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Metal artefacts in CT images may disrupt image quality and interfere withdiagnosis. Recently many deep-learning-based CT metal artefact reduction (MAR)methods have been proposed. Current deep MAR methods may be troubled withdomain gap problem, where methods trained on simulated data cannot perform wellon practical data. In this work, we experimentally investigate two image-domainsupervised methods, two dual-domain supervised methods and two image-domainunsupervised methods on a dental dataset and a torso dataset, to explorewhether domain gap problem exists or is overcome. We find that I-DL-MAR andDudoNet are effective for practical data of the torso dataset, indicating thedomain gap problem is solved. However, none of the investigated methods performsatisfactorily on practical data of the dental dataset. Based on theexperimental results, we further analyze the causes of domain gap problem foreach method and dataset, which may be beneficial for improving existing methodsor designing new ones. The findings suggest that the domain gap problem in deepMAR methods remains to be addressed.

Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods