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Abstract #0452

Validation of a Deep Learning based Automated Myocardial Inversion Time Selection for Late Gadolinium Enhancement Imaging in a Prospective Study

Seung Su Yoon1,2, Michaela Schmidt2, Manuela Rick2, Teodora Chitiboi3, Puneet Sharma3, Tilman Emrich4,5, Christoph Tilmanns6, Ralph Waßmuth6, Jens Wetzl2, and Andreas Maier1
1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany, 3Siemens Medical Solutions USA, Inc., Princeton, NJ, United States, 4Department of Radiology, University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany, 5Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, United States, 6Diagnostikum Berlin, Berlin, Germany

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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