  Recently, both supervised and unsupervised deep learning methods have been
widely applied on the CT metal artifact reduction (MAR) task. Supervised
methods such as Dual Domain Network (Du-DoNet) work well on simulation data;
however, their performance on clinical data is limited due to domain gap.
Unsupervised methods are more generalized, but do not eliminate artifacts
completely through the sole processing on the image domain. To combine the
advantages of both MAR methods, we propose an unpaired dual-domain network
(U-DuDoNet) trained using unpaired data. Unlike the artifact disentanglement
network (ADN) that utilizes multiple encoders and decoders for disentangling
content from artifact, our U-DuDoNet directly models the artifact generation
process through additions in both sinogram and image domains, which is
theoretically justified by an additive property associated with metal artifact.
Our design includes a self-learned sinogram prior net, which provides guidance
for restoring the information in the sinogram domain, and cyclic constraints
for artifact reduction and addition on unpaired data. Extensive experiments on
simulation data and clinical images demonstrate that our novel framework
outperforms the state-of-the-art unpaired approaches.
