树的泊松流与瓦瑟斯坦配准
Poisson Flow and Wasserstein Registration of Trees
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中文总结 AI 辅助
针对成像应用中树状结构的配准问题，提出基于筛选泊松流与瓦瑟斯坦距离的非线性配准框架，无需显式地标或分支匹配，可在结构MRI的皮层沟回折叠配准中生成有解剖学意义的对应关系。
中文摘要 AI 辅助
树状结构广泛存在于各类成像应用中，包括血管网络、神经元树突、气道树以及大脑皮层沟回折叠等。本文提出一种基于筛选泊松流（screened Poisson flow）与瓦瑟斯坦距离（Wasserstein distance）的非线性配准框架，其中筛选泊松方程将几何特征转换为平滑的多尺度概率分布；配准通过最小化这些分布间的瓦瑟斯坦距离实现，无需显式地标或分支匹配即可生成具有解剖学意义的对应关系，该框架已在结构磁共振成像（structural MRI）的大脑皮层沟回折叠模式非线性配准中得到验证。
英文摘要
Tree-like structures arise in numerous imaging applications, including vascular networks, neuronal arbors, airway trees, and cortical sulcal--gyral folding. We present a nonlinear registration framework based on screened Poisson flow and Wasserstein distance. The screened Poisson equation transforms geometric features into smooth multiscale probability distributions. Registration is formulated by minimizing the Wasserstein distance between these distributions, producing anatomically meaningful correspondences without explicit landmark or branch matching. The framework is demonstrated on the nonlinear registration of cortical sulcal--gyral folding patterns from structural MRI.
