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Abstract #4185

Dual-domain self-supervised network for removing motion artifact related to Gadoxetic acid-enhanced MRI

Qingjia Bao1, Feng Pan2, Chongxin Bai3, Kewen Liu3, Zhao Li1, Peng Sun4, Jiazheng Wang4, Linkun Zhong5, Aodong Xiao6, Lian Yang2, and Chaoyang Liu1
1State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Phys, Wuhan, China, 2Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 3Wuhan University of Technology School of Information Engineering, Wuhan, China, 4Philips Healthcare, Beijing, China, 5Wuhan University of Arts and Science, Wuhan, China, 6Henan University of Science and Technology, Luoyang, China


We proposed a dual-domain self-supervised motion artifacts disentanglement network (DSMAD-Net) for the liver's gadoxetic acid-enhanced arterial phase images. The motion correction is converted to the image-to-image translation problem by assuming that motion-free images and motion-corrupted images belong to different domains. Specifically, image-to-image translation within the same domain is designed to constrain auto-encoders to learn the feature representation by utilizing the input images as supervision information. Moreover, the cross-domain translation explores the cycle consistency in the absence of paired motion-free and motion-corrupted images. Experimental results demonstrate that our method remarkably removes artifacts in the gadoxetic acid-enhanced arterial phase images.

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