</h2>
+    <img src="{{ T1w_to_DCEref }}" alt="{{ image_alt1 }}"></img>
+    <h2 id="VFA_Z-norm">VFA Z-Normalization</h2>
+    {% for i in range(0, num_FAs) %}
+        <h3> {{ FAs[i] }} </h3>
+        <img src="{{ Zs[i] }}" alt="{{ image_alt1 }}" width="1000"></img>
+    {% endfor %}
+    <h2 id="T1_map">T1 Map</h2>
+    <!-- <p>check for T1 ROCKETSHIP params</p> -->
+    <li>{{ T1_TR }}</li>
+    <li>{{ T1_FAs }}</li>
+    <li>{{ T1_GPU }}</li>
+    <li>{{ T1_wm_median }}</li>
+    <li>{{ T1_wm_std }}</li>
+    <li>{{ T1_gm_median }}</li>
+    <li>{{ T1_gm_std }}</li>
+    <img src="{{ T1_map }}" alt="{{ image_alt1 }}" width="75%"></img>
+    <!-- <p>T1 wm/gm graphs</p> -->
+    <h2 id="MCFLIRT">MCFLIRT Displacements</h2>
+    <img src="{{ displacements }}" alt="{{ image_alt1 }}" width="1000"></img>
+    <h2 id="AIF">AIF mask and curve</h2>
+    <img src="{{ AIF_mask }}" alt="AutoAIF img missing" width="50%"></img>
+    <img src="{{ AIF_curve }}" alt="AutoAIF img missing" width="50%"></img>
+    <br>
+    <img src="{{ AIF_overlay }}" width="50%"></img>
+    <p>{{ AIF_metric }}</p>
+    <img src="{{ AIF_graph }}" width="50%"></img>
+    <h2 id="T1w_dyn">Registrations to Dynamic Space</h2>
+    <img src="{{ t1w_bet_dyn }}" alt="{{ image_alt1 }}"></img>
+    <img src="{{ t1w_wm_dyn }}" alt="{{ image_alt1 }}"></img>
+    <img src="{{ t1w_gm_dyn }}" alt="{{ image_alt1 }}"></img>
+    <h2 id="DCE_Z-norm">DCE Z-Normalization</h2>
+    <img src="{{ Z_DCE }}" alt="{{ image_alt1 }}" width="1200"></img>
+    <h1 id="DCE">DCE Outputs</h1>
+    <h2 id="RUN_A">RUN A</h2>
+    <ul>
+        <li>{{ DCE_TR }}</li>
+        <li>{{ DCE_FA }}</li>
+        <li>{{ Hematocrit }}</li>
+        <li>{{ SNR_Threshold }}</li>
+        <li>{{ Relaxivity }}</li>
+        <li>{{ T1_blood }}</li>
+        <li>{{ A_last_line }}</li>
+    </ul>
+    <!-- % of voxels removed? -->
+    <h2 id="RUN_B">RUN B</h2>
+    <ul>
+        <li>{{ Time_Resolution }}</li>
+        <li>{{ R_squared_fit }}</li>
+        <li>{{ R_squared_raw }}</li>
+        <li>{{ B_last_line }}</li>
+    </ul>
+    <!-- <h3>AIF Fitting and Time Curves</h2> -->
+    <div class="dce-image-container">
+        <img src="{{ DCE_AIF_fit }}" alt="{{ image_alt1 }}" width="800"></img>
+        <img src="{{ DCE_AIF_timecurve }}" alt="{{ image_alt1 }}" width="800"></img>
+    </div>
+    <h2 id="RUN_D">RUN D</h2>
+    <ul>
+        <li>{{ DCE_model }}</li>
+        <li>{{ GPU_DCE }}</li>
+        <li>{{ DCE_elapsed_time }}</li>
+    </ul>
+    <h2 id="Ktrans">Ktrans maps (10^-3/min)</h2>
+    <ul>
+        <!-- <li>{{ ktrans_wm_mean }}</li> -->
+        <li>{{ ktrans_wm_median }}</li>
+        <li>{{ ktrans_wm_std }}</li>
+        <!-- <li>{{ ktrans_gm_mean }}</li> -->
