The statistical sinogram smoothing via adaptive-weighted total variation regularization for low-dose X-ray CT

Though clinically desired, low-dose X-ray computed tomography (CT) images tend to be degraded by the noise-contaminated sinogram data. Preprocessing the noisy sinogram before filtered back-projection (FBP) is an effective way to solve this problem. This paper presents a statistical sinogram smoothing approach for low-dose CT reconstruction. The approach is obtained by minimizing an energy function consisting of an adaptive-weighted total variation (AWTV) regularization term and a data fidelity term based on the Markov random fields (MRF) framework. The AWTV regularization term can make our algorithm automatically adjust the smoothing degree according to the feature and the level of noise of the smoothed pixel. The experimental results indicate that the proposed approach has the excellent performance in visual effects and quantitative analysis.