  Computed tomography (CT) has been widely used for medical diagnosis,
assessment, and therapy planning and guidance. In reality, CT images may be
affected adversely in the presence of metallic objects, which could lead to
severe metal artifacts and influence clinical diagnosis or dose calculation in
radiation therapy. In this paper, we propose a generalizable framework for
metal artifact reduction (MAR) by simultaneously leveraging the advantages of
image domain and sinogram domain-based MAR techniques. We formulate our
framework as a sinogram completion problem and train a neural network (SinoNet)
to restore the metal-affected projections. To improve the continuity of the
completed projections at the boundary of metal trace and thus alleviate new
artifacts in the reconstructed CT images, we train another neural network
(PriorNet) to generate a good prior image to guide sinogram learning, and
further design a novel residual sinogram learning strategy to effectively
utilize the prior image information for better sinogram completion. The two
networks are jointly trained in an end-to-end fashion with a differentiable
forward projection (FP) operation so that the prior image generation and deep
sinogram completion procedures can benefit from each other. Finally, the
artifact-reduced CT images are reconstructed using the filtered backward
projection (FBP) from the completed sinogram. Extensive experiments on
simulated and real artifacts data demonstrate that our method produces superior
artifact-reduced results while preserving the anatomical structures and
outperforms other MAR methods.
