发表机构
National University of Singapore; Southeast University（新加坡国立大学; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出解剖对齐的UV表面学习框架，将稀疏短轴电影MRI心肌重建转化为坐标场补全，在三个数据集上优于现有方法，并保持心室功能准确性。
AI 中文摘要
从电影MRI进行患者特定的4D心肌重建支持定量功能评估、区域运动分析和基于模拟的建模。然而，常规采集的短轴（SAX）电影MRI在穿平面方向上采样稀疏，使得密集且解剖一致性的表面重建具有挑战性。在本研究中，我们提出了一种解剖对齐的表面学习框架，该框架在共享的周向-纵向UV域上参数化心外膜和心内膜表面。这种表述将不规则的3D重建转换为结构化坐标场补全，并在受试者和心脏相位之间具有明确的对应关系。稀疏SAX轮廓被编码为UV观测场，覆盖感知采样提高了对不完整切片覆盖的鲁棒性，而拓扑和畸变感知学习保持了周向连续性和局部表面质量。在三个公开电影MRI数据集上的实验表明，所提出的方法始终优于代表性的基于网格和隐式重建方法，在ACDC上实现了总体Chamfer距离为2.887毫米，在M&Ms上为2.641毫米，在M&Ms-2上为2.810毫米。重建序列还保留了心室功能，舒张末期容积和射血分数的误差分别为3.3毫升和1.1%。这些结果表明，解剖对齐的UV学习为稀疏电影MRI重建和心肌建模提供了准确、高效且具有对应意识的表示。源代码将在此https URL提供。
英文摘要
Patient-specific 4D myocardial reconstruction from cine MRI supports quantitative functional assessment, regional motion analysis, and simulation-based modeling. However, routinely acquired short-axis (SAX) cine MRI is sparsely sampled along the through-plane direction, making dense and anatomically consistent surface reconstruction challenging. In this study, we propose an anatomy-aligned surface learning framework that parameterizes the epicardial and endocardial surfaces on a shared circumferential-longitudinal UV domain. This formulation converts irregular 3D reconstruction into structured coordinate-field completion with explicit correspondence across subjects and cardiac phases. Sparse SAX contours are encoded as UV observation fields, coverage-aware sampling improves robustness to incomplete slice coverage, and topology- and distortion-aware learning preserves circumferential continuity and local surface quality. Experiments on three public cine MRI datasets showed that the proposed method consistently outperformed representative mesh-based and implicit reconstruction approaches, achieving overall Chamfer distances of $2.887$~mm on ACDC, $2.641$~mm on M\&Ms, and $2.810$~mm on M\&Ms-2. The reconstructed sequences also preserved ventricular function, with end-diastolic volume and ejection fraction errors of $3.3$~mL and $1.1 \%$, respectively. These results demonstrate that anatomy-aligned UV learning provides an accurate, efficient, and correspondence-aware representation for sparse cine MRI reconstruction and myocardial modeling. The source code will be available at https://github.com/yuan-xiaohan/SAX2MyoSurf.
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