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.

翻译：纤维束追踪在无创重建白质纤维通路中发挥着关键作用，为脑连接性提供重要信息，并支持精准的神经外科手术规划。尽管传统方法主要依赖于经典的确定性和概率性方法，但近期的进展受益于监督式深度学习（DL）和深度强化学习（DRL），以改进纤维束重建。纤维束追踪中一个持续的挑战是在最小化虚假连接的同时准确重建白质纤维束。为解决此问题，我们提出了TractRLFusion，一种新颖的基于GPT的策略融合框架，通过数据驱动的融合策略整合多个强化学习策略。我们的方法采用两阶段训练数据选择过程以实现有效的策略融合，随后进行多评判器微调阶段以增强鲁棒性和泛化能力。在HCP、ISMRM和TractoInferno数据集上的实验表明，TractRLFusion在准确性和解剖学可靠性方面优于单个强化学习策略以及最先进的经典方法和深度强化学习方法。