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Abstract #4785

Automated Time Adjusted Inversion Time Selection for Late Gadolinium Enhancement Imaging - Validation and Application on Patient Datasets

Seung Su Yoon1,2, Michaela Schmidt2, Manuela Rick2, Teodora Chitiboi3, Puneet Sharma3, Tilman Emrich4,5, Christoph Tillmanns6, Andreas Seitz7, Heiko Mahrholdt7, Solenn Toupin8, Théo Pezel9,10, Jérôme Garot9, 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, Germany, 6Diagnostikum Berlin, Berlin, Germany, 7Department of Cardiology, Robert Bosch Medical Center, Stuttgart, Germany, 8Siemens Healthcare France, Saint-Denis, France, 9Institut Cardiovasculaire Paris Sud, Cardiovascular Magnetic Resonance Laboratory, Hôpital Privé Jacques Cartier - Ramsay Santé, Massy, France, 10Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States


In cardiac MRI, the Late Gadolinium Enhancement technique is usually performed after inversion recovery scout sequences that are acquired to null the myocardial properly for optimal image contrast. In clinical practice, the selection and adjustment of the inversion time for healthy myocardium nulling are manually performed. To standardize and automate the process, we propose an automated deep-learning-based system combined with a linear regression model, which automates the selection of the inversion time, taking into consideration the time delay between the inversion time scout and LGE sequences. In this work, we validated the system in a large retrospective study (N=765).

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