Registration of diffusion MRI tractography is an essential step for analyzing group similarities and variations in the brain's white matter (WM). Streamline-based registration approaches can leverage the 3D geometric information of fiber pathways to enable spatial alignment after registration. Existing methods usually rely on the optimization of the spatial distances to identify the optimal transformation. However, such methods overlook point connectivity patterns within the streamline itself, limiting their ability to identify anatomical correspondences across tractography datasets. In this work, we propose a novel unsupervised approach using deep learning to perform streamline-based dMRI tractography registration. The overall idea is to identify corresponding keypoint pairs across subjects for spatial alignment of tractography datasets. We model tractography as point clouds to leverage the graph connectivity along streamlines. We propose a novel keypoint detection method for streamlines, framed as a probabilistic classification task to identify anatomically consistent correspondences across unstructured streamline sets. In the experiments, we compare several existing methods and show highly effective and efficient tractography registration performance.

翻译：扩散磁共振成像纤维束成像的配准是分析大脑白质群体相似性与变异性的关键步骤。基于流线的配准方法能够利用纤维通路的3D几何信息，实现配准后的空间对齐。现有方法通常依赖于空间距离的优化来识别最优变换。然而，此类方法忽略了流线内部的点连接模式，限制了其识别跨纤维束成像数据集解剖对应关系的能力。本研究提出了一种基于深度学习的新型无监督方法，用于执行基于流线的扩散磁共振成像纤维束成像配准。其核心思想是通过识别跨被试的对应关键点对，实现纤维束成像数据集的空间对齐。我们将纤维束成像建模为点云，以利用沿流线的图连接性。我们提出了一种针对流线的新型关键点检测方法，将其构建为概率分类任务，以识别非结构化流线集合间解剖学上一致的对应关系。在实验中，我们比较了多种现有方法，并展示了高效且有效的纤维束成像配准性能。