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Abstract #1358

Prospective motion assessment within multi-shot imaging using coil mixing of the data consistency error and deep learning

Julian Hossbach1,2,3, Daniel Nicolas Splitthoff3, Bryan Clifford4, Daniel Polak3, Stephan F. Cauley5, and Andreas Maier1
1Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, 2Erlangen Graduate School in Advanced Optical Technologies, Erlangen, Germany, 3Siemens Healthcare GmbH, Erlangen, Germany, 4Siemens Medical Solutions, Boston, MA, United States, 5Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States

In this work we investigate the effect of motion on the data consistency error coil-mixing matrix, obtained by singular value decomposition. More specifically, a Neural Network is trained to translate motion induced deviations of this coil-mixing matrix relative to a reference acquisition into a motion score. This score can be used for the prospective detection of the most corrupted echo trains for removal or triggering a replacement by reacquisition. We show that a selective removal/replacement using the prospective motion score increases the image quality.

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