用于致心律失常性右心室心肌病自动应变定量的深度学习
Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy
AI总结：
本研究提出一种基于隐式神经表示的深度学习方法，通过层间对齐、超分辨率及多视图联合分析自动配准CMRI并量化心脏应变，有效区分ARVC患者与健康对照，辅助诊断。
摘要：
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）诊断的重要组成部分。然而，专家利用CMRI评估运动异常是一项具有挑战性的任务。为了自动评估心脏运动，我们使用隐式神经表示（INRs）配准来自心动周期不同时间点的CMRI，并执行受心肌不可压缩性假设启发的生物力学正则化。为了提高配准性能，我们的方法首先通过执行由长轴视图引导的刚性配准来纠正CMRI固有的层间错位，然后使用无监督深度学习超分辨率方法提高层面内分辨率。最后，我们提出协同结合来自短轴和4腔长轴视图的信息，以及通过初始化来整合多个心动时间点的信息。此后，为了量化心脏运动，我们计算了一个心动周期内的全局和节段应变，并计算峰值应变。该方法在包含47名ARVC患者和67名对照者的电影CMRI扫描数据集上进行了评估。结果表明，层间对齐和超分辨率体积生成与两个心脏视图的联合分析相结合，显著提高了配准性能。此外，提出的初始化产生了更具生理合理性的配准。在ARVC患者和健康对照之间观察到的峰值应变的显著差异表明，自动运动定量方法可辅助诊断，并进一步理解疾病特异性心脏运动改变。
英文摘要：
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.
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