The fat that accumulates between the myocardium and the visceral pericardium is called epicardial adipose tissue (EAT). When volume is increased, the EAT can secrete chemicals that influence the development of coronary disease. Volumetric assessment of magnetic resonance imaging (MRI) can quantify EAT, but volume alone gives no information about its distribution across the myocardial surface. In this study, a three-dimensional (3D) modeling technique is developed and used to quantify the distribution of the EAT across the surface of the heart. Dixon MRI scans, which produce a registered 3D set of fat-only and water-only images, were acquired in 11 subjects for a study on exercise intervention. A previously developed segmentation algorithm was used to identify the heart and EAT in six of the scans. Contours were extracted from the labeled images and imported into NX 10, where 3D models of both surfaces were created. Procrustes analysis was used to register the heart models and create an average heart surface. An iterative closest point algorithm was used to register and align the EAT models for calculation of EAT thickness. Rays were cast in directions specified by a spherical parameterization of elevation and azimuthal angles, and intersections of the ray with the EAT surface were used to calculate the thickness at each location. The thickness maps were averaged and then “painted” onto the average heart model, creating a single, integrated model representing the average EAT thickness across the surface of the myocardium.