Volumetric RT MRI

.gitignore

@@ -28,6 +28,9 @@ test/integration/*.xprot
 test/integration/*.xml
 .atom-build.json
 #.vscode/
+test/nhlbi_integration_tests/test
+test/nhlbi_integration_tests/baselines
+*.seq
 dep-build/
 stats.csv
 junit.xml

Dockerfile

@@ -3,7 +3,7 @@ ARG USERNAME="vscode"
 ARG USER_UID=1000
 ARG USER_GID=1000
 
-FROM ubuntu:22.04 AS gadgetron_baseimage
+FROM ubuntu:24.04 AS gadgetron_baseimage
 LABEL org.opencontainers.image.source=https://github.com/gadgetron/gadgetron
 
 ARG USERNAME
@@ -12,9 +12,13 @@ ARG USER_GID
 ARG HOME=/home/$USERNAME
 
 RUN apt-get update \
-    && DEBIAN_FRONTEND=noninteractive apt-get install -y sudo wget git-core git-lfs rsync curl net-tools libxml2 \
+    && DEBIAN_FRONTEND=noninteractive apt-get install -y sudo wget git-core git-lfs rsync curl net-tools libxml2 emacs-nox \
     && apt-get clean
 
+# Remove default ubuntu user (Ubuntu 24.04 ships with UID/GID 1000 taken)
+RUN userdel -r ubuntu 2>/dev/null || true \
+    && groupdel ubuntu 2>/dev/null || true
+
 # Create the user
 RUN groupadd --gid $USER_GID $USERNAME \
     && useradd --uid $USER_UID --gid $USER_GID -m $USERNAME -s /bin/bash \
@@ -41,20 +45,7 @@ RUN wget --no-hsts --quiet https://github.com/conda-forge/miniforge/releases/dow
     && chmod -R g+w /opt/conda \
     && find /opt -type d | xargs -n 1 chmod g+s
 
-#RUN wget --no-hsts --quiet https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh -O /tmp/miniforge.sh \
-#    && /bin/bash /tmp/miniforge.sh -b -p /opt/conda \
-#    && rm /tmp/miniforge.sh \
-#    && /opt/conda/bin/mamba clean --tarballs --index-cache --packages --yes \
-#    && find /opt/conda -follow -type f -name '*.a' -delete \
-#    && find /opt/conda -follow -type f -name '*.pyc' -delete \
-#    && /opt/conda/bin/mamba clean --force-pkgs-dirs --all --yes  \
-#    && groupadd -r conda --gid ${CONDA_GID} \
-#    && usermod -aG conda ${USERNAME} \
-#    && chown -R :conda /opt/conda \
-#    && chmod -R g+w /opt/conda \
-#    && find /opt -type d | xargs -n 1 chmod g+s
-
-# Copy environment, which will be filtered for later staged
+# Copy environment
 COPY --chown=$USER_UID:conda environment.yml /tmp/build/
 
 # Create mount points for tests
@@ -76,17 +67,21 @@ ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /tini
 RUN chmod +x /tini
 
 USER root
-RUN mkdir -p /opt/GIRF/
 COPY --chown=$USER_UID:conda toolboxes/nhlbi_gt_toolbox/GIRF/ /opt/GIRF/
+#COPY --chown=$USER_UID:conda toolboxes/nhlbi_gt_toolbox/models_dl/ /opt/models/
 
 FROM gadgetron_baseimage AS gadgetron_dev_cuda
 ARG USER_UID
 ARG HOME
 USER ${USER_UID}
 RUN mkdir -p ${HOME}/.cache/conda/notices && sudo chown -R ${USER_UID}:conda ${HOME}/.cache/conda/notices
 
-RUN grep -v "#.*\<NOFILTER\>" /tmp/build/environment.yml > /tmp/build/filtered_environment.yml
-RUN umask 0002 && /opt/conda/bin/mamba env remove -n gadgetron || true && /opt/conda/bin/mamba env create -f /tmp/build/filtered_environment.yml && /opt/conda/bin/mamba clean -afy && sudo chown -R :conda /opt/conda
+RUN umask 0002 && /opt/conda/bin/mamba env remove -n gadgetron || true \
+    && /opt/conda/bin/mamba env create -f /tmp/build/environment.yml \
+    && /opt/conda/bin/mamba clean -afy \
+    && sudo chown -R :conda /opt/conda
+
+
 RUN echo 'export PATH="/opt/conda/envs/gadgetron/nvvm/bin/:$PATH"' >> ${HOME}/.bashrc
 
