Few-View CT Reconstruction with Group-Sparsity Regularization

TL;DR

This paper introduces GSR-SART, a novel CT reconstruction method for few-view scenarios that uses group-sparsity regularization to better preserve image details and reduce over-smoothing, outperforming existing methods.

Contribution

The paper proposes a new group-sparsity regularization approach integrated with SART for improved few-view CT reconstruction, addressing over-smoothing issues of traditional TV methods.

Findings

Outperforms existing methods in qualitative assessments

Achieves higher quantitative accuracy

Reduces over-smoothing effects

Abstract

Classical total variation (TV) based iterative reconstruction algorithms assume that the signal is piecewise smooth, which causes reconstruction results to suffer from the over-smoothing effect. To address this problem, this work presents a novel computed tomography (CT) reconstruction method for the few-view problem called the group-sparsity regularization-based simultaneous algebraic reconstruction technique (GSR-SART). Group-based sparse representation, which utilizes the concept of a group as the basic unit of sparse representation instead of a patch, is introduced as the image domain prior regularization term to eliminate the over-smoothing effect. By grouping the nonlocal patches into different clusters with similarity measured by Euclidean distance, the sparsity and nonlocal similarity in a single image are simultaneously explored. The split Bregman iteration algorithm is applied to obtain the numerical scheme. Experimental results demonstrate that our method both qualitatively and quantitatively outperforms several existing reconstruction methods, including filtered back projection, expectation maximization, SART, and TV-based projections onto convex sets.