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

TL;DR

This paper introduces a contrast-free method for myocardial scar segmentation in cine MRI by fusing motion and texture features, offering an alternative to contrast-enhanced imaging with comparable accuracy.

Contribution

A novel framework combining cardiac motion and texture in cine MRI for scar segmentation without contrast agents, utilizing deep neural networks for motion tracking.

Findings

Achieves scar segmentation accuracy comparable to LGE MRI

Eliminates need for contrast agents in myocardial scar detection

Demonstrates potential for safer, faster cardiac imaging

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.