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Image reconstruction in sparse-view CT using improved nonlocal total variation regularization
Author(s): Yongchae Kim; Hiroyuki Kudo; Kazuki Chigita; Songzhe Lian
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Paper 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.

Paper Details

Date Published: 10 September 2019
PDF: 9 pages
Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131K (10 September 2019); doi: 10.1117/12.2529164
Show Author Affiliations
Yongchae Kim, Univ. of Tsukuba (Japan)
Hiroyuki Kudo, Univ. of Tsukuba (Japan)
Kazuki Chigita, Univ. of Tsukuba (Japan)
Songzhe Lian, Univ. of Tsukuba (Japan)


Published in SPIE Proceedings Vol. 11113:
Developments in X-Ray Tomography XII
Bert Müller; Ge Wang, Editor(s)

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