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.

翻译：纤维束成像作为神经影像学的基石，通过扩散磁共振成像实现了大脑白质通路的精细映射。这对于理解大脑连接与功能至关重要，使其成为神经学应用中一项有价值的工具。尽管其重要性显著，纤维束成像仍因其复杂性及易产生假阳性而面临挑战，导致关键通路被错误表征。为解决这些问题，近期策略已转向深度学习，利用依赖于精确标注数据的监督学习，或无需标注数据的强化学习。在本工作中，我们提出Tract-RLFormer网络，该网络在双阶段策略优化过程中同时采用监督学习与强化学习，显著提升了跨不同数据集的准确性与泛化能力。通过采用特定纤维束的研究方法，我们的网络直接勾勒出目标纤维束，绕过了传统的分割流程。通过在TractoInferno、HCP及ISMRM-2015等数据集上的严格验证，我们的方法展现了纤维束成像领域的突破性进展，证明了其精准绘制大脑白质纤维束的能力。