HeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation

• URL: http://arxiv.org/abs/2607.04243v1
• Date: Sun, 05 Jul 2026 11:42:37 GMT
• Title: HeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation
• Abstract summary: HeartVolMesh lifts each template to an anisotropic Gaussian kernel and uses a 3D CNN-GNN to predict per-vertex displacements.<n>For volumetric meshing, we warp a fixed tetrahedral template to the reconstructed surface via staged alignment, non-rigid registration, and deformation propagation.<n>Experiments show consistent gains over deformation-based baselines in surface mesh accuracy and volumetric mesh fidelity.
• Score: 40.08276134769528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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