Abstract #1826

Severe MR Motion Artefact Correction with 2 step Deep Learning-based guidance

Julian Hossbach^1,2, Daniel Nicolas Splitthoff², Bryan Clifford³, Daniel Polak^4,5, Stephan Cauley⁵, and Andreas Maier¹

¹Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, ²Siemens Healthcare GmbH, Erlangen, Germany, ³Siemens Medical Solutions, Boston, MA, United States, ⁴Siemens Healthcare Gmbh, Erlangen, Germany, ⁵Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Motion CorrectionMotion artifacts can pose a difficult challenge in the clinical workflow. For addressing this issue, we here investigate the performance of two Deep Learning based motion mitigation strategies, MoPED and NAMER, and demonstrate that both approaches can readily be combined. This allows for the correction of severely corrupted images.

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