〔Abstract〕 Objective To evaluate the predictive value of epicardial adipose tissue (EAT) parameters for microvascular obstruction (MVO) formation in patients with ST segment elevation myocardial infarction (STEMI) using cardiac magnetic resonance quantification. MethodsA total of 139 STEMI patients were included in this study, and various parameters such as EAT thickness, volume, and mass index were measured utilizing cardiac magnetic resonance. All included patients were divided into MVO group and non-MVO group according to whether MVO occurred. Differences in EAT related parameters between two groups were compared and correlation analysis was applied to evaluate the correlation between quantitative indicators of EAT and indicators such as infarct size and ejection fraction. Logistic regression analysis was used to identify the relevant risk factors for MVO formation. Receiver Operating Characteristic (ROC) curve analysis was performed to assess the predictive value of the epicardial adipose tissue (EAT) quality index and other indicators for the occurrence of MVO. ResultsCompared with non MVO group, patients in MVO group presented with higher peak troponin T levels, increase of neutrophil lymphocyte ratio (NLR) and C-reactive protein (CRP), larger infarct size and compromised left ventricular ejection fraction (LVEF%) ( P<0.05). Total EAT volume, EAT mass index, left atrioventricular EAT volume, left atrioventricular EAT mass index and thickness of EAT in the left atrioventricular groove were significantly higher in patients with MVO. Multivariate logistic regression analysis demonstrated that NLR, peak troponin T levels and left atrioventricular EAT mass index were independent predictors of MVO. The ROC curve suggested that the left atrioventricular EAT mass index had the highest predictive power for MVO formation in STEMI patients. Conclusion The parameters of EAT quantified by cardiac magnetic resonance serve as imaging biomarkers for predicting MVO formation in STEMI patients. These metrics enable risk stratification post-myocardial infarction and facilitate early identification of high-risk individuals, thereby supporting personalized therapeutic decision-making.