Few-view CT reconstruction with group-sparsity regularization

Abstract

Classical total variation-based iterative reconstruction algorithm is effective for the reconstruction of piecewise smooth image, but it causes oversmoothing effect for textured regions in the reconstructed image. To address this problem, this work presents a novel computed tomography reconstruction method for the few-view problem called the group-sparsity regularization-based simultaneous algebraic reconstruction technique (SART). Group-based sparse representation, which uses 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 oversmoothing 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, SART, total variation-based projections onto convex sets, and SART-based dictionary learning.

Keywords: computed tomography; few-view reconstruction; sparse representation; total variation.