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Abstract #2127

Automatic Analysis of Multicycle Real-time MRI for the Assessment of Variable Cardiac Function based on Multi-orientation U-net Segmentation

Anja Brigitte Ziva Hennemuth1,2, Jan-Martin Kuhnigk2, Michael Steinmetz3, Sebastian Ulrich Kelle4, Teodora Chitiboi5, Jens Frahm6, and Markus Huellebrand1,2

1Institute for Cardiovascular Computer-assisted Medicine, Charité - Universitaetsmedizin Berlin, Berlin, Germany, 2Fraunhofer MEVIS, Berlin, Germany, 3Universitaetsmedizin Goettingen, Goettingen, Germany, 4Deutsches Herzzentrum Berlin, Berlin, Germany, 5Siemens Healthineers, Princeton, NJ, United States, 6Max-Planck-Institut fuer biophysikalische Chemie, Goettingen, Germany

Real time MRI is a promising modality for the measurement or myocardial function without the need for breath-holding or ECG triggering. To enable the quantitative assessment of non-temporally aligned image slices representing multiple heartcycles we present an automatic image analysis approach based on a segmentation using the U-net convolutional neural network model. The comparison of segmentation masks with reference data show a very good DICE coefficient of 0.94. The comparison of quantitative results achieved based on the expert-corrected conventional segmentation shows promising results and suggests that further improvement can be achieved through parameter adaptation.

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