室性心律失常（VA）是心肌梗死后发生心源性猝死的主要原因，预测其潜在的发生风险对减少冠心病的死亡率非常重要。已知VA的触发灶多源于梗死边缘区（PIZ），且心肌缝隙连接蛋白Cx-43表达下降。我们前期也观察到PIZ重构明显的患者出现VA的概率有增大趋势，但目前国内外缺少运用心脏磁共振（CMR）定量指标评估VA潜在风险的研究。因此，我们提出假设：PIZ重构表现为心肌纤维排列紊乱，细胞外容积增大，导致心电传导异常并诱发VA，而这种结构异常是可通过CMR早期定量检测的。我们拟运用多模态CMR定量技术，进行DTI、T1mapping、Tissue tracking等成像，对PIZ的心肌纤维排列、细胞外容积及组织学特性等进行定量分析。以PIZ的电生理指标为判断VAI潜在风险的标准，并与Cx-43表达对照。探讨CMR的PIZ重构定量指标与VA的发生风险及机制的关系，为VA潜在风险的临床预测提供可靠依据。

Ventricular arrhythmia (VA) fallowing acute myocardial infarction is the main reason resulting in sudden cardiac death, predicting the potential risk of VA is very important to reduce coronary heart disease (CHD) mortality. It is known that the trigger target of VA is mainly located in the peri-infarct zone (PIZ), and the expression of connexin-43 (Cx-43) also decrease. Our previous studies have found that the more seriously the PIZ remodeling, the higher incidence arrhythmia has, but the correlation of PIZ remodeling and the potential risk of ventricular arrhythmia evaluated by quantitative CMR combined with connexin-43 are less at home and abroad. For this reason, we put forward the hypothesis, during the period of PIZ remodeling, the myocardial fibers arrange in disorder and extracellular volume increases, which can cause abnormal electric conduction and induce ventricular arrhythmia. And the abnormal structure of PIZ can be quantitative detected by CMR at the early phase. Therefore, we intend to take advantage of multimodal CMR techniques, such as diffusion tensor imaging, T1 mapping, tissue tracking, delayed contrast enhancement imaging and other sequences, to make a comprehensive evaluation of PIZ remodeling, including the arrangement of myocardial fibers, extracellular volume (ECV) quantification, tissue characteristics and so on. The electrophysiological indexes are served as the criteria to judge the potential risk of ventricular arrhythmia, and compared with Cx-43 expression, to explore the relationship between the quantitative indicators of PIZ remodeling and the occurrence risk of VA, so as to provide reliable basis for predicting the potential risk of VA in clinic.