Spatial phenotyping of epicardial adipose tissue from cardiac MRI.

Epicardial adipose tissue (EAT) is increasingly recognized as an important contributor to cardiovascular disease (CVD), but its spatial distribution across the heart remains insufficiently characterized due to limitations in existing segmentation and analysis methods. In this study, we developed a deep learning-based framework that integrates automated cardiac MRI segmentation with spatial statistical modeling to enable quantitative, chamber-resolved characterization of EAT distribution. An asymmetric multi-modal CNN-Transformer network was developed to segment the four cardiac chambers and EAT from cardiac MRI. Based on the resulting whole-heart segmentations, EAT was automatically partitioned into four chamber-specific subregions, voxel-wise thickness maps were reconstructed, and spatial statistics were applied to identify localized clustering patterns of EAT. Chamber-resolved and region-specific quantitative features were extracted to characterize the spatial distribution of EAT across the heart. The proposed approach was evaluated on a cohort including individuals with type 2 diabetes (T2D) and matched controls, revealing distinct T2D-associated remodeling, including increased chamber-specific burden, localized thickening, and spatial hotspot clustering in metabolically vulnerable regions. This work introduces a scalable and automated method for regional EAT phenotyping and provides spatially resolved imaging biomarkers that may support CVD research and risk stratification.