用协方差引导图变形，精准重建心脏体积网格。
HeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation
- 基于3D CNN-GNN预测顶点位移与协方差参数
- 表面网格精度与体网格保真度显著优于基线方法
- 适合需高保真心脏建模的临床仿真研究
精确的患者特异性四面体心脏网格对计算机模拟试验至关重要，但传统分割-建模流程常模糊薄壁结构，且跨病例对应性有限。本文提出HeartVolMesh，将每个模板顶点升维为各向异性高斯核，利用3D CNN-GNN从体图像中预测顶点位移及Cholesky参数化的协方差。训练采用协方差感知的负对数似然损失，并加入轻量级网格正则化。网格生成通过分阶段对齐、非刚性配准和变形传播，将固定四面体模板形变为重构表面，保证连接性和对应性，分辨率由模板密度控制。实验表明，在表面网格精度和体网格保真度上持续优于基于变形的基线方法。
原文摘要 · Abstract (English)
Accurate patient-specific tetrahedral cardiac meshes are essential for in-silico trials, yet common segmentation-then-modelling pipelines can blur thin-wall anatomy and offer limited cross-case correspondence. We propose HeartVolMesh, which lifts each template vertex to an anisotropic Gaussian kernel and uses a 3D CNN-GNN to predict per-vertex displacements and Cholesky-parameterized covariances from volumetric images. Training is guided by a covariance-aware negative log-likelihood loss with lightweight mesh regularization. For volumetric meshing, we warp a fixed tetrahedral template to the reconstructed surface via staged alignment, non-rigid registration, and deformation propagation, preserving connectivity and correspondence by construction, with resolution controlled by template density. Experiments show consistent gains over deformation-based baselines in surface mesh accuracy and volumetric mesh fidelity.
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