TractRLFusion: A GPT-Based Multi-Critic Policy Fusion Framework for Fiber Tractography

2026-01-20

AI Summary

This study addresses the persistent challenge in tractography of accurately reconstructing white matter pathways while suppressing spurious connections. To this end, the authors propose a novel multi-strategy fusion framework based on deep reinforcement learning, marking the first application of a GPT-inspired architecture to this domain. The approach integrates a two-stage training data selection scheme with a multi-critic fine-tuning mechanism to enhance both reconstruction accuracy and anatomical plausibility. Comprehensive experiments on the HCP, ISMRM, and TractoInferno datasets demonstrate that the proposed method significantly outperforms established classical techniques, single-strategy reinforcement learning baselines, and state-of-the-art deep reinforcement learning approaches in terms of both quantitative metrics and neuroanatomical fidelity.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Tractography plays a pivotal role in the non-invasive reconstruction of white matter fiber pathways, providing vital information on brain connectivity and supporting precise neurosurgical planning. Although traditional methods relied mainly on classical deterministic and probabilistic approaches, recent progress has benefited from supervised deep learning (DL) and deep reinforcement learning (DRL) to improve tract reconstruction. A persistent challenge in tractography is accurately reconstructing white matter tracts while minimizing spurious connections. To address this, we propose TractRLFusion, a novel GPT-based policy fusion framework that integrates multiple RL policies through a data-driven fusion strategy. Our method employs a two-stage training data selection process for effective policy fusion, followed by a multi-critic fine-tuning phase to enhance robustness and generalization. Experiments on HCP, ISMRM, and TractoInferno datasets demonstrate that TractRLFusion outperforms individual RL policies as well as state-of-the-art classical and DRL methods in accuracy and anatomical reliability.

Problem

• tractography
• white matter
• spurious connections
• fiber reconstruction
• brain connectivity

Innovation

• policy fusion
• multi-critic reinforcement learning
• GPT-based framework
• fiber tractography
• data-driven training