 
@@ -98,8 +93,7 @@ ARG USER_UID
 ARG HOME
 USER ${USER_UID}
 RUN mkdir -p ${HOME}/.cache/conda/notices && sudo chown -R ${USER_UID}:conda ${HOME}/.cache/conda/notices
-RUN grep -v "#.*\<cuda\>" /tmp/build/environment.yml > /tmp/build/filtered_environment.yml
-RUN umask 0002 && /opt/conda/bin/mamba env create -f /tmp/build/filtered_environment.yml && /opt/conda/bin/mamba clean -afy && sudo chown -R :conda /opt/conda
+RUN umask 0002 && /opt/conda/bin/mamba env create -f /tmp/build/environment.yml && /opt/conda/bin/mamba clean -afy && sudo chown -R :conda /opt/conda
 USER root
 
 FROM gadgetron_dev_cuda AS gadgetron_cudabuild
@@ -108,28 +102,16 @@ USER ${USER_UID}
 WORKDIR /opt
 ARG USER_GID=1000
 
-RUN echo "UID=$USER_UID GID=$USER_GID"
-
-RUN sudo chown $USER_UID:$USER_GID /opt && mkdir -p /opt/code/gadgetron && mkdir -p /opt/package 
+RUN sudo chown $USER_UID:$USER_GID /opt && mkdir -p /opt/code/gadgetron && mkdir -p /opt/package
 COPY --chown=$USER_UID:conda . /opt/code/gadgetron/
 
-
-# Ensure NHLBI-GT-Non-Cartesian is inside the build context before building
-#COPY --chown=$USER_UID:conda NHLBI-GT-Non-Cartesian/ /opt/code/NHLBI-GT-Non-Cartesian/
-
-
 ENV CONDA_ENV_PATH=/opt/conda/envs/gadgetron
 RUN echo "export NUMBA_CUDA_ENABLE_PYNVJITLINK=1" >> $CONDA_ENV_PATH/etc/conda/activate.d/env_vars.sh
 
-#COPY --chown=$USER_UID:conda OpticalFlow3d/ /opt/code/OpticalFlow3d/
 RUN . /opt/conda/etc/profile.d/conda.sh && umask 0002 && conda activate gadgetron && \
     sh -x && \
     pip install git+https://github.com/ahsanjav/OpticalFlow3d.git
-    # cd /opt/code/OpticalFlow3d/ && \
-    # pip install -e . 
-    # # && \
-    # pip install numpy==1.23
-
+
 SHELL ["/bin/bash", "-c"]
 RUN . /opt/conda/etc/profile.d/conda.sh && umask 0002 && conda activate gadgetron && \
     export PATH="/opt/conda/envs/gadgetron/nvvm/bin/:$PATH" && \
@@ -165,28 +147,25 @@ USER ${USER_UID}
 ARG USER_GID=1000
 
 RUN mkdir -p ${HOME}/.cache/conda/notices && sudo chown -R ${USER_UID}:conda ${HOME}/.cache/conda/notices
-RUN grep -v "#.*\<dev\>" /tmp/build/environment.yml > /tmp/build/filtered_environment.yml
-RUN umask 0002 && /opt/conda/bin/mamba env create -f /tmp/build/filtered_environment.yml && /opt/conda/bin/mamba clean -afy && sudo chown -R :conda /opt/conda
 
+# Copy the already-solved conda env from the build stage (eliminates redundant ~20 min conda solve)
+COPY --from=gadgetron_cudabuild --chown=$USER_UID:conda /opt/conda/envs/gadgetron /opt/conda/envs/gadgetron
+
+# Copy build artifacts and entrypoint
 COPY --from=gadgetron_cudabuild --chown=$USER_UID:conda /opt/package /opt/conda/envs/gadgetron/
 COPY --from=gadgetron_cudabuild --chown=$USER_UID:conda /opt/code/gadgetron/docker/entrypoint.sh /opt/
-#RUN mkdir -p /opt/models/
 #COPY --from=gadgetron_cudabuild --chown=$USER_UID:conda /opt/models/ /opt/models/
 
 ENV CONDA_ENV_PATH=/opt/conda/envs/gadgetron
 RUN echo "export NUMBA_CUDA_ENABLE_PYNVJITLINK=1" >> $CONDA_ENV_PATH/etc/conda/activate.d/env_vars.sh
 
-#COPY --chown=$USER_UID:conda OpticalFlow3d/ /opt/code/OpticalFlow3d/
-RUN . /opt/conda/etc/profile.d/conda.sh && umask 0002 && conda activate gadgetron && \
-    sh -x && \
-    pip install git+https://github.com/ahsanjav/OpticalFlow3d.git
-    # cd /opt/code/OpticalFlow3d/ && \
-    # pip install . 
 
