Abstract #0178

Automated detection and quantification of myocardial scar using AI-powered joint bright- and black-blood LGE imaging

Aurelien Bustin^1,2,3, Indra Ribal¹, Géraldine Montier², Jean-David Maes², Thibault Boullé², Victor de Villedon de Naide¹, Pauline Gut^1,3, Soumaya Sridi², Valery Ozenne¹, Marta Nuñez-Garcia¹, Maxime Sermesant¹, Michel Montaudon², Gäel Dournes², François Laurent², Pierre Jaïs^1,4, Matthias Stuber^1,3,5, and Hubert Cochet^1,2

¹IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux – INSERM U1045, Bordeaux, France, ²Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Bordeaux, France, ³Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁴Department of Cardiac Electrophysiology, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Bordeaux, France, ⁵CIBM Center for Biomedical Imaging, Lausanne, Switzerland

Synopsis

Keywords: Heart, Data ProcessingBright-blood late gadolinium-enhanced (LGE) imaging is the current clinical gold standard to assess myocardial scar. However, poor contrast at the blood-scar interface makes scar detection and quantification challenging. Bright- and black-blood LGE imaging technologies have recently enabled more accurate scar detection and localization and have shown promising results for scar quantification. Here we aim to introduce a framework for fully automated scar detection and quantification combining novel joint bright- and black-blood LGE imaging with artificial intelligence-powered analysis.

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