通过直接变形纤维束端点实现的皮层对齐
ConSEAL: Connectivity-Informed Streamline Endpoint Alignment for Diffeomorphic Cortical Registration
AI总结：
本文提出了一种基于连接性的皮层对齐方法，通过直接操作白质纤维束端点来对齐皮层表面，以提高纤维束层面的对应性，并在主要纤维束上实现更高的连接性重叠系数和更强的鲁棒性。
摘要：
皮层表面注册通常由局部几何描述符（例如沟回深度和曲率）驱动。尽管这种方法实现了几何对应，但忽略了白质解剖结构所施加的远距离连接约束。扩散磁共振成像束追踪提供了这些关键约束；然而，先前的连接性指导流程通常对预计算的连接性矩阵进行对齐，使优化高度敏感于连接性估计及其分辨率。在本文中，我们提出了一种新的基于连接性的皮层对齐方法，通过直接在白质纤维束端点上操作来对齐皮层表面。我们将束端点建模为产品流形Ω×Ω上的点云，其中Ω代表膨胀的皮层半球的球形域。我们的对齐方法通过迭代（i）通过最小化连接性不匹配计算Ω的小变形扭曲，并（ii）根据此扭曲更新端点。该方法依赖于一个几何框架，确保输出扭曲是微分同胚，并具有最终目标，即优化已知纤维束的匹配。在人类连接组计划（HCP）数据上的实验表明，该方法在纤维束层面实现了改进的对应性，实现了主要纤维束上的更高连接性重叠系数，并在Ω的网格分辨率下比最先进的方法如ENCORE和MSMAll表现出更强的鲁棒性。
英文摘要：
Cortical surface registration is often driven by local geometric descriptors (e.g., sulcal depth and curvature). While this approach achieves geometric correspondence, it neglects the long-range wiring constraints imposed by white-matter anatomy. Diffusion MRI tractography offers these crucial constraints; however, prior connectivity-informed pipelines typically align precomputed connectivity matrices, making the optimization highly sensitive to connectivity estimation and its resolution. In this paper, we introduce ConSEAL (Connectivity-Informed Streamline Endpoint Alignment), a novel connectivity-based surface registration method that aligns cortical surfaces by operating directly on white-matter fiber-tract endpoints. We model tract endpoints as a point cloud on the product manifold $Ω\times Ω$, where $Ω$ represents the spherical domain of the inflated cortical hemispheres. Our alignment method iteratively (i) computes a small diffeomorphic warp for $Ω$ by minimizing connectivity mismatch, and (ii) updates the endpoints based on this warp. The method relies on a geometric framework that ensures output warps are diffeomorphisms and has a final goal that optimizes the matching of well-known fiber bundles. Experiments on Human Connectome Project (HCP) data demonstrate that ConSEAL improves tract-level correspondence, achieving higher connectivity-level overlap coefficients on major fiber bundles and stronger robustness across grid resolutions for $Ω$ compared to state-of-the-art methods such as ENCORE and MSMAll.
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