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

2025-07-20

AI Summary

To address the limitations of late gadolinium enhancement MRI—including its reliance on contrast agents, low spatial resolution, and 2D sampling leading to inaccurate infarct localization—this study proposes a contrast-free, fully automated 3D geometric reconstruction method for myocardial infarction. First, a biv-me deep shape-fitting model reconstructs a four-dimensional dynamic mesh of both ventricles from routine cine MRI sequences. Second, an explicit cardiac motion modeling framework is introduced: CMotion2Infarct-Net analyzes regional wall motion abnormalities to achieve precise identification and 3D localization of infarcted tissue. Validated on 126 patients (205 scans), the method demonstrates strong agreement with manual segmentations (Dice coefficient ≈ 0.78). This work significantly advances the clinical feasibility of contrast-free digital twin modeling of myocardial infarction.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Reconstruct 3D infarct geometry without contrast agents
• Improve spatial resolution beyond sparse 2D LGE MRI
• Utilize cardiac motion patterns for infarct localization

Innovation

• Reconstruct 4D biventricular mesh from cine MRI
• Utilize motion patterns to localize infarct regions
• Contrast-free 3D infarct reconstruction from cine MRI