  Metal implants can heavily attenuate X-rays in computed tomography (CT)
scans, leading to severe artifacts in reconstructed images, which significantly
jeopardize image quality and negatively impact subsequent diagnoses and
treatment planning. With the rapid development of deep learning in the field of
medical imaging, several network models have been proposed for metal artifact
reduction (MAR) in CT. Despite the encouraging results achieved by these
methods, there is still much room to further improve performance. In this
paper, a novel Dual-domain Adaptive-scaling Non-local network (DAN-Net) for
MAR. We correct the corrupted sinogram using adaptive scaling first to preserve
more tissue and bone details as a more informative input. Then, an end-to-end
dual-domain network is adopted to successively process the sinogram and its
corresponding reconstructed image generated by the analytical reconstruction
layer. In addition, to better suppress the existing artifacts and restrain the
potential secondary artifacts caused by inaccurate results of the
sinogram-domain network, a novel residual sinogram learning strategy and
nonlocal module are leveraged in the proposed network model. In the
experiments, the proposed DAN-Net demonstrates performance competitive with
several state-of-the-art MAR methods in both qualitative and quantitative
aspects.
