Deep-Learning-based Quantification of Epicardial Adipose Tissue by 3D Dixon Cardiovascular Magnetic Resonance

Background: Epicardial adipose tissue (EAT) is a metabolically active fat depot adjacent to the myocardium and the coronary arteries that can be non-invasively assessed by cardiovascular magnetic resonance (CMR). Increased EAT volume quantified by CMR has been linked to adverse cardiac remodeling, atrial fibrillation, coronary artery disease, and heart failure. Among CMR techniques, isotropic three-dimensional (3D) Dixon imaging at 1.3 x 1.3 x 1.3 mm3 resolution was developed to improve tissue characterization, providing fat-water signal separation for precise volumetric EAT assessment. However, manual segmentation of 3D datasets is highly time-consuming. For integration into clinical and research CMR workflows, reliable and fast automated segmentation is needed. Purpose: To develop and evaluate an automated deep-learning-based pipeline for ventricular EAT quantification based on isotropic 3D Dixon CMR acquisitions. Methods: An nnU-Net model was trained on 165 3D Dixon CMR cases encompassing healthy individuals and patients with underlying cardiovascular disease. The model was trained using all four Dixon phase images (opposed-phase, in-phase, fat-phase, water-phase). Manual 3D ventricular EAT segmentations served as the ground truth for training and evaluation. Performance was evaluated in 30 independent cases using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), volumetric agreement, Pearson correlation, intraclass correlation (ICC), and Bland-Altman analysis. Model performance was benchmarked against interobserver and intraobserver variability. Results: Automated segmentation achieved a mean DSC of 0.896 {+/-} 0.039 and HD95 of 1.84 {+/-} 0.93 mm versus ground truth. Volumetric agreement with ground truth was high (r = 0.984, ICC = 0.988, p < 0.001; mean bias -0.70 mL, limits of agreement (LoA) [-10.31, 8.90] mL), exceeding interobserver agreement (bias -25.24 mL, LoA [-42.81, -7.66] mL) and comparable to intraobserver reproducibility (bias 2.72 mL, LoA [-8.73, 14.17] mL). Automated segmentation required less than one minute per case compared to 58.4 {+/-} 7.9 minutes for manual segmentation. Two of 30 cases (6.7%) required minor manual correction, both less than five minutes. Conclusion: Fully automated nnU-Net-based ventricular EAT segmentation from isotropic 3D Dixon CMR achieves accuracy comparable to intraobserver reproducibility while significantly reducing post-processing time. The approach may facilitate large-scale and longitudinal EAT quantification in CMR-based research workflows.