  A conventional approach to computed tomography (CT) or cone beam CT (CBCT)
metal artifact reduction is to replace the X-ray projection data within the
metal trace with synthesized data. However, existing projection or sinogram
completion methods cannot always produce anatomically consistent information to
fill the metal trace, and thus, when the metallic implant is large, significant
secondary artifacts are often introduced. In this work, we propose to replace
metal artifact affected regions with anatomically consistent content through
joint projection-sinogram correction as well as adversarial learning. To handle
the metallic implants of diverse shapes and large sizes, we also propose a
novel mask pyramid network that enforces the mask information across the
network's encoding layers and a mask fusion loss that reduces early saturation
of adversarial training. Our experimental results show that the proposed
projection-sinogram correction designs are effective and our method recovers
information from the metal traces better than the state-of-the-art methods.
