The reconstruction of epicardial and endocardial extracellular potentials (EEP) by noninvasive methods has become a significant topic in cardiac electrophysiology over recent years. It is of great importance for the diagnosis of arrhythmia and for guidance of radiofrequency ablation, based on the difference in potentials between different locations on the heart's surface. In this study, we propose a non-local regularization of total variation (TV) in a low-rank (LR) and sparse decomposition framework, suitable for the rank-deficient problem of EEP reconstruction. LR and sparse decomposition can be utilized to extract the spatial-temporal information resulting from the sparse properties of EEP data, and the non-local similarities in the LR part can be a constraint for a non-local total variation regularization. The proposed method is implemented in simulated myocardial infarction (MI), interventional, and clinical premature ventricular contraction (PVC) experiments to verify its feasibility and reliability. Compared with the existing LR and TV methods, the proposed method performs better at potential reconstruction as well as PVC localization, particularly in the boundary of the MI region, while the results of this method are also consistent with those of invasive measurements using an EnSite 3000 system in the clinical experiment.

近年来，通过无创方法重建心外膜和心内膜细胞外电位（EEP）已成为心脏电生理学中的重要课题。这对心律失常的诊断和对射频消融的指导非常重要，这是基于心脏表面不同位置之间的电势差。在这项研究中，我们提出了在低秩（LR）和稀疏分解框架中的总变化（TV）的非局部正则化，适用于EEP重建的秩不足问题。可以利用LR和稀疏分解来提取EEP数据稀疏属性所产生的时空信息，并且LR部分中的非局部相似性可能成为非局部总变化正则化的约束。该方法在模拟性心肌梗死（MI），介入性和临床室性早搏（PVC）实验中得以实现，以验证其可行性和可靠性。与现有的LR和TV方法相比，该方法在电位重建以及PVC定位方面表现更好，特别是在MI区域的边界，而该方法的结果也与使用EnSite 3000进行的侵入性测量的结果一致系统在临床实验中。