Automated quantification of epicardial adipose tissue in cardiac magnetic resonance imaging.

Cardiovascular disease is one of the leading causes of death worldwide. Epicardial adipose tissue (EAT) has emerged as an independent predictor of high cardiometabolic risk. Cardiovascular MRI has proven to be a feasible and reproducible method to assess EAT quantitatively. We present a novel approach for the automated quantification of EAT using "a priori" anatomical information. We extracted a region of interest (ROI) in the end-diastolic heart phase followed by a GVF-snake algorithm to smooth it. For the EAT and endocardial boundary detection, a Law's texture filter is applied. Left and right ventricle are localized using spatial prior information. Then, thresholding is applied to quantify the cardiac muscle. For the EAT, it is differentiated from the paracardial fat by K-cosine curvature analysis. Results for 10 morbidly obese patients show no significant differences between manual and automatic quantification with a remarkable time and effort saving between them.