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Abstract #2131

Automatic Cardiac Resting Phase Detection for Static Cardiac Imaging Using Deep Neural Networks

Seung Su Yoon1, Elisabeth Hoppe1, Michaela Schmidt2, Christoph Forman2, Puneet Sharma3, Christoph Tillmanns4, Andreas Maier1, and Jens Wetzl2

1Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany, 3 Siemens Medical Solutions USA, Inc., Princeton, NJ, United States, 4Diagnostikum Berlin, Berlin, Germany

To perform static cardiac imaging, manual inspection of CINE images is currently necessary to detect a quiescent window within the cardiac cycle. We propose an automated system using two chained Deep Neural Networks to determine localized end-systolic and end-diastolic resting phases. The first network finds a region of interest (e.g. RCA, right or left atrium) and the second determines a quantitative motion curve for this region. Training and evaluation was performed on data from volunteers and patients acquired on different scanners and field strengths and a comparison to manually annotated resting phases showed accuracy in the range of 35ms.

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