RePCM：区域特定和表型适应的双心室心脏运动合成
RePCM: Region-Specific and Phenotype-Adaptive Bi-Ventricular Cardiac Motion Synthesis
- School of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程学院）
- School of Automation, Southeast University, Nanjing, China（东南大学自动化学院）
- School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China（南京航空航天大学计算机科学与技术学院）
- School of Information Science and Engineering, Yunnan University, Kunming, China（云南大学信息科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出RePCM方法，通过单帧双心室网格运动补全，利用区域特定和表型适应性来提升心脏运动合成的准确性，以应对心血管疾病导致的区域和疾病特异性差异。
AI中文摘要：
心脏周期内的运动对于量化区域功能至关重要，并且强烈受到心血管疾病的影响。由于在实践中难以获得时间密集的网格序列，我们专注于利用更易获得的终舒张期帧来推断完整的周期序列。由于存在强区域和疾病特异性差异，传统方法常通过依赖生成模型来过度平滑数据，这些模型是为全球模式优化的。为了解决这个问题，我们提出了Region-Aware和Phenotype-Adaptive Bi-Ventricular Cardiac Motion Synthesis（RePCM）方法，用于单帧双心室网格运动补全。在第一阶段，重建网络学习顶点级别的运动描述符，聚类产生数据驱动的功能分区，提供显式的运动衍生区域结构。在第二阶段，Region-Specific Injection模块在条件VAE中强制执行掩码同步的区域交换，保留局部特定动态并限制跨区域混合。Phenotype-Adaptive Mixture-of-Experts先验条件于ED形状，使用解剖引导的提示来建模潜在运动趋势并捕捉跨疾病变化。在三个涵盖不同心血管疾病的数据集上的实验显示，在几何和功能指标上取得了持续的改进，并且区域特定动态的保护得到了改善。
英文摘要：
Cardiac motion over a cardiac cycle is crucial for quantifying regional function and is strongly affected by cardiovascular diseases. Since temporally dense mesh sequences are difficult to obtain in practice, we focus on leveraging the more accessible end-diastolic frame to infer a full-cycle sequence. Due to strong regional and disease-specific differences, traditional methods often oversmooth the data by relying on generative models that are optimized for global patterns. To address this problem, we propose Region-Aware and Phenotype-Adaptive Bi-Ventricular Cardiac Motion Synthesis (RePCM) for single frame Bi-ventricular mesh motion completion. In Stage I, a reconstruction network learns vertex wise motion descriptors and clustering yields a data driven functional partition, providing an explicit motion derived region structure. In Stage II, a Region-Specific Injection Module enforces masked, synchronized region exchange within a conditional VAE, preserving localized specific dynamics and restricting cross-region mixing. A Phenotype-Adaptive Mixture-of-Experts prior conditioned on ED shape uses anatomy-guided cues to model latent motion trends and capture inter-disease variability. Experiments on three datasets covering different cardiovascular diseases show consistent gains in geometric and functional metrics and improved preservation of region specific dynamics.