-RUN sudo rm -rf /opt/code/
-RUN chmod +x /opt/entrypoint.sh
-RUN sudo mkdir -p /opt/integration-test && sudo chown ${USER_GID}:${USER_UID} /opt/integration-test
+RUN sudo rm -rf /opt/code/ \
+    && chmod +x /opt/entrypoint.sh \
+    && sudo mkdir -p /opt/integration-test \
+    && sudo chown ${USER_GID}:${USER_UID} /opt/integration-test
 COPY --from=gadgetron_cudabuild --chown=$USER_UID:conda /opt/code/gadgetron/test/integration /opt/integration-test/
+COPY --from=gadgetron_cudabuild --chown=$USER_UID:conda /opt/code/gadgetron/test/nhlbi_integration_tests /opt/nhlbi-integration-test/
 ENTRYPOINT [ "/tini", "--", "/opt/entrypoint.sh" ]
 
 # FROM gadgetron_baseimage AS gadgetron_rt_nocuda

README.md

@@ -1,15 +1,12 @@
 
-# “Two-for-one”: 3D cardiac and pulmonary MR imaging from a single acquisition using bSTAR
-
-This project is a Gadgetron extension that provides a dual inline image reconstruction pipeline for simultaneous cardiac-resolved whole heart and respiratory-resolved lung imaging.
+# Volumetric real-time MRI for the guidance of cardiac catheterization at 0.55 T
 
+This project is a Gadgetron extension that provides a inline image reconstruction pipeline for volumetric, stack-of-spirals real-time acquisitions to guide invasive procedures such as right-heart catheterization.
 ## Publication 
 
-This open-source is described in :
-
-- Daudé P.,Taylor J., Plummer J., Ramasawmy R., Tsakirellis A., Moss J., Bandettini W.P., Chen M., Campbell-Washburn A.E.,Javed A. “Two-for-one”: 3D cardiac and pulmonary MR imaging from a single acquisition using bSTAR
-. In preparation
+This project is described in :
 
+- Kumar P., Ramasawmy R., Javed A., Le D., Daudé P., O'Brien K., Jaimes A.E., Franson D., Chow K., Dong F., Maier F., Lederman R.J., Nayak K.S., Campbell-Washburn A.E., “Volumetric real-time MRI for the guidance of cardiac catheterization at 0.55 T". In preparation
 
 ## Documentation
 

apps/gadgetron/connection/HeaderConnection.cpp

@@ -48,11 +48,15 @@ namespace {
             /* tSequenceVariant in the header give information about the hash of the pulseq seq file
             Loop through all seq files in a directory and find the correct seq file (hash is in the last line )
             */
+
+            // PK commenting this out ecause the presence of tSequenceVariant is not guaranteed for only bSTAR...
+            /*
             if (header.userParameters) {
                 ISMRMRD::UserParameters user_params = header.userParameters.get();
                 std::vector<ISMRMRD::UserParameterString> strings = user_params.userParameterString;
                 std::vector<ISMRMRD::UserParameterString>::iterator it;
 
+
                 for (it = strings.begin(); it != strings.end(); ++it) {
                     if (it->name == "tSequenceVariant") {
                         GDEBUG("Hash bstar found %s found\n", it->value.c_str());
@@ -159,9 +163,11 @@ namespace {
                     }
                 }
             }
+            */
             //PD
             header_callback(header);
         }
+
 
     private:
         std::function<void(Header)> header_callback;

environment.yml

@@ -15,7 +15,8 @@ dependencies:
   - numpy=1.24.4
   - scipy=1.10.1
   - pyfftw=0.15.0
-  - h5py>=3.6,<3.8  # Must match conda HDF5 1.10.x
+  - hdf5>=1.14.3
+  - h5py=3.12.1  # Requires hdf5 >=1.14.3 (provided by ismrmrd=1.15.0)
   - pyyaml=6.0
   - jq=1.6
   - yq=2.13
@@ -52,27 +53,25 @@ dependencies:
 
   # Math and Signal Processing
   - fftw=3.3.9
-  - armadillo=9.900.5
+  - armadillo>=9.900
   - mkl>=2024.2.2,<2025.0a0
   - mkl-include>=2024.2.2,<2025.0a0
-  - libxml2=2.9
-  - libcurl=7.79.1
+  - libxml2>=2.9.14,<2.14
+  - libcurl>=7.79
   - plplot=5.15.0
-  - pugixml=1.12.1
-  - onnxruntime=1.10.0
+  - pugixml=1.15
+  - onnxruntime>=1.10
   - xsdata>=22.2
 
