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Abstract #0863

MoDGAN: Unsupervised rigid motion detection and correction with generative adversarial networks

Mu-Yul Park1, Seul Lee1, Kyu-Jin Jung1, Jisu Yun1, and Dong-Hyun Kim1
1Department of Electrical and Electronic, Yonsei university, Seoul, Korea, Republic of


Rigid motion artifacts, which are caused by subject’s motion during MRI acquisition, are a significant problem of image degradation. In this study, we propose a GAN-based method for unsupervised rigid motion detection and correction. The proposed method detects the motion-corrupted phase encoding lines using an anomaly score for motion detection. To reduce motion artifacts, we replace them with corresponding lines that generator yielded. We show that proposed method achieves high accuracy in detecting motion-corrupted line without any hardware or modification in sequence. Furthermore, the results of motion correction also showed noticeable performance.

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