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A Riemannian framework for incorporating white matter bundle prior in orientation distribution function based

Thomas Durantel1, Gabriel Girard2,3, Emmanuel Caruyer1

  • 1Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN - ERL U 1228, Rennes, France.

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|March 25, 2025
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Summary
This summary is machine-generated.

This study introduces a novel method to improve diffusion magnetic resonance imaging (dMRI) tractography by using anatomical priors. This approach enhances the accuracy of brain connectivity mapping, particularly in complex fiber regions.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Diffusion magnetic resonance imaging (dMRI) tractography is crucial for studying brain structural connectivity.
  • Current tractography methods often generate false positive fibers due to crossing fibers and bottlenecks, limiting clinical reliability.
  • Existing algorithms struggle with accuracy in complex white matter regions.

Purpose of the Study:

  • To develop a novel method for guiding dMRI tractography algorithms using anatomical prior knowledge.
  • To improve the accuracy and reliability of tractograms, especially in challenging regions with crossing fibers.
  • To enhance the clinical applicability of dMRI tractography for studying brain anatomy and function.

Main Methods:

  • Developed a method to create anatomical priors applicable to orientation distribution function (ODF)-based tractography.
  • Captured tract orientation distribution (TOD) from an atlas of segmented fiber bundles.
  • Incorporated TOD priors into tracking using a Riemannian framework with iFOD2 and Trekker PTT algorithms.
  • Tested on Diffusion-Simulated Connectivity (DiSCo) and Human Connectome Project (HCP) datasets.

Main Results:

  • The proposed method significantly improves the overall spatial coverage and connectivity of tractograms.
  • Enhanced performance was particularly noted in regions with crossing fibers.
  • Demonstrated superior results compared to the bundle specific tractography (BST) method's priors.

Conclusions:

  • The novel anatomical prior incorporation method enhances dMRI tractography accuracy.
  • This approach offers potential for improved fiber reconstruction in clinical data, informed by high-quality data priors.
  • Facilitates more robust studies of brain anatomy and function using dMRI.