Abstract #4785

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

Seung Su Yoon^1,2, Michaela Schmidt², Manuela Rick², Teodora Chitiboi³, Puneet Sharma³, Tilman Emrich^4,5, Christoph Tillmanns⁶, Andreas Seitz⁷, Heiko Mahrholdt⁷, Solenn Toupin⁸, Théo Pezel^9,10, 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

Synopsis

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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