Think outside the box: Exploiting the imaging workflow for Deep Learning based motion estimation and correction
Julian Hossbach1,2, Daniel Nicolas Splitthoff3, Bryan Clifford4, Daniel Polak3,5, Wei-Ching Lo4, Stephan Cauley5, and Andreas Maier1
1Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, 2Erlangen Graduate School in Advanced Optical Technologies, Erlangen, Germany, 3Siemens Healthcare, Erlangen, Germany, 4Siemens Medical Solutions, Malden, MA, United States, 5Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
Estimating intrascan subject motion enables the reduction of motion artifacts but often requires further calibration data e.g. from an additional motion-free reference. In this work, we explore how the reuse of supplementary scans in the imaging workflow can be used as motion calibration data. More specifically, the preceding parallel imaging calibration scan is reutilized to support a Deep Learning (DL) approach for estimating motion. Results are presented which indicate that DL, in contrast to a conventional optimization approach, can extract the motion and improve the image quality despite contrast differences between the calibration and imaging scans.
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