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