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Abstract #2211

Automated Deep-Learning-based Inversion Time Selection for Cardiac Late Gadolinium Enhancement Imaging

Seung Su Yoon1,2, Michaela Schmidt2, Bernd J Wintersperger3,4, Teodora Chitiboi5, Puneet Sharma5, Christoph Tillmanns6, Andreas Maier1, and Jens Wetzl2
1Department of Computer Science, Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany, 3Department of Medical Imaging, University Health Network, Toronto, ON, Canada, 4Department of Medical Imaging, University of Toronto, Toronto, ON, Canada, 5Siemens Medical Solutions USA, Inc, Princeton, NJ, United States, 6Diagnostikum Berlin, Berlin, Germany

In Cardiac MRI, Late gadolinium enhancement (LGE) imaging is generally performed for the assessment of myocardial viability. As LGE is based on inversion recovery techniques, the correct myocardial nulling is necessary for image contrast optimization. In current clinical practice, it is done by visual evaluation. As it required user expertise and interaction, an automated inversion time selection is proposed. The Deep-Learnig-based system to detect the null point of inversion time was successfully demonstrated in all datasets comparing with two expert annotations.

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