Effect of Smoothing on Sparsity Prior CT Reconstruction

Abstract

We systematically evaluate the performance of smoothing on several state-of-the-art sparsity prior CT reconstruction algorithms. State-of-the-art algorithms have been implemented and their performance analyzed with and without applying different smoothing filters. Aiming for successful reconstruction from less number of projections, sparsity prior reconstruction algorithms are found to be useful in CT, provided that the signal reconstruction is performed in a compressed domain (i.e. gradient or wavelet domain). The subject matter of this work is the investigation of the reconstruction performance variation with the application of a smoothing filter prior sparsifying transform. Experiments on simulated and real medical images show that the performance of the reconstruction algorithms vary, and smoothing before the sparsifying transform ensures better reconstruction.