arXiv 2508.045682025-08-06cs.CV

DDTracking: 一种用于扩散磁共振成像束追踪的深度生成框架，结合流线局部-全局时空建模

DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling

University of Electronic Science and Technology of China（电子科技大学）；Nanjing University of Science and Technology（南京理工大学）；Brigham and Women’s Hospital（布里奇沃特医院）；Harvard Medical School（哈佛医学院）

Yijie Li, Wei Zhang, Xi Zhu, Ye Wu, Yogesh Rathi, Lauren J. O'Donnell, Fan Zhang

备注：: Preprint version. The content may be updated in the future
DOI：: 10.1016/j.media.2026.103967

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AI总结：

DDTracking通过深度生成框架结合局部-全局时空建模，实现更精确的扩散磁共振成像束追踪。

摘要：

本文提出了DDTracking，一种新颖的深度生成框架，用于扩散磁共振成像束追踪，将流线传播建模为条件去噪扩散过程。在DDTracking中，我们引入了一个双路径编码网络，联合建模局部空间编码（在每个流线点捕捉精细结构细节）和全局时间依赖性（确保整个流线的长程一致性）。此外，我们设计了一个条件扩散模型模块，利用学习到的局部和全局嵌入来预测流线传播方向，以端到端可训练的方式进行束追踪。我们对多样、独立获取的dMRI数据集进行了全面评估，包括合成和临床数据。在两个具有地面真实值的基准测试（ISMRM挑战和TractoInferno）上进行的实验表明，DDTracking在很大程度上优于当前最先进的束追踪方法。此外，我们的结果强调了DDTracking在异质数据集上的强大泛化能力，涵盖各种健康状况、年龄组、成像协议和扫描仪类型。总体而言，DDTracking提供了在解剖学上合理且稳健的束追踪，为广泛的应用提供了可扩展、可调整和端到端可学习的解决方案。代码可在：https://github.com/yishengpoxiao/DDtracking.git

英文摘要：

This paper presents DDTracking, a novel deep generative framework for diffusion MRI tractography that formulates streamline propagation as a conditional denoising diffusion process. In DDTracking, we introduce a dual-pathway encoding network that jointly models local spatial encoding (capturing fine-scale structural details at each streamline point) and global temporal dependencies (ensuring long-range consistency across the entire streamline). Furthermore, we design a conditional diffusion model module, which leverages the learned local and global embeddings to predict streamline propagation orientations for tractography in an end-to-end trainable manner. We conduct a comprehensive evaluation across diverse, independently acquired dMRI datasets, including both synthetic and clinical data. Experiments on two well-established benchmarks with ground truth (ISMRM Challenge and TractoInferno) demonstrate that DDTracking largely outperforms current state-of-the-art tractography methods. Furthermore, our results highlight DDTracking's strong generalizability across heterogeneous datasets, spanning varying health conditions, age groups, imaging protocols, and scanner types. Collectively, DDTracking offers anatomically plausible and robust tractography, presenting a scalable, adaptable, and end-to-end learnable solution for broad dMRI applications. Code is available at: https://github.com/yishengpoxiao/DDtracking.git

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。