射血分数保留型心衰与心外膜脂肪组织相关性的影像学研究进展OA

Epicardial adipose tissue(EAT)is involved in the pathophysiology of heart failure with preserved ejection fraction(HFpEF)through mechanical compression and the secretion of bioactive substances.Currently,commonly used imaging modalities for evaluating EAT include echocardiography,computed tomography(CT),and cardiac magnetic resonance(CMR).Quantitative assessment of EAT can be achieved by measuring parameters such as thickness,volume,density,and texture entropy.With advances in artificial intelligence(AI),deep learning-based automated segmentation and feature mining provide new approaches for quantitative EAT analysis.This review summarizes the pathophysiological mechanisms by which EAT is involved in HFpEF,the current status of imaging techniques for quantitative EAT assessment,and research advances in the application of AI to EAT analysis.