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Abstract #0864

Unsupervised Deep Learning using modified Cycle Generative Adversarial Network for rigid motion correction in pediatric brain MRI

Sewook Kim1, Seul Lee1, Jaeuk Yi1, Mohammed A. Al-Masni1, Sung-Min Gho2, Young Hun Choi3, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2GE Healthcare, Seoul, Korea, Republic of, 3Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of


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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