发表机构
Indian Institute of Technology Mandi; School of Computing and Electrical Engineering（印度理工学院曼迪分校; 计算机与电气工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合强化学习与监督学习的靶向白质纤维束追踪混合框架，通过多策略融合提升性能，在TractoInferno等基准数据集上验证了其有效性，减少对真实标注的依赖。
AI 中文摘要
纤维束追踪技术能够重建大脑的结构通路，已成为现代神经成像的关键组成部分，可实现对结构连接性的详细非侵入式映射，并支持广泛的神经学研究和临床应用。然而，尽管该技术十分重要，但由于白质结构的固有复杂性以及易受假阳性影响，纤维束追踪仍是一项具有挑战性的任务，假阳性会导致关键通路的错误表征。为克服这些局限性，本论文提出了一种混合框架，将强化学习与监督学习相结合以优化强化学习策略，该框架专为靶向特定纤维束的追踪任务定制。值得注意的是，本框架不依赖真实纤维进行训练；此外，针对特定纤维束的公式化表述绕过了显式分割流程的需求，简化了整体管线。本研究包含两项主要贡献，每项均基于前一项展开：第一，引入一种混合方法，将强化学习与监督学习（具体为基于GPT的策略学习）相结合，在特定纤维束的场景下优化策略；第二，提出一种可扩展的数据驱动多策略融合框架，该框架利用多种强化学习策略的互补优势，提升纤维束追踪的性能与鲁棒性。我们通过在TractoInferno、HCP和ISMRM-2015等基准公开数据集上进行的大量验证，证明了本框架的有效性，凸显其跨数据源泛化以及准确重建大脑白质纤维束的能力。我们认为，这些贡献代表了纤维束追踪领域的重大进展，在提升鲁棒性、可靠性和准确性的同时，减少了对真实标注的依赖。
英文摘要
Fiber tractography's ability to reconstruct the brain's structural pathways, has made it a crucial component of modern neuroimaging, enabling detailed, non-invasive mapping of structural connectivity and supporting a wide range of neurological research and clinical applications. However, despite its importance, tractography remains a challenging task due to the inherent complexity of white matter structure and its susceptibility to false positives, which can lead to the misrepresentation of critical pathways. To overcome these limitations, in this thesis, we propose a hybrid framework that integrates reinforcement learning with supervised learning for refining RL policies, specifically tailored for tract-specific tractography. Notably, our framework does not rely on ground-truth fibers for training. Moreover, the tract-specific formulation bypasses the need for an explicit segmentation process, simplifying the overall pipeline. Our work includes two main contributions, each building upon the previous. First, we introduce a hybrid approach that combines reinforcement learning with supervised learning (specifically, GPT-based policy learning) to refine policies in a tract-specific context. Second, we propose a scalable framework for data-driven multi-policy fusion, which leverages the complementary strengths of multiple RL policies to improve tractography performance and robustness. We demonstrate the effectiveness of our framework through extensive validation on benchmark public datasets including TractoInferno, HCP, and ISMRM-2015, highlighting its ability to generalize across data sources and accurately reconstruct brain white matter tracts. We believe that these contributions represent significant advancements in the field of tractography, improving robustness, reliability, and accuracy while reducing dependence on ground-truth annotations.
CommentsMTech (Research) thesis at IIT Mandi
