In cardiac MRI using the Late Gadolinium Enhancement technique, inversion recovery sequences are acquired for the correct myocardial nulling for optimal image contrast. In clinical practice, the selection of the proper inversion time to null healthy myocardium is manually performed by visual inspection. To standardize the process, we propose an automated deep-learning-based system which selects the “null inversion time” where the myocardium signal is darkest, and “contrast inversion time” where the contrast between the myocardium and blood pool is highest. We validated the system on a prospective study on different scanners. The system achieved high accuracy in observers’ annotation range.
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