Open-source tools for Dynamic Contrast-Enhanced MRI processing & analysis
+
+
+
+
+
+
+
+ DCEASY is a collection of open-source software maintained by the
+ PET/MRI Lab
+ for end-to-end Dynamic Contrast-Enhanced (DCE) MRI workflows —
+ from parametric mapping and pharmacokinetic modelling to arterial input function (AIF) curation.
+ Choose the tool that fits your step in the pipeline below.
+
+ A napari desktop application for manual arterial input function (AIF)
+ annotation on 4D MRI NIfTI data. Designed for high-volume multi-rater
+ review sessions: draw a 3D ROI, inspect the mean signal-intensity
+ curve over time, save a BIDS-style derivative, and jump straight to
+ the next case.
+
+
+
Loads BIDS-compliant 4D desc-hmc_DCE.nii[.gz] files, directories, or manifest lists
+
Live ROI curve preview with per-label and normalised views
+
BIDS-style derivative outputs with rater ID embedded in filenames
+
Auto-resumes at first unreviewed case; prefetches next image
+ The ROCKETSHIP parametric mapping module, also usable as a
+ stand-alone tool. Generates T1, T2, T2*, and ADC maps from
+ multi-echo or inversion-recovery NIFTI series — a required
+ pre-processing step for accurate DCE pharmacokinetic modelling.
+
+
+
T1 (inversion recovery), T2, T2*, and ADC map generation
# Install dependencies
+pip install -r requirements.txt
+
+# Launch the annotation app
+python aif_artist.py /path/to/bids_dataset \
+ --rater AB
+
+
+
+
+
+
Contact & contributing
+
+ Questions? Open an issue on the relevant repository or contact the lab
+ maintainer at sabarnes@llu.edu.
+ Contributions via pull request are welcome on all repositories.
+
Open-source tools for Dynamic Contrast-Enhanced MRI processing & analysis
+
Dynamic Contrast-Enhanced Analysis SYstem — open-source tools for DCE-MRI processing & analysis
@@ -19,10 +19,10 @@
DCEASY
- DCEASY is a collection of open-source software maintained by the
+ DCEASY (Dynamic Contrast-Enhanced Analysis SYstem) is a collection of open-source software maintained by the
PET/MRI Lab
for end-to-end Dynamic Contrast-Enhanced (DCE) MRI workflows —
- from parametric mapping and pharmacokinetic modelling to arterial input function (AIF) curation.
+ from parametric mapping and pharmacokinetic modelling to arterial input function (AIF) detection.
Choose the tool that fits your step in the pipeline below.
3 Annotate or fit the arterial input function → AIFArtist / ROCKETSHIP
-
4 Fit pharmacokinetic models (Ktrans, ve, vp …) → ROCKETSHIP
+
3 Automatically detect or manually annotate the AIF → AutoAIF (preferred) / AIFArtist / ROCKETSHIP
+
4 Fit pharmacokinetic models (Ktrans, ve, vp …) → ROCKETSHIP + gpufit
5 Analyse & compare parametric maps → ROCKETSHIP
@@ -70,6 +70,32 @@
ROCKETSHIP
+
+
+
+
AutoAIF
+ Python
+
+
+ Preferred for AIF detection. A 3D U-Net deep-learning model (Keras/TensorFlow)
+ that automatically detects the arterial input function in brain DCE-MRI.
+ Pretrained weights are provided; the model handles multi-site data by
+ resampling inputs to a canonical resolution and outputs a vascular
+ function curve together with a 3D vascular mask.
+
+
+
Fully automatic — no manual ROI drawing required
+
Pretrained on multi-site brain DCE-MRI cohorts
+
Outputs vascular function curve + 3D mask (NIfTI)
+
Supports inference and fine-tuning on new datasets
+
Published in Magnetic Resonance in Medicine (2025)
+ A GPU-accelerated Levenberg–Marquardt curve-fitting library with
+ Python and MATLAB wrappers. This PET/MRI Lab fork adds MRI-specific
+ pharmacokinetic models and updated compiler/CUDA support. Used by
+ ROCKETSHIP for fast voxel-wise model fitting.
+
+
+
Patlak, Tofts, Extended Tofts, Tissue Uptake, 2CXM, T1 FA Exponential
+
Full GPU (Gpufit) and CPU (Cpufit) parity for all MRI models
+
Pre-built binaries for Windows, Linux (CUDA 11.8–13.0), and macOS (CPU)
+
Python and MATLAB wrappers included
+
Based on Przybylski et al., Scientific Reports (2017)
3 Automatically detect or manually annotate the AIF → AutoAIF (preferred) / AIFArtist / ROCKETSHIP
4 Fit pharmacokinetic models (Ktrans, ve, vp …) → ROCKETSHIP + gpufit
5 Analyse & compare parametric maps → ROCKETSHIP
@@ -169,6 +169,33 @@
gpufit
+
+
+
+
DCEPrep
+ Shell / Python
+
+
+ An end-to-end preprocessing and analysis pipeline for brain DCE-MRI.
+ Wraps FSL, ANTs, FreeSurfer, ROCKETSHIP, and AutoAIF into a single
+ configurable shell script. Handles VFA-based T1 mapping, bias field
+ correction, z-axis normalisation, head motion correction, AIF
+ selection, Ktrans fitting, and automated QC reporting. A Docker
+ image is provided for a consistent, reproducible environment.
