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