+        <li> {{ ktrans_gm_median }}</li>
+        <li>{{ ktrans_gm_std }}</li>
+    </ul>
+    <img src="{{ ktrans }}" alt="{{ ELYOUEL }}" width="1600"></img>
+    <!-- <p>maybe Ktrans with segmentation overlay</p> -->
+    <h2>Ktrans stats</h2>
+    <div class="dce-image-container">
+        <img src="{{ ktrans_zeros }}" alt="{{ image_alt1 }}" width="800"></img>
+        <img src="{{ ktrans_analysis }}" alt="{{ image_alt1 }}" width="800"></img>
+    </div>
+    <h2>wmparc Registration</h2>
+    <div id="wmparc_hover" class="image-container">
+        <img id="dceref" src="{{ DCEref }}" alt="{{ image_alt1 }}">
+        <img id="wmparc_overlay" src="{{ wmparc_overlay }}" alt="{{ image_alt2 }}">
+    </div>
+    <!-- <h2 id="MNI">MNI Registration</h2>
+    <div id="image-container-3" class="image-container">
+        <img id="MNI_std" src="{{ MNI_img }}" alt="{{ image_alt1 }}" width="800"></img>
+        <img id="T1w_MNI" src="{{ MNI_T1w }}" alt="{{ image_alt1 }}" width="800"></img>
+    </div> -->
+    <!-- <div id="image-container-3" class="image-container">
+        <img id="MNI_std" src="{{ MNI_img }}" alt="{{ image_alt1 }}" width="800"></img>
+        <img id="Ktrans_MNI" src="{{ MNI_Ktrans }}" alt="{{ image_alt1 }}" width="800"></img>
+    </div> -->
+    <h2 id="Placement">Placement in Population</h2>
+    <img src="placeholder_wm" alt="to be inserted post-population report" width="800"></img>
+    <img src="placeholder_gm" alt="to be inserted post-population report" width="800"></img>
+    <script>
+        var containers = document.querySelectorAll('.image-container');
+        containers.forEach(function(container) {
+            container.addEventListener('mouseenter', function() {
+                var images = container.querySelectorAll('img');
+                images[0].style.opacity = 1;
+            });
+
+            container.addEventListener('mouseleave', function() {
+                var images = container.querySelectorAll('img');
+                images[0].style.opacity = 1;
+            });
+        });
+    </script>
+</body>
+</html>
diff --git a/tktregistration.sh b/tktregistration.sh
deleted file mode 100755
index 1158ad3..0000000
--- a/tktregistration.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash
-
-3dAFNItoNIFTI -prefix TempDataset1.nii.gz $1
-3dAFNItoNIFTI -prefix TempDataset2.nii.gz $2
-
-#if [[ $1 = "rat" ]]
-#then
-#FOV='64'
-#fi
-
-#if [[ $1 = "human" ]]
-#then
-FOV='256'
-#fi
-
-tkregister2 --targ TempDataset1.nii.gz --mov TempDataset2.nii.gz --reg Register.dat --regheader --noedit --fov $FOV
-
-mri_vol2vol --mov TempDataset2.nii.gz --targ TempDataset1.nii.gz --reg Register.dat --o OutputDataset.nii.gz
-
