Computed Tomography Sinogram Inpainting With Compound Prior Modelling Both Sinogram and Image Sparsity

Abstract

The presence of metal objects remains a challenge in x-ray computed tomography (CT) imaging. Sinograms passing through metals, called metal trace, usually provide uncorrected information and are considered missing in CT image reconstruction. The sparse prior of an image in some appropriate transform domains, defined implicit sparsity, is often used in sinogram inpainting methods for metal trace recovery. However, conventional inpainting methods only employ the implicit sparsity of a sinogram and often result in several artifacts in the reconstructed images. In this paper, we propose a sinogram inpainting model with implicit sparsity exploitation for both sinogram and image. A newly added regularization term, which minimizes the sparse representation of image objects, is utilized to reduce unwanted artifacts and preserve fine structures. To solve the proposed model, we then present an efficient iterative algorithm based on the Chambolle-Pock optimization approach. The results for both digital phantom and actual CT data indicate that the new inpainting method exhibits reasonable performance and outperforms the conventional methods when applied to metal artifact reduction problems.