Improving the management of cardiac complications in metabolic diseases, obesity and diabetes, is a major challenge for our society. The measurement of epicardial adipose tissue (EAT), a fat depot attached to the heart, is an emerging and promising diagnosis to identify patients at risk. We developed the automation of this measurement on routine MRI images by deep learning. Then, an innovative MRI technique was proposed to measure and characterize the EAT in 3D, combining: a free-breathing acquisition, an image reconstruction robust to cardio-respiratory motion and MRI imperfections, an optimized and validated fat characterization algorithm and the knowledge of the composition of ex-vivo EAT samples. Together, this allows for in vivo, non-invasive characterization of EAT, a novel diagnosis for cardiometabolic risk.