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

TL;DR

This paper introduces a novel sinogram interpolation method for CT scans that leverages self-similarity and smoothness, significantly improving image quality from low-dose scans and reducing radiation exposure.

Contribution

The paper presents a new interpolation algorithm exploiting sinogram self-similarity and smoothness, enhancing low-dose CT image reconstruction.

Findings

Improved image quality from low-dose scans

Significant reduction in projection measurements needed

Effective in both simulated and real CT data

Abstract

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.