Tract-RLFormer: A Tract-Specific RL policy based Decoder-only Transformer Network

TL;DR

Tract-RLFormer is a novel tract-specific transformer network that combines supervised and reinforcement learning to enhance the accuracy and generalizability of brain white matter tractography from diffusion MRI data.

Contribution

It introduces a two-stage policy refinement process using a transformer-based architecture tailored for tract-specific delineation, improving upon existing methods.

Findings

Significantly improves tractography accuracy across multiple datasets.

Demonstrates robustness and generalizability in brain white matter mapping.

Outperforms traditional and deep learning-based tractography methods.

Abstract

Fiber tractography is a cornerstone of neuroimaging, enabling the detailed mapping of the brain's white matter pathways through diffusion MRI. This is crucial for understanding brain connectivity and function, making it a valuable tool in neurological applications. Despite its importance, tractography faces challenges due to its complexity and susceptibility to false positives, misrepresenting vital pathways. To address these issues, recent strategies have shifted towards deep learning, utilizing supervised learning, which depends on precise ground truth, or reinforcement learning, which operates without it. In this work, we propose Tract-RLFormer, a network utilizing both supervised and reinforcement learning, in a two-stage policy refinement process that markedly improves the accuracy and generalizability across various data-sets. By employing a tract-specific approach, our network directly delineates the tracts of interest, bypassing the traditional segmentation process. Through rigorous validation on datasets such as TractoInferno, HCP, and ISMRM-2015, our methodology demonstrates a leap forward in tractography, showcasing its ability to accurately map the brain's white matter tracts.