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Track-to-Learn: A general framework for tractography with deep reinforcement learning.

Antoine Théberge1, Christian Desrosiers2, Maxime Descoteaux1

  • 1Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, CA, J1K 2R1.

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Summary

Track-To-Learn uses deep reinforcement learning to improve brain white-matter tractography. This novel approach bypasses the need for curated data, offering a more efficient and accurate method for assessing brain connectivity.

Keywords:
Deep learningReinforcement learningTractography

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • Diffusion MRI tractography is the sole non-invasive method for mapping white-matter structural connectivity in the brain.
  • Existing tractography methods often generate inaccurate tracks and miss true connections.
  • Prior machine learning approaches require difficult-to-obtain curated streamline data.

Purpose of the Study:

  • To introduce a novel framework, Track-To-Learn, for brain tractography.
  • To leverage deep reinforcement learning to overcome limitations of existing tractography methods.
  • To eliminate the need for curated ground-truth data in tractography.

Main Methods:

  • Tractography is framed as a deep reinforcement learning problem.
  • Algorithms are trained to maximize rewards based on streamline alignment with diffusion MRI principal directions.
  • The framework, Track-To-Learn, utilizes neural networks without anatomical priors.

Main Results:

  • Track-To-Learn demonstrates competitive performance on established datasets.
  • The method shows minimal performance degradation when generalizing to new, unseen data.
  • Achieved comparable results to existing machine learning-based tractography algorithms.

Conclusions:

  • Deep reinforcement learning offers a viable and effective alternative for brain tractography.
  • Track-To-Learn successfully applies deep reinforcement learning to tractography, a first in the field.
  • This approach enhances the accuracy and efficiency of mapping white-matter structural connectivity.