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.

本研究解决了无对比剂心肌瘢痕检测的难题，提出了一种新框架，将动态心电图中的心脏运动与图像纹理信息相结合，以分割左心室的心肌和瘢痕组织。实验结果表明，该方法在无对比剂的心动图像上实现了与传统的对比增强MRI相当的精度，展示了其作为心肌瘢痕检测替代方案的潜力。

无对比剂的心肌瘢痕分割：利用运动与纹理融合进行超声心动图像分析