Dual-domain unsupervised network for removing motion artifact related to Gadoxetic acid-enhanced MRI. Issue 1 (1st April 2022)

• Record Type: Journal Article
• Authors: Bai, Chongxin; Liu, Kewen; Chen, Shi; Li, Zhao; Xie, Weida; Bao, Qingjia; Liu, Chaoyang
• Is Part Of: Journal of physics. Volume 2258:Issue 1(2022)
• Publication Date: 2022-04-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2258/1/012037
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The transient severe motion may cause severe image degradation during gadoxetic acid-enhanced arterial phase imaging. This work proposes a new dual-domain unsupervised motion artifacts disentanglement network for motion correction related to gadoxetic acid-enhanced MRI. We assume that motion-free images and motion-corrupted images belong to the different domains, then the motion correction is converted to the image-to-image translation problem. The image-to-image translation within the same domain is designed to constrain autoencoders to learn the feature representation. And the cross-domain translation explores the cycle consistency in the absence of paired images. Experimental results demonstrate that our method can effectively reduce artifacts in the gadoxetic acid-enhanced images.