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TractLearn: A geodesic learning framework for quantitative analysis of brain bundles.

Arnaud Attyé1, Félix Renard2, Monica Baciu2

  • 1Neuroradiology and MRI, Grenoble Alpes University Hospital, Grenoble, France; School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.

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|March 10, 2021
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TractLearn uses geodesic learning for brain fascicle analysis, improving detection of subtle alterations in diffusion MRI data. This framework offers a novel manifold approach for quantitative analysis in precision medicine.

Keywords:
Diffusion MRIFiber tractographyManifold learningPrecision medicine

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Physics

Background:

  • Deep learning excels at segmenting brain fascicles from diffusion MRI.
  • Quantitative analysis of fascicles relies on tractography metrics or voxel data.
  • General Linear Model (GLM) struggles with high-dimensional, low-sample data, showing high variability in controls.

Purpose of the Study:

  • Introduce TractLearn, a unified framework for quantitative brain fascicle analysis.
  • Utilize geodesic learning and Riemannian geometry for data-driven analysis.
  • Improve detection of subtle quantitative alterations in brain bundles.

Main Methods:

  • Developed TractLearn, a unified framework using geodesic learning.
  • Employed a manifold approach to model control variability, moving beyond Euclidean means.
  • Mapped high-dimensional image data to a reduced latent space of brain fascicles.

Main Results:

  • Demonstrated robustness in a healthy population using test-retest multi-shell diffusion MRI data.
  • Successfully differentiated global MRI session effects from local bundle alterations.
  • Validated efficiency on a small cohort of mild traumatic brain injury patients.

Conclusions:

  • TractLearn provides a novel manifold approach for capturing anatomical variability.
  • The tool detects global voxel value variations, considering voxel interactions within structures.
  • TractLearn is a ready-to-use algorithm for identifying nonlinear variations in diffusion MRI metrics, supporting precision medicine.