基于多尺度时间建模与可微分轮廓渲染的个性化4D全心网格重建
Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering
- Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）
- School of Automation, Southeast University（东南大学自动化学院）
- Department of Medicine, National University of Singapore（新加坡国立大学医学系）
- Department of Cardiology, National University Heart Centre Singapore（新加坡国立大学心脏中心心脏病学系）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出端到端框架，从稀疏电影MRI重建4D全心网格，利用可微分轮廓渲染和多尺度时间建模，实现低误差（1.68 mm）和高运动平滑度。
中文摘要 AI 辅助
从稀疏电影MRI中准确重建4D全心网格对于创建心脏数字孪生至关重要，但由于2D切片覆盖有限以及心脏形状与运动之间的复杂耦合，仍然具有挑战性。现有方法通常依赖于中间轮廓拟合，并且通常重建静态、单相或部分心脏几何结构，限制了其捕捉全腔动力学的能力。我们提出了一种新颖的端到端框架，通过学习图像到网格的映射，从多视角2D电影MRI序列重建时间分辨的全心网格。该框架结合了受比尔-朗伯衰减原理启发的可微分轮廓渲染器，通过基于轮廓的投影损失实现对3D+t网格变形的解剖感知监督。为了改善心脏周期内的时间一致性，我们进一步引入了多尺度时间建模模块，该模块将全局周期级动力学与局部帧间一致性相结合，以生成平滑且生理上合理的网格轨迹。所提出的方法实现了全心平均绝对误差1.68 ± 0.31 mm和运动抖动0.77 ± 0.17 mm/帧³，优于现有方法，具有更低的重建误差和显著改善的运动平滑度。它还改善了多个电影MRI视图中的2D轮廓对齐，并支持概念验证的电生理模拟。代码将在稿件被接受发表后公开发布。
英文摘要
Accurate 4D whole-heart mesh reconstruction from sparse cine MRI is critical for creating cardiac digital twins, but remains challenging due to limited 2D slice coverage and the complex coupling between cardiac shape and motion. Existing methods often rely on intermediate contour fitting and typically reconstruct static, single-phase, or partial cardiac geometries, limiting their ability to capture full-chamber dynamics. We propose a novel end-to-end framework for reconstructing temporally resolved whole-heart meshes from multi-view 2D cine MRI sequences by learning an image-to-mesh mapping. The framework incorporates a differentiable contour renderer inspired by the Beer-Lambert attenuation principle, enabling anatomy-aware supervision of 3D+t mesh deformation through contour-based projection losses. To improve temporal consistency across the cardiac cycle, we further introduce a multi-scale temporal modeling module that integrates global cycle-level dynamics with local inter-frame coherence to generate smooth and physiologically plausible mesh trajectories. The proposed method achieved a whole-heart mean absolute error of 1.68 $\pm$ 0.31 mm and a motion jitter of 0.77 $\pm$ 0.17 $\mathrm{mm}/\mathrm{frame}^{3}$, outperforming existing methods with lower reconstruction error and substantially improved motion smoothness. It also improved 2D contour alignment across multiple cine MRI views and supported downstream proof-of-concept electrophysiological simulation. The code will be released publicly upon acceptance of the manuscript for publication.
