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Proceedings Paper

A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT
Author(s): Hiroyuki Kudo; Fukashi Yamazaki; Takuya Nemoto; Keita Takaki
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Paper Abstract

This paper concerns iterative reconstruction for low-dose and few-view CT by minimizing a data-fidelity term regularized with the Total Variation (TV) penalty. We propose a very fast iterative algorithm to solve