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Merge branch 'main' into Unit_Testing_Tolerances
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‎.github/workflows/docker-build-and-run.yml

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name: Build and Run Docker
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on:
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push:
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branches:
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- main
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pull_request:
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types: [opened, synchronize, edited]
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branches:
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- main
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- "docker/**"
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jobs:
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build-and-run-docker:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.x'
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- name: Install Python dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Generate input files
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run: |
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python -m Docker.generate_signal_docker_test
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- name: Verify input files
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run: |
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for file in ivim_simulation.nii.gz ivim_simulation.bval ivim_simulation.bvec; do
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if [ ! -f "$file" ]; then
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echo "Error: $file not found"
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exit 1
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fi
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done
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echo "All input files generated successfully"
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Build Docker image
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run: |
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docker build -t tf2.4_ivim-mri_codecollection -f Docker/Dockerfile .
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- name: Run Docker container
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run: |
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docker run --rm --name TF2.4_IVIM-MRI_CodeCollection \
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-v ${{ github.workspace }}:/usr/src/app \
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-v ${{ github.workspace }}:/usr/app/output \
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tf2.4_ivim-mri_codecollection ivim_simulation.nii.gz ivim_simulation.bvec ivim_simulation.bval
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- name: Verify output files
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run: |
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for file in f.nii.gz dp.nii.gz d.nii.gz; do
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if [ ! -f "$file" ]; then
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echo "Error: $file not found"
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exit 1
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fi
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done
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echo "All output files generated successfully"
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- name: Clean up artifacts and Docker image
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run: |
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docker rmi tf2.4_ivim-mri_codecollection || true
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rm -f tf2.4_ivim-mri_codecollection.tar.gz
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rm -f ${{ github.workspace }}/f.nii.gz
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rm -f ${{ github.workspace }}/dp.nii.gz
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rm -f ${{ github.workspace }}/d.nii.gz
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rm -f ${{ github.workspace }}/ivim_simulation.nii.gz
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rm -f ${{ github.workspace }}/ivim_simulation.bval
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rm -f ${{ github.workspace }}/ivim_simulation.bvec
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- name: Cleanup Docker
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run: |
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docker system prune -a --force

‎.github/workflows/documentation.yml renamed to ‎.github/workflows/website.yml

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name: Build & Deploy Documentation
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name: Build & Deploy Website
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on:
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workflow_run:
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run: |
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pip install -r requirements.txt
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# Action to download artifacts from a different workflow (analysis.yml)
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# Action Figures artifact
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- name: 'Download artifact'
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if: ${{ github.event.workflow_run.conclusion == 'success' }}
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uses: ./.github/actions/download-artifact
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with:
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name: 'Figures'
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# Action analysis data artifact
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- name: 'Download analysis data'
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if: ${{ github.event.workflow_run.conclusion == 'success' }}
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uses: ./.github/actions/download-artifact
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with:
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name: 'Data'
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- name: Run the test that generates the plots report.
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run: |
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pytest tests/IVIMmodels/unit_tests/test_ivim_fit.py --json-report
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mv .report.json utilities/
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python utilities/report-summary.py .report.json report-summary.json
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- name: 'Filter and compress results file.'
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run: python utilities/reduce_output_size.py test_output.csv test_output.csv.gz
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- name: Build html
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- name: move data to the dashboard folder
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run: |
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mv test_output.csv.gz website/dashboard
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mv report-summary.json website/dashboard
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- name: Build documentation
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run: |
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mkdir docs/_static
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mv *.pdf docs/_static/
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sphinx-apidoc -o docs src
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cd docs/
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make html
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- name: move data to the website folder
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run: |
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mv "docs/_build/html" "website/documentation"
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- name: Upload docs artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: 'docs/_build/html'
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path: 'website'
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deploy:
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needs: build

‎.gitignore

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*.raw
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bvals.txt
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download
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.Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb
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.ipynb_checkpoints
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md5sums.txt
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*.gz
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*.zip

