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Abstract #2210

A Robust Deep-Learning-based Automated Cardiac Resting Phase Detection: Validation in a Prospective Study

Seung Su Yoon1,2, Elisabeth Hoppe1, Michaela Schmidt2, Christoph Forman2, Teodora Chitiboi3, Puneet Sharma3, Christoph Tillmanns4, 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, 3Siemens Medical Solutions USA, Inc, Princeton, NJ, United States, 4Diagnostikum Berlin, Berlin, Germany

The detection of a window with the least motion, e.g. end-systolic or end-diastolic resting phases (RPs) within the cardiac cycle is necessary for data acquisition for static cardiac imaging. In the current workflow, it is manually performed on CINE images by visual inspection. To automate the workflow, we propose an improved Deep-Learning-based automated localized RP detection. While the first step is responsible to localize the anatomy of interest, the second is to quantify motion within the target, and third is to classify RPs. We validated the system with a prospective volunteer study and achieved accuracy in the range of 28ms.

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