  Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously
challenging task because the artifacts are structured and non-local in the
image domain. However, they are inherently local in the sinogram domain. Thus,
one possible approach to MAR is to exploit the latter characteristic by
learning to reduce artifacts in the sinogram. However, if we directly treat the
metal-affected regions in sinogram as missing and replace them with the
surrogate data generated by a neural network, the artifact-reduced CT images
tend to be over-smoothed and distorted since fine-grained details within the
metal-affected regions are completely ignored. In this work, we provide
analytical investigation to the issue and propose to address the problem by (1)
retaining the metal-affected regions in sinogram and (2) replacing the
binarized metal trace with the metal mask projection such that the geometry
information of metal implants is encoded. Extensive experiments on simulated
datasets and expert evaluations on clinical images demonstrate that our novel
network yields anatomically more precise artifact-reduced images than the
state-of-the-art approaches, especially when metallic objects are large.
