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As the medical usage of computed tomography (CT) continues to grow, theradiation dose should remain at a low level to reduce the health risks.Therefore, there is an increasing need for algorithms that can reconstructhigh-quality images from low-dose scans. In this regard, most of the recentstudies have focused on iterative reconstruction algorithms, and littleattention has been paid to restoration of the projection measurements, i.e.,the sinogram. In this paper, we propose a novel sinogram interpolationalgorithm. The proposed algorithm exploits the self-similarity and smoothnessof the sinogram. Sinogram self-similarity is modeled in terms of the similarityof small blocks extracted from stacked projections. The smoothness is modeledvia second-order total variation. Experiments with simulated and real CT datashow that sinogram interpolation with the proposed algorithm leads to asubstantial improvement in the quality of the reconstructed image, especiallyon low-dose scans. The proposed method can result in a significant reduction inthe number of projection measurements. This will reduce the radiation dose andalso the amount of data that need to be stored or transmitted, if thereconstruction is to be performed in a remote site.

Interpolation of CT Projections by Exploiting Their Self-Similarity and Smoothness