Abstract #1358

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

Julian Hossbach^1,2,3, 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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