Package: radtract
Radiomic Tractometry (RadTract)Copyright © German Cancer Research Center (DKFZ),Division of Medical Image Computing (MIC). Please make sure that your usage of this code is in compliance with the codelicense.If you use RadTract, please cite ourpaper:Neher, P., Hirjak, D. & Maier-Hein, K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 15, 303 (2024). https://doi.org/10.1038/s41467-023-44591-3OverviewPython package for radiomic tractometry (RadTract). RadTract is a tool designed for tract-specific microstructural analysis of the brain’s white matter using diffusion MRI. It enhances traditional tractometry, which often misses valuable information due to its reliance on summary statistics and scalar values.RadTract incorporates radiomics, a method that analyzes a multitude of quantitative image features beyond visual perception, into tractometry. This integration allows for improved predictive modeling while maintaining the localization capability of tractometry.RadTract has demonstrated its effectiveness in diagnosing disease subgroups across various datasets and estimating demographic and clinical parameters in multiple clinical populations. It holds the potential to pioneer a new generation of tract-specific imaging biomarkers, benefiting a wide range of applications from basic neuroscience to medical research.For details about the approach, please refer to ourpaper:Neher, P., Hirjak, D. & Maier-Hein, K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 15, 303 (2024). https://doi.org/10.1038/s41467-023-44591-3. An overview of the method is shown in Figure 1.a multitude_Figure 1: Illustration of the complete RadTract process. The points of a statically resampled tract (a) can be seen as samples of partly overlapping classes that are not linearly separable. We are aiming at finding the hyperplanes, superimposed as white lines on the tract in (a), that optimally separate the classes with the smallest amount of errors. This task can be solved using large-margin classifiers such as SVMs. This enables us to create parcellations directly in voxel-space (b) that do not suffer from projection-induced misassignments, as is the case in the centerline-based approach (d). For visualization purposes, the tract parcellation in voxel-space is projected back on the original streamlines (e). The proposed tract parcellation in voxel-space (b) is used to calculate a multitude of radiomics features per parcel, visualized in (c). Exemplary feature classes and image filters available when usingpyradiomicsas calculation engine are listed in (f). RadTract currently supportsMIRPas an alternative engine for calculating radiomics features.InstallationRequirementsNo specific hardware requirements. A state-of-the-art desktop computer should be sufficient.Tested on Ubuntu 22.04 but should run on other systems as well.Tested with Python 3.8 and higherNumpy should be installed prior to the RadTract setup, all other dependencies will be installed automatically.It is recommended to use a virtual environment for the installation.See.gitlab-ci.ymlfor the currently tested configurations.InstallationInstallation via anaconda is not supported currently!virtual environmentCreate a virtual environment:python -m venv myvenvActivate the virtual environment:source myvenv/bin/activateInstallationInstall from source: navigate to the root directory of RadTract and runpip install .Install from PyPI: runpip install radtractInstallation should complete within a few seconds.ExamplesA complete pipeline example can be found inexample.ipynb.Further examples can be found in the RadTract test scripttests\test_radtract.py. Test data is included intests\test_data.Expected runtimesRadTract parcellation and feature calculation should complete within a couple of minutes on a standard desktop computer.