Image reconstruction in sparse-view CT using improved nonlocal total variation regularization

Yongchae Kim; Hiroyuki Kudo; Kazuki Chigita; Songzhe Lian

doi:10.1117/12.2529164

10 September 2019 Image reconstruction in sparse-view CT using improved nonlocal total variation regularization

Yongchae Kim, Hiroyuki Kudo, Kazuki Chigita, Songzhe Lian

Proceedings Volume 11113, Developments in X-Ray Tomography XII; 111131K (2019) https://doi.org/10.1117/12.2529164
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

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 possesses the following three features. First, we introduce the newly developed modified nonlocal TV regularization term to preserve smooth intensity changes. Second, we utilize Passty’s proximal splitting framework to construct an accelerated iterative algorithm 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 achieve significant image quality from 50-100 projection data with less than 20 iterations, through simulation studies using a clinical abdominal CT image.

Citation Download Citation

Yongchae Kim, Hiroyuki Kudo, Kazuki Chigita, and Songzhe Lian "Image reconstruction in sparse-view CT using improved nonlocal total variation regularization", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131K (10 September 2019); https://doi.org/10.1117/12.2529164

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