This paper proposes a new image reconstruction algorithm in sparse-view CT using the so-called nonlocal Total Variation(nonlocal TV) regularization. Compared to the previous work using the nonlocal TV, the proposed algorithm possessesthe following three features. First, we introduce the newly developed modified nonlocal TV regularization term to preservesmooth intensity changes. Second, we utilize Passty’s proximal splitting framework to construct an accelerated iterativealgorithm to minimize the cost function. Third, we introduce a novel technique called Selective Artifact Reduction (SAR)for further reduction of streak artifacts during the iteration. We demonstrate that the proposed algorithm can achievesignificant image quality from 50-100 projection data with less than 20 iterations, through simulation studies using aclinical abdominal CT image.
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