Abstract #2211

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

Seung Su Yoon^1,2, Michaela Schmidt², Bernd J Wintersperger^3,4, 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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