-3dcalc -a OutputDataset.nii.gz -expr 'a' -prefix $3 -overwrite
-
-rm TempDataset*
-rm OutputDataset*
-
-echo ' '
-echo '*** Registration Done ***'
-echo ' '
diff --git a/utils/constants.py b/utils/constants.py
new file mode 100644
index 0000000..9ba72f9
--- /dev/null
+++ b/utils/constants.py
@@ -0,0 +1,2 @@
+# values below this are physiologically nonsensical and is supported by historical analysis
+KTRANS_MIN_THRESHOLD = 1e-5
diff --git a/venv_requirements.txt b/venv_requirements.txt
new file mode 100644
index 0000000..e6247b9
--- /dev/null
+++ b/venv_requirements.txt
@@ -0,0 +1,110 @@
+absl-py==2.1.0
+asteval==1.0.5
+astunparse==1.6.3
+cachetools==5.5.0
+cairocffi==1.7.1
+CairoSVG==2.7.1
+certifi==2024.12.14
+cffi==1.17.1
+chardet==5.2.0
+charset-normalizer==3.4.0
+contourpy==1.3.1
+cssselect2==0.7.0
+cycler==0.12.1
+defusedxml==0.7.1
+dill==0.3.9
+flatbuffers==24.3.25
+fonttools==4.55.3
+gast==0.4.0
+google-auth==2.37.0
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
+grpcio==1.68.1
+h5py==3.12.1
+idna==3.10
+imageio==2.36.1
+importlib_resources==6.4.5
+jax==0.4.30
+jaxlib==0.4.30
+Jinja2==3.1.5
+joblib==1.4.2
+keras==3.7.0
+kiwisolver==1.4.7
+libclang==18.1.1
+llvmlite==0.43.0
+lmfit==1.3.2
+lxml==5.3.0
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.0
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+nibabel==5.3.2
+nilearn==0.11.0
+numba==0.60.0
+numpy==2.1.0
+nvidia-cublas-cu12==12.5.3.2
+nvidia-cuda-cupti-cu12==12.5.82
+nvidia-cuda-nvcc-cu12==12.5.82
+nvidia-cuda-nvrtc-cu12==12.5.82
+nvidia-cuda-runtime-cu12==12.5.82
+nvidia-cudnn-cu12==9.3.0.75
+nvidia-cufft-cu12==11.2.3.61
+nvidia-curand-cu12==10.3.6.82
+nvidia-cusolver-cu12==11.6.3.83
+nvidia-cusparse-cu12==12.5.1.3
+nvidia-nccl-cu12==2.21.5
+nvidia-nvjitlink-cu12==12.5.82
+oauthlib==3.2.2
+opt_einsum==3.4.0
+optree==0.13.1
+packaging==24.2
+pandas==2.2.3
+pandas-flavor==0.6.0
+patsy==1.0.1
+pillow==11.0.0
+pingouin==0.5.5
+plotly==5.24.1
+protobuf==4.25.5
+psutil==6.1.1
+pyasn1==0.6.1
+pyasn1_modules==0.4.1
+pycparser==2.22
+pydicom==3.0.1
+Pygments==2.18.0
+pyparsing==3.2.0
+PySimpleGUI==5.0.7
+python-dateutil==2.9.0.post0
+pytz==2024.2
+reportlab==4.2.5
+requests==2.32.3
+requests-oauthlib==2.0.0
+rich==13.9.4
+rsa==4.9
+scikit-learn==1.6.0
+scipy==1.14.1
+seaborn==0.13.2
+six==1.17.0
+statsmodels==0.14.4
+tabulate==0.9.0
+tenacity==9.0.0
+tensorboard==2.18.0
+tensorboard-data-server==0.7.2
+tensorflow==2.18.0
+tensorflow-addons==0.23.0
+tensorflow-estimator==2.12.0
+tensorflow-io-gcs-filesystem==0.37.1
+termcolor==2.5.0
+threadpoolctl==3.5.0
+tinycss2==1.4.0
+typeguard==2.13.3
+typing_extensions==4.12.2
+tzdata==2024.2
+uncertainties==3.2.2
+urllib3==2.2.3
+webencodings==0.5.1
+Werkzeug==3.1.3
+wrapt==1.14.1
+xarray==2024.11.0