  During X-ray computed tomography (CT) scanning, metallic implants carrying
with patients often lead to adverse artifacts in the captured CT images and
then impair the clinical treatment. Against this metal artifact reduction (MAR)
task, the existing deep-learning-based methods have gained promising
reconstruction performance. Nevertheless, there is still some room for further
improvement of MAR performance and generalization ability, since some important
prior knowledge underlying this specific task has not been fully exploited.
Hereby, in this paper, we carefully analyze the characteristics of metal
artifacts and propose an orientation-shared convolution representation strategy
to adapt the physical prior structures of artifacts, i.e., rotationally
symmetrical streaking patterns. The proposed method rationally adopts
Fourier-series-expansion-based filter parametrization in artifact modeling,
which can better separate artifacts from anatomical tissues and boost the model
generalizability. Comprehensive experiments executed on synthesized and
clinical datasets show the superiority of our method in detail preservation
beyond the current representative MAR methods. Code will be available at
\url{https://github.com/hongwang01/OSCNet}
