Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion

2025-01-09

AI Summary

To address the clinical limitations of late gadolinium enhancement MRI (LGE-MRI)—including contrast-related risks, prolonged scan time, and patient discomfort—in post-myocardial infarction scar detection, this work proposes a fully automatic, contrast-free scar segmentation method. Methodologically, we introduce the first end-to-end deep learning framework that jointly exploits motion representations derived from intensity-based registration of full-cardiac-cycle cine MRI and multi-scale local texture features, integrated via a motion-texture cascaded fusion architecture. Our key innovation lies in substituting contrast-enhancement-derived signal differences with intrinsic cardiac motion dynamics for scar delineation, thereby circumventing LGE-MRI’s fundamental constraints. Evaluated on clinical data, the method achieves a Dice score of ≈0.86—comparable to conventional LGE-MRI—demonstrating its potential as a safe, efficient, and clinically viable alternative for scar quantification.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Late gadolinium enhancement MRI (LGE MRI) is the gold standard for the detection of myocardial scars for post myocardial infarction (MI). LGE MRI requires the injection of a contrast agent, which carries potential side effects and increases scanning time and patient discomfort. To address these issues, we propose a novel framework that combines cardiac motion observed in cine MRI with image texture information to segment the myocardium and scar tissue in the left ventricle. Cardiac motion tracking can be formulated as a full cardiac image cycle registration problem, which can be solved via deep neural networks. Experimental results prove that the proposed method can achieve scar segmentation based on non-contrasted cine images with comparable accuracy to LGE MRI. This demonstrates its potential as an alternative to contrast-enhanced techniques for scar detection.

Problem

• Cardiac Scar Detection
• Cine MRI
• Late Gadolinium Enhancement MRI (LGE MRI)

Innovation

• Dynamic Cardiac Motion
• Texture Features
• Non-invasive Alternative for Myocardial Scar Detection