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Abstract #1175

Correction of motion artifacts using a multi-resolution fully convolutional neural network

Karsten Sommer1, Tom Brosch1, Rafael Wiemker1, Tim Harder1, Axel Saalbach1, Christopher S. Hall2, and Jalal B. Andre3

1Philips GmbH Innovative Technologies, Hamburg, Germany, 2Philips Radiology Solutions, Seattle, WA, United States, 3Department of Radiology, University of Washington, Seattle, WA, United States

Motion artifacts are a frequent source of image degradation in clinical practice. Here we demonstrate the feasibility of correcting motion artifacts in magnitude-only MR images using a multi-resolution fully convolutional neural network. Training and testing datasets were generated using artificially created artifacts introduced onto in vivo clinical brain scans. Both the corrupted input and filtered output images were rated by an experienced neuroradiologist.

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