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Abstract #3360

MoCo Cycle-MedGAN: Unsupervised correction of rigid MR motion artifacts

Tobias Hepp1, Karim Armanious1,2, Aastha Tanwar2, Sherif Abdulatif2, Thomas Küstner1, Bin Yang2, and Sergios Gatidis1
1University Hospital Tübingen, Tübingen, Germany, 2University of Stuttgart, Stuttgart, Germany

Motion is one of the main sources for artifacts in magnetic resonance (MR) imaging and can affect the diagnostic quality of MR images significantly. Previously, supervised adversarial approaches have been suggested for the correction of MR motion artifacts. However,supervised approaches require paired and co-registered datasets for training, which are often hard or impossible to acquire. We introduced a new adversarial framework for the unsupervised correction of severe rigid motion artifacts in the brain region. Quantitative and qualitative comparisons with other supervised and unsupervised translation approaches showed the enhanced performance of the introduced framework.

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