+
+
+
BIDS-compliant input/output
+
VFA T1 mapping, bias field correction, z-axis normalisation
+
Head motion correction via FSL mcflirt
+
AutoAIF integration for automatic AIF detection
+
Ktrans / vp mapping + per-case & population HTML QC reports
+
Docker image available for easy, reproducible deployment
# Clone and set up environment
-git clone https://github.com/petmri/vascular_function.git
-cd vascular_function
+git clone https://github.com/petmri/AutoAIF.git
+cd AutoAIF
python3 -m venv tf && source tf/bin/activate
pip install -r requirements.txt
From 525f05785430b61002af331c69750c5333de1025 Mon Sep 17 00:00:00 2001
From: Sam Barnes
Date: Fri, 26 Jun 2026 12:58:03 -0700
Subject: [PATCH 8/8] Polish site content and wording
- US spelling throughout (modeling, normalization, analyze, visualize)
- Add meta description for SEO/social previews
- Trim repeated DCEASY acronym in intro
- Drop stale GitHub star count from ROCKETSHIP card
- Unify Gpufit casing across page
- Add git clone step to AIFArtist quick start
Co-Authored-By: Claude Opus 4.8 (1M context)
---
index.html | 65 +++++++++++++++++++++++++++++++++---------------------
styles.css | 41 ++++++++++++++++++++++++++++++++++
2 files changed, 81 insertions(+), 25 deletions(-)
diff --git a/index.html b/index.html
index 7fd5703..1f0a180 100644
--- a/index.html
+++ b/index.html
@@ -3,6 +3,7 @@
+
DCEASY — Dynamic Contrast-Enhanced Analysis SYstem
@@ -10,8 +11,10 @@
-
DCEASY
-
Dynamic Contrast-Enhanced Analysis SYstem — open-source tools for DCE-MRI processing & analysis
+
DCE Processing Tools
+
DCEASY
+
(Dynamic Contrast-Enhanced
+ Analysis SYstem)
@@ -19,22 +22,32 @@
DCEASY
- DCEASY (Dynamic Contrast-Enhanced Analysis SYstem) is a collection of open-source software maintained by the
+ DCEASY is a collection of open-source software maintained by the
PET/MRI Lab
for end-to-end Dynamic Contrast-Enhanced (DCE) MRI workflows —
- from parametric mapping and pharmacokinetic modelling to arterial input function (AIF) detection.
+ from parametric mapping and pharmacokinetic modeling to arterial input function (AIF) detection.
Choose the tool that fits your step in the pipeline below.
-
DCE Processing pipeline overview
+
DCE Processing Overview
+
+
Run the whole pipeline at once
+
+
+ DCEPrep
+ Automated & Dockerized — preprocessing through quality control and analysis (steps 1–5)
+
3 Extract an AIF → AutoAIF (automatic) / AIFArtist (manual)
-
4 Fit pharmacokinetic models (Ktrans, ve, vp …) → DCEPrep (end-to-end) / ROCKETSHIP + gpufit (standalone)
-
5 Analyse & compare parametric maps → ROCKETSHIP
+
1 Preprocessing → standard MRI tools (FSL, ANTs, etc.)
+
2 Generate T1 maps → parametric_scripts
+
3 Extract an AIF → AutoAIF (automatic) / AIFArtist (manual)
+
4 Signal intensity to concentration, fit pharmacokinetic models (Ktrans, ve, vp …) → ROCKETSHIP + Gpufit
+
5 Analyze & compare parametric maps → ROCKETSHIP
@@ -53,7 +66,7 @@
ROCKETSHIP
A flexible, GUI-driven suite for full DCE-MRI analysis. Covers pre-contrast
T1 mapping, AIF selection and fitting, multi-model pharmacokinetic curve
fitting (Tofts, Extended Tofts, Patlak, Two-Compartment Exchange, FXR, …),
- and results visualisation. Supports NIFTI and DICOM inputs and optional
+ and results visualization. Supports NIFTI and DICOM inputs and optional
GPU acceleration.
Loads BIDS-compliant 4D desc-hmc_DCE.nii[.gz] files, directories, or manifest lists
-
Live ROI curve preview with per-label and normalised views
+
Live ROI curve preview with per-label and normalized views
BIDS-style derivative outputs with rater ID embedded in filenames
Auto-resumes at first unreviewed case; prefetches next image
Flag-and-skip for poor AIFs or missing baselines
@@ -131,7 +144,7 @@
parametric_scripts
The ROCKETSHIP parametric mapping module, also usable as a
stand-alone tool. Generates T1, T2, T2*, and ADC maps from
multi-echo or inversion-recovery NIFTI series — a required
- pre-processing step for accurate DCE pharmacokinetic modelling.
+ pre-processing step for accurate DCE pharmacokinetic modeling.
T1 (inversion recovery), T2, T2*, and ADC map generation
@@ -147,7 +160,7 @@
parametric_scripts
-
gpufit
+
Gpufit
CUDA / C++
@@ -179,13 +192,13 @@
DCEPrep
An end-to-end preprocessing and analysis pipeline for brain DCE-MRI.
Wraps FSL, ANTs, FreeSurfer, ROCKETSHIP, and AutoAIF into a single
configurable shell script. Handles VFA-based T1 mapping, bias field
- correction, z-axis normalisation, head motion correction, AIF
+ correction, z-axis normalization, head motion correction, AIF
selection, Ktrans fitting, and automated QC reporting. A Docker
image is provided for a consistent, reproducible environment.
BIDS-compliant input/output
-
VFA T1 mapping, bias field correction, z-axis normalisation
+
VFA T1 mapping, bias field correction, z-axis normalization
Head motion correction via FSL mcflirt
AutoAIF integration for automatic AIF detection
Ktrans / vp mapping + per-case & population HTML QC reports
@@ -211,7 +224,11 @@
Which tool do I need?
-
Full automated processing pipeline of BIDS DCE data (motion correction, alignment, artifact correction, create T1 maps, AIF detection, Ktrans generation)