Ring-Artifact Correction With Total-Variation Regularization for Material Images in Photon-Counting CT

Abstract

We propose a ring-artifact correction method with a compressed sensing for material images obtained with a photon-counting computed tomography (CT) system. The ring-artifacts are caused by nonuniformity of detector properties. Conventional ring-artifact correction methods tend to degrade the quality of images. In contrast, compressed sensing methods can correct ring-artifacts with less degradation of the image quality owing to a priori knowledge that ring-artifacts appeared as stripes in sinograms. In this study, we extend the compressed sensing methods for material sinograms obtained with a photon-counting CT system. This is because material sinograms tend to be simpler and sparser, for which a compressed-sensing method can be more effective. We introduced a cost function with a total variation-regularization term and positivity constraint, and optimized it with a prime-dual splitting method. The feasibility of this method was confirmed by simulations and an experiment. In both the simulations and experiment, the proposed method better corrected the ring artifacts than those on attenuation domain and without a priori knowledge. The comparison with previous methods in literature also showed the same results. These results suggest that our method is effective for correcting ring-artifacts in material images.