Keywords: Myocardium, Machine Learning/Artificial Intelligence
Motivation: Although late Gadolinium Enhancement (LGE) imaging is widely used for diagnosing myocardial infarction (MI), contrast-free approaches are in need for patients with gadolinium contraindications.
Goal(s): To develop Cine Generated Enhancement (CGE), a novel technique that uses contrast-free cine images to predict images resembling LGE.
Approach: A deep generative model was trained to translate cine images into LGE images of acute MI exploiting the different motion dynamics between heathy and infarcted myocardium.
Results: Realistic enhancement images can be generated for acute MI patients using cine images unseen during training. The scar size and transmurality estimated with CGE agreed well with LGE.
Impact: This study presents an effective, non-invasive, and contrast-free method for predicting LGE in acute MI, potentially reducing the use of gadolinium-based contrast agents and shortening cardiac MR examinations.
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