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

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.