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Abstract #2602

Retrospective Motion Artifact Reduction with CNN (MARC) combined with model-based artifact simulation for T2WI of the liver

Motohide Kawamura1, Daiki Tamada1, Tetsuya Wakayama2, Hiroshi Onishi1, and Utaroh Motosugi1
1Department of Radiology, University of Yamanashi, Yamanashi, Japan, 2MR Collaboration and Development, GE Healthcare, Tokyo, Japan

Motion artifact is a problem in abdominal imaging. Respiratory-triggering techniques are commonly used to suppress the motion artifact. However, it is not always perfect in clinical practice. Deep learning-based motion correction is an attractive solution. However, it requires pairs of images with and without motion artifacts, which is difficult in body MRI. Here, we propose a deep learning-based method to remove motion artifact using a simulation of artifacts based on a simple model for respiratory gating failure. Preliminary results showed that the proposed method can remove motion artifacts in respiratory-triggered FSE-T2WI of the liver, which were corrupted by irregular breathing.

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