-  # Gadgetron + ISMRMRD
-  - ismrmrd::ismrmrd=1.13.7
-  - ismrmrd::ismrmrd-python>=1.9.8
-  - ismrmrd::siemens_to_ismrmrd=1.2.11
+  # Gadgetron + ISMRMRD (C libraries via conda; Python bindings via pip below)
+  - ismrmrd::ismrmrd=1.15.0
+  - ismrmrd::siemens_to_ismrmrd=1.2.13
   - ismrmrd::mrd-storage-server=0.0.12
-  - gadgetron::gadgetron-python=1.4.1
   - gadgetron::range-v3=0.11.0
 
   # Boost
   - boost=1.80.0
-  - icu=70.1
+  - icu>=70.1
 
   # Docs
   - sphinx=4.4.0
@@ -94,6 +93,10 @@ dependencies:
   - pynvjitlink
 
   - pip:
+      # ismrmrd and gadgetron Python bindings installed via pip
+      # (conda ismrmrd-python channel only has up to 1.14.2)
+      - ismrmrd==1.14.2
+      - gadgetron==1.4.1
       - nvidia-cuda-runtime-cu12==12.8.57  # Required for CuPy JIT compilation (must match PyTorch cu128 requirement)
       - sigpy
       - scikit-image
@@ -108,3 +111,8 @@ dependencies:
       - torchvision==0.22.0
       - torchaudio==2.7.0
       - onnxruntime-gpu
+      # NHLBI integration tests (Azure Blob Storage for private test data)
+      - azure-storage-blob
+      - azure-identity
+      - matplotlib
+      - pyigtl

gadgets/python/legacy/gadgets/accumulate_and_recon.py

@@ -38,7 +38,7 @@ def process(self, acq, data, *args):
 
         if (acq.flags & (1<<7)): #Is this the last scan in slice
             image = cifftn(self.myBuffer, axes=(0, 1, 2))
-            image = image * np.product(image.shape)*100 #Scaling for the scanner
+            image = image * np.prod(image.shape)*100 #Scaling for the scanner
             #Create a new image header and transfer value
             img_head = ismrmrd.ImageHeader()
             img_head.version = 1

test/nhlbi_integration_tests/cases/3drt.cfg

@@ -0,0 +1,52 @@
+[dependency.siemens]
+data_file = 3drt/noise_data.h5
+measurement = 0
+additional_arguments = skip_converstion
+
+[dependency.client]
+configuration = default_measurement_dependencies.xml
+
+[dependency.siemens.1]
+data_file = 3drt/prescan-reference_noise.h5
+measurement = 0
+additional_arguments = skip_converstion
+
+[dependency.client.1]
+configuration = default_measurement_dependencies.xml
+
+[dependency.siemens.2]
+data_file = 3drt/prescan-reference.h5
+measurement = 0
+additional_arguments = skip_converstion
+
+[dependency.client.2]
+configuration = spiral_3DRT_CSM_test.xml
+
+[reconstruction.siemens]
+data_file = 3drt/recon_data.h5
+measurement = 0
+additional_arguments = skip_converstion
+
+[reconstruction.client]
+configuration = spiral_3DRT_test.xml
+
+[reconstruction.test]
+reference_file = 3drt/baseline_output.h5
+reference_images = spiral_3DRT_test.xml/image_0
+output_images = spiral_3DRT_test.xml/image_0
+value_comparison_threshold = 0.1
+scale_comparison_threshold = 0.01
+
+[requirements]
+system_memory = 8192
+gpu_support = 1
+gpu_memory = 8192
+
+[tags]
+tags = nhlbi,3drt
+
+[nhlbi]
+description = spiral 3drt
+noise_file = 3drt/noise_data.h5
+baseline_recon_time = 42.3
+

test/nhlbi_integration_tests/cases/csm_prescan.cfg

@@ -0,0 +1,36 @@
+[dependency.siemens]
+data_file = csm_prescan/noise_data.h5
+measurement = 0
+additional_arguments = skip_converstion
+
+[dependency.client]
+configuration = default_measurement_dependencies.xml
+
+[reconstruction.siemens]
+data_file = csm_prescan/recon_data.h5
+measurement = 0
+additional_arguments = skip_converstion
+
+[reconstruction.client]
+configuration = spiral_3DRT_CSM_test.xml
+
+[reconstruction.test]
+reference_file = csm_prescan/baseline_output.h5
+reference_images = spiral_3DRT_CSM_test.xml/image_0
+output_images = spiral_3DRT_CSM_test.xml/image_0
+value_comparison_threshold = 0.01
+scale_comparison_threshold = 0.01
+
+[requirements]
+system_memory = 8192
+gpu_support = 1
+gpu_memory = 8192
+
+[tags]
+tags = nhlbi,3drt
+
+[nhlbi]
+description = Prescan CSM generation
+noise_file = csm_prescan/noise_data.h5
+baseline_recon_time = 56.2
+