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Abstract #2224

DeepECG: Towards 3-D Continuous Cardiac MRI without ECG-Gating - Deep Learning-based R-Wave Classification for Automated Cardiac Phase Binning

Elisabeth Hoppe1, Jens Wetzl2, Seung Su Yoon1, Manuel Schneider2, Bernhard Stimpel1, Alexander Preuhs1, and Andreas Maier1
1Department of Computer Science, Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare, Erlangen, Germany

For continuous cardiac CINE acquisitions, cardiac binning of the data is necessary, which is done either using ECG-gating or hand-crafted postprocessing methods. To overcome these limitations, we propose a deep learning classifier to detect R-waves from repeated 1-D superior-inferior projections of the imaged data. After training with R-wave positions from the ECG signal as ground-truth data, detection of R-waves is possible without additional ECG-gating or hand-crafted features and can be used for retrospective cardiac binning. Our first proof-of-concept achieves a high accuracy of over 91% on previously unseen cardiac CINE data.

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