A CT Reconstruction Algorithm Based on Contourlet Transform and Split Bregman Method

Gradient operator has some shortages in sparse representation in Computed Tomography (CT) image reconstruction algorithms, while Contourlet transform can represent image better because it contains direction information of images. Based on this, this paper introduce Contourlet transform to sparse CT image reconstruction. As a starting point, we propose an algorithm which combine Contourlet transform with Total Variation (TV) and then we use the Split Bregman method to solve the optimization problem. The experimental results show that the reconstructed results using the proposed algorithm with fewer projection can suppress noise effectively, and reduce the artifacts, which indicate that the proposed algorithm is more suitable with sparse sampling.