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Beyond FA Baseline

Here is an example repository for the Beyond FA challenge. This model calculates fractional anisotropy (FA) from the diffusion MRI, then finds the average value of FA in regions defined by TractSeg.

Building the Docker

See Dockerfile for an example of setting up the Docker container. The Docker container is based the mrtrix Docker, which includes several common neuroimaging packages (e.g., MRtrix3, ANTs, FSL, Freesurfer). It also installs the following:

To build this Docker container, clone the repository and run the following command in the root directory:

DOCKER_BUILDKIT=1 sudo docker build -t beyondfa_baseline:v1.1.8 .

The Docker runs the code from scripts/entrypoint.sh.

Running the Docker

Your Docker container should be able to read input data from /input and write output data to /output. Intermediate data should be written to /tmp. The input data will be a .mha file containing the diffusion MRI data with gradient table information contained in a .json file. The input file will be in /input/images/dwi-4d-brain-mri/, with gradient table information at /input/dwi-4d-acquisition-metadata.json. Your Docker should write a JSON list to the output directory with the name /output/features-128.json. Your JSON list must contain 128 values. You may zero-pad the list if you wish to provide fewer than 128 values.

See scripts/convert_mha_to_nifti.py and scripts/convert_json_to_bvalbvec.py for scripts to convert the .mha to .nii.gz and the .json to .bval and .bvec files.

To run this Docker:

input_dir=".../input_data"
output_dir=".../output_data"

mkdir -p $output_dir

DOCKER_NOOP_VOLUME="beyondfa_baseline-volume"
sudo docker volume create "$DOCKER_NOOP_VOLUME" > /dev/null
sudo docker run \
    -it \
    --platform linux/amd64 \
    --network none \
    --gpus all \
    --rm \
    --volume $input_dir:/input:ro \
    --volume $output_dir:/output \
    --volume "$DOCKER_NOOP_VOLUME":/tmp \
    beyondfa_baseline:v1.1.8
sudo docker volume rm "$DOCKER_NOOP_VOLUME" > /dev/null