Quantification of cardiac motion with cine Cardiac Magnetic Resonance Imaging (CMRI) is an integral part of arrhythmogenic right ventricular cardiomyopathy (ARVC) diagnosis. Yet, the expert evaluation of motion abnormalities with CMRI is a challenging task. To automatically assess cardiac motion, we register CMRIs from different time points of the cardiac cycle using Implicit Neural Representations (INRs) and perform a biomechanically informed regularization inspired by the myocardial incompressibility assumption. To enhance the registration performance, our method first rectifies the inter-slice misalignment inherent to CMRI by performing a rigid registration guided by the long-axis views, and then increases the through-plane resolution using an unsupervised deep learning super-resolution approach. Finally, we propose to synergically combine information from short-axis and 4-chamber long-axis views, along with an initialization to incorporate information from multiple cardiac time points. Thereafter, to quantify cardiac motion, we calculate global and segmental strain over a cardiac cycle and compute the peak strain. The evaluation of the method is performed on a dataset of cine CMRI scans from 47 ARVC patients and 67 controls. Our results show that inter-slice alignment and generation of super-resolved volumes combined with joint analysis of the two cardiac views, notably improves registration performance. Furthermore, the proposed initialization yields more physiologically plausible registrations. The significant differences in the peak strain, discerned between the ARVC patients and healthy controls suggest that automated motion quantification methods may assist in diagnosis and provide further understanding of disease-specific alterations of cardiac motion.

翻译：利用电影心脏磁共振成像（CMRI）量化心脏运动是致心律失常性右心室心肌病（ARVC）诊断不可或缺的环节。然而，通过CMRI专家评估运动异常是一项具有挑战性的任务。为自动评估心脏运动，我们采用隐式神经表示（INRs）对心动周期不同时相的CMRI进行配准，并基于心肌不可压缩性假设实施生物力学约束正则化。为提升配准性能，本方法首先通过长轴视图引导的刚性配准校正CMRI固有的层间错位，继而采用无监督深度学习超分辨率方法提高层面间分辨率。最后，我们提出协同融合短轴与四腔长轴视图信息，并结合初始化策略整合多时相心脏信息。进而，为量化心脏运动，我们计算心动周期内的整体与节段应变，并获取峰值应变。基于47例ARVC患者与67例对照组的电影CMRI数据集进行方法评估。结果表明，层间对齐与超分辨率体素生成结合双视图联合分析显著提升了配准性能。此外，所提出的初始化策略可生成更符合生理特征的配准结果。ARVC患者与健康对照组间峰值应变的显著差异表明，自动化运动量化方法可能辅助诊断，并加深对疾病特异性心脏运动改变的理解。