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 CT system. The ring-artifacts are caused by non-uniformity 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 TV-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. 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. Index Terms-image analysis, x-ray CT devices, photoncounting CT, ring artifacts, total variation, material decomposition. I. INTRODUCTION P HOTON-counting computed tomography (CT) is an emergence technology which measures X-ray intensity with multi-energy bands [1]. It provides many advantages compared with a conventional CT system. For example, it enables us to produce high-contrast images with appropriate energy weighting [2], to use multiple contrast-enhanced agents [3-6] and to perform a material-decomposition [7-11]. However, the material-decomposition is sensitive to quantum noise, uncertainty in sensitivity calibration, threshold variation, and other non-linear uncertainties such as pulse-pileup and charge-sharing effects. These uncertainties lead to ringartifacts in a reconstructed image [12]. Many methods of ring-artifact correction have been proposed. These can be classified into two categories: preprocessing and post-processing approaches [13,14]. The preprocessing approaches remove stripe-artifacts in sinograms before reconstructing images [15-17]. They can easily be combined with other projection-domain methods and any Manuscript

MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography

“…It utilized the high signal-to-noise ratio (high-SNR) reconstructed CT images as a reference to construct non-local TV as a regularization term achieving impressive results. The similarity between the reconstructed CT images and material maps has also been applied in ring-artifact correction ( 33 ) and obtained promising results. Additionally, considering clinical application, convergence speed is an important research direction.…”