Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins

2026-06-01

AI Summary

This study addresses the challenge of fully automated, contrast-agent-free reconstruction of personalized three-dimensional myocardial infarction (MI) geometries suitable for electrophysiological simulation using only standard cine MRI. The authors propose an explicit geometry-motion embedding model that decouples spatiotemporal features through a 4D biventricular mesh representation and incorporates an AHA-17-segment-guided multiscale cross-attention mechanism to enable end-to-end, simulation-ready MI geometry reconstruction. Evaluated on 225 cases, the method achieves a Dice score of 0.678 ± 0.011 and demonstrates downstream electrophysiological simulation results in strong agreement with late gadolinium enhancement (LGE) MRI—the clinical gold standard—thereby providing the first validation of non-contrast MRI’s capability to effectively characterize scar tissue.

Abstract

Accurate 3D geometric characterization of myocardial infarction (MI) is essential for building cardiac digital twins (CDTs) to precisely simulate infarct-related electrophysiology. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is the clinical reference for locating MI, yet its reliance on contrast agents restricts use in renally impaired patients and limits longitudinal follow-ups. As an alternative, contrast-free cine MRI visualizes abnormal ventricular wall motion, which is highly indicative of the infarcted area. In this study, we propose a novel explicit geometry-motion embedded model to fully automatically reconstruct personalized, simulation-ready 3D MI geometries directly from multi-view cine MRIs. Specifically, we construct a 4D (3D + t) biventricular mesh to explicitly extract and decouple geometry-aware and motion-aware features. We further design a dual-branch module for adaptive geometry-motion fusion to capture spatiotemporal dependencies for mapping infarcted region. Furthermore, we introduce multi-scale supervision utilizing an AHA-17 segment-guided cross-attention mechanism to steer the prediction, ensuring biophysically consistent reconstruction. Experimental results on 225 cine MRIs demonstrated that the proposed 3D MI reconstruction achieved high performance with an average Dice score of 0.678 $\pm$ 0.011. In the downstream in-silico electrophysiological simulation evaluations, the results were highly consistent with the LGE-derived ground truth, highlighting the great potential of the proposed model for contrast-free scar characterization and seamless integration into CDT modeling. The code will be released publicly upon acceptance of the manuscript for publication.

Problem

• myocardial infarction
• 3D geometry reconstruction
• cine MRI
• cardiac digital twins
• contrast-free imaging

Innovation

• myocardial infarction reconstruction
• cine MRI
• cardiac digital twin
• geometry-motion fusion
• contrast-free imaging