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TractLearn is a Manifold Learning Toolbox for precision medicine. The first application is for Diffusion-Weighted MRI quantitative analysis.

Python 100.00%
diffusion-mri manifold-learning precision-medicine

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tractlearn-wholebrain's Issues

Clarification on processing steps

Hi @ArnaudAttye,

I have few questions on the processing pipeline outlined here:

  • Should the population template be computed from "control only" or from "control + patients"? The page suggests

    As TractLearn requires an excellent matching between subjects, the first steps imply non-linear coregistration in a common template space. Here we assume that you have already created a template using for example population_template coming from MRtrix (https://www.mrtrix.org/).

  • Should the DWI volumes be all registered to MNI space? For poplation-template and TractSeg outputs? Or is it only required for FA volumes? It suggests

    FA analysis does firstly require to coregister all individual FA maps (named FA_MNI.nii.gz as it is assumed to be in the MNI space for TractSeg)

  • If we want to add other parameters to compute z-score, MD for example, should the pathway be similar as used for FA?

    Note that this framework can easily be extended to other metrics.

Thanks a lot for your time. It is a great tool, having fun using it :)

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