Motion artifact that appears due to patient movement in MRI scans is a major obstacle in pediatric imaging. Since acquiring paired motion-clean and motion-corrupted dataset is difficult, unsupervised deep learning such as Cycle Generative Adversarial Network (Cycle-GAN) has been used in motion correction tasks these days. In this study, we propose a rigid motion artifact reducing strategy with modified Cycle-GAN by replacing generator that converts motion-free to motion-corrupted domain with a motion simulator. Our proposed model outperforms in contrast preservation and reducing artifacts compared to the original Cycle-GAN as well as reduces training parameters.
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