Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling

TL;DR

This paper introduces a novel deep learning framework that reconstructs detailed 3D myocardial infarct geometry from standard cine MRI without contrast agents, leveraging cardiac motion modeling for improved accuracy.

Contribution

It presents a new automated method combining 4D mesh reconstruction and motion-aware infarct localization from cine MRI, eliminating the need for contrast-enhanced imaging.

Findings

High agreement with manual infarct delineation

Effective contrast-free infarct reconstruction

Potential for patient-specific cardiac modeling

Abstract

Accurate representation of myocardial infarct geometry is crucial for patient-specific cardiac modeling in MI patients. While Late gadolinium enhancement (LGE) MRI is the clinical gold standard for infarct detection, it requires contrast agents, introducing side effects and patient discomfort. Moreover, infarct reconstruction from LGE often relies on sparsely sampled 2D slices, limiting spatial resolution and accuracy. In this work, we propose a novel framework for automatically reconstructing high-fidelity 3D myocardial infarct geometry from 2D clinically standard cine MRI, eliminating the need for contrast agents. Specifically, we first reconstruct the 4D biventricular mesh from multi-view cine MRIs via an automatic deep shape fitting model, biv-me. Then, we design a infarction reconstruction model, CMotion2Infarct-Net, to explicitly utilize the motion patterns within this dynamic geometry to localize infarct regions. Evaluated on 205 cine MRI scans from 126 MI patients, our method shows reasonable agreement with manual delineation. This study demonstrates the feasibility of contrast-free, cardiac motion-driven 3D infarct reconstruction, paving the way for efficient digital twin of MI.