‎Docker/generate_signal_docker_test.py

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import numpy as np
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import nibabel as nib
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from utilities.data_simulation.GenerateData import GenerateData
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from WrapImage.nifti_wrapper import save_nifti_file
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def save_bval_bvec(filename, values):
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if filename.endswith('.bval'):
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# Convert list to a space-separated string for bval
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values_string = ' '.join(map(str, values))
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elif filename.endswith('.bvec'):
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# Convert 2D list to a line-separated, space-separated string for bvec
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values_string = '\n'.join(' '.join(map(str, row)) for row in values)
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else:
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raise ValueError("Unsupported file extension. Use '.bval' or '.bvec'.")
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with open(filename, 'w') as file:
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file.write(values_string)
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# Set random seed for reproducibility
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np.random.seed(42)
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# Create GenerateData instance
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gd = GenerateData()
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# Generate random input data
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shape = (10, 10, 5)
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f_in = np.random.uniform(low=0, high=1, size=shape)
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D_in = np.random.uniform(low=0, high=1e-3, size=shape)
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Dp_in = np.random.uniform(low=0, high=1e-1, size=shape)
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S0 = 1000 # Setting a constant S0 for simplicity
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bvals = np.array([0, 50, 100, 500, 1000])
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bvals_reshaped = np.broadcast_to(bvals, shape)
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# Generate IVIM signal
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signals = gd.ivim_signal(D_in, Dp_in, f_in, S0, bvals_reshaped)
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# Save the generated image as a NIfTI file
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save_nifti_file(signals, "ivim_simulation.nii.gz")
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# Save the bval in a file
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save_bval_bvec("ivim_simulation.bval", [0, 50, 100, 500, 1000])
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# Save the bvec value
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save_bval_bvec("ivim_simulation.bvec", [[1, 0, 0], [0, 1, 0], [0, 0, 1]])

‎README.md

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This **IVIM code collection** code library is maintained by OSIPI [Taskforce 2.4](https://www.osipi.org/task-force-2-4/) (*currently not available*) and aims to collect, test and share open-source code related to intravoxel incoherent motion (IVIM) analysis of diffusion encoded MRI data to be used in research and software development. Code contributions can include any code related IVIM analysis (denoising, motion correction, model fitting, etc.), but at an initial phase, development of tests and other features of the repository will predominantly focus on fitting algorithms. A goal of the IVIM OSIPI task force is to develop a fully tested and harmonized code library, building upon the contributions obtained through this initiative. Documentation and analysis are available on the [OSIPI TF2.4](https://osipi.github.io/TF2.4_IVIM-MRI_CodeCollection/).
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We have some useful tools and further documentation on https://osipi.github.io/TF2.4_IVIM-MRI_CodeCollection/.
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## How to contribute
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If you would like to get involve in OSIPI and work within the task force, please email the contacts listed on our website.
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* [Guidelines for IVIM code contribution](doc/guidelines_for_contributions.md)
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* [Guidelines to creating a test file](doc/creating_test.md)
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If you would like to use code from the repository and/or are new to Github or IVIM, please see the jupyter notebook below:
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* [Introduction to TF2.4_IVIM-MRI_CodeCollection github and IVIM Analysis using Python](doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb)
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## Repository Organization
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The repository is organized in four main folders along with configuration files for automated testing.
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## View Testing Reports
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[![Unit tests](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/unit_test.yml/badge.svg?branch=main)](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/unit_test.yml)
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[![Algorithm Analysis](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/analysis.yml/badge.svg?branch=main)](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/analysis.yml)
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[![Build & Deploy Documentation](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/documentation.yml/badge.svg?branch=main)](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/documentation.yml)
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[![Build & Deploy Website](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/website.yml/badge.svg?branch=main)](https://github.com/OSIPI/TF2.4_IVIM-MRI_CodeCollection/actions/workflows/website.yml)

‎WrapImage/nifti_wrapper.py

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For saving the 3d nifti images of the output of the algorithm
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"""
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if affine is None:
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affine = np.eye(data.ndim + 1)
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affine = np.eye(4)
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else:
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affine = np.array(affine.reshape(4, 4))
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output_img = nib.nifti1.Nifti1Image(data, affine , **kwargs)
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nib.save(output_img, output_file)
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n = data.ndim
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total_iteration = np.prod(data.shape[:n-1])
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for idx, view in tqdm(loop_over_first_n_minus_1_dimensions(data), desc=f"{args.algorithm} is fitting", dynamic_ncols=True, total=total_iteration):
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[f_fit, Dp_fit, D_fit] = fit.osipi_fit(view, bvals)
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f_image.append(f_fit)
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Dp_image.append(Dp_fit)
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D_image.append(D_fit)
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fit_result = fit.osipi_fit(view, bvals)
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f_image.append(fit_result["f"])
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Dp_image.append(fit_result["D*"])
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D_image.append(fit_result["D"])
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# Convert lists to NumPy arrays
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f_image = np.array(f_image)

‎doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb

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