Wang Junyi, Zhu Xi, Zhang Wei, Du Mubai, Wells William M, O'Donnell Lauren J, Zhang Fan
University of Electronic Science and Technology of China, Chengdu, China.
Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Neuroimage. 2025 Jul 26;318:121401. doi: 10.1016/j.neuroimage.2025.121401.
Diffusion MRI (dMRI) is an advanced imaging technique that enables in-vivo tracking of white matter fiber tracts and estimates the underlying cellular microstructure of brain tissues. Groupwise registration of dMRI data from multiple individuals is an important task for brain template creation and investigation of inter-subject brain variability. However, groupwise registration is a challenging task due to the uniqueness of dMRI data that include multi-dimensional, orientation-dependent signals that describe not only the strength but also the orientation of water diffusion in brain tissues. Deep learning approaches have shown successful performance in standard subject-to-subject dMRI registration. However, no deep learning methods have yet been proposed for groupwise dMRI registration. . In this work, we propose Deep Diffusion MRI Template (DDTemplate), which is a novel deep-learning-based method building upon the popular VoxelMorph framework to take into account dMRI fiber tract information. DDTemplate enables joint usage of whole-brain tissue microstructure and tract-specific fiber orientation information to ensure alignment of white matter fiber tracts and whole brain anatomical structures. We propose a novel deep learning framework that simultaneously trains a groupwise dMRI registration network and generates a population brain template. During inference, the trained model can be applied to register unseen subjects to the learned template. We compare DDTemplate with several state-of-the-art registration methods and demonstrate superior performance on dMRI data from multiple cohorts (adolescents, young adults, and elderly adults) acquired from different scanners. Furthermore, as a testbed task, we perform a between-population analysis to investigate sex differences in the brain, using the popular Tract-Based Spatial Statistics (TBSS) method that relies on groupwise dMRI registration. We find that using DDTemplate can increase the sensitivity in population difference detection, showing the potential of our method's utility in real neuroscientific applications.
扩散磁共振成像（dMRI）是一种先进的成像技术，能够在体内追踪白质纤维束，并估计脑组织潜在的细胞微观结构。对来自多个个体的dMRI数据进行组间配准是创建脑模板和研究个体间脑差异的一项重要任务。然而，由于dMRI数据的独特性，组间配准是一项具有挑战性的任务，dMRI数据包含多维的、依赖方向的信号，这些信号不仅描述了脑组织中水分子扩散的强度，还描述了其方向。深度学习方法在标准的个体间dMRI配准中已显示出成功的表现。然而，尚未有针对组间dMRI配准提出的深度学习方法。在这项工作中，我们提出了深度扩散磁共振成像模板（DDTemplate），这是一种基于流行的VoxelMorph框架的新型深度学习方法，以考虑dMRI纤维束信息。DDTemplate能够联合使用全脑组织微观结构和特定纤维束的纤维方向信息，以确保白质纤维束和全脑解剖结构的对齐。我们提出了一种新颖的深度学习框架，该框架同时训练组间dMRI配准网络并生成群体脑模板。在推理过程中，训练好的模型可用于将未见过的受试者配准到学习到的模板上。我们将DDTemplate与几种最先进的配准方法进行比较，并在来自不同扫描仪采集的多个队列（青少年、年轻人和老年人）的dMRI数据上展示了卓越的性能。此外，作为一个测试任务，我们使用依赖组间dMRI配准的流行的基于纤维束的空间统计学（TBSS）方法进行群体间分析，以研究大脑中的性别差异。我们发现使用DDTemplate可以提高群体差异检测的灵敏度，显示了我们的方法在实际神经科学应用中的潜在效用。
