用神经微分流学习胎盘的体积一致隐式模板，提升三维对齐精度。
Volumetrically Consistent Implicit Atlas Learning via Neural Diffeomorphic Flow for Placenta MRI
- 联合重建符号距离函数与神经微分流，实现体积一致性对齐
- 在胎盘MRI上达到更优几何保真度与体积对齐效果
- 适合需要拓扑一致性的群体分析研究者使用
建立解剖形状间的密集体积对应关系对群体分析至关重要，但对隐式神经表示仍具挑战。现有隐式配准方法多依赖零等值面附近的监督，仅捕捉表面对应，忽略内部变形约束。本文提出一种体积一致的隐式模型，将符号距离函数（SDF）重建与神经微分流相结合，学习胎盘的共享标准模板。通过雅可比行列式和双调和正则化，抑制局部折叠并促进全局一致变形。在胎盘MRI的应用中，该方法联合重建个体胎盘，将其对齐至群体导出的隐式模板，并在统一的规范空间中实现体素级强度映射。在活体胎盘MRI数据上的实验表明，相比基于表面的隐式基线方法，本方法在几何保真度和体积对齐方面均有提升，生成的解剖可解释且拓扑一致的展开结果，适用于群体分析。
原文摘要 · Abstract (English)
Establishing dense volumetric correspondences across anatomical shapes is essential for group-level analysis but remains challenging for implicit neural representations. Most existing implicit registration methods rely on supervision near the zero-level set and thus capture only surface correspondences, leaving interior deformations under-constrained. We introduce a volumetrically consistent implicit model that couples reconstruction of signed distance functions (SDFs) with neural diffeomorphic flow to learn a shared canonical template of the placenta. Volumetric regularization, including Jacobian-determinant and biharmonic penalties, suppresses local folding and promotes globally coherent deformations. In the motivating application to placenta MRI, our formulation jointly reconstructs individual placentas, aligns them to a population-derived implicit template, and enables voxel-wise intensity mapping in a unified canonical space. Experiments on in-vivo placenta MRI scans demonstrate improved geometric fidelity and volumetric alignment over surface-based implicit baseline methods, yielding anatomically interpretable and topologically consistent flattening suitable for group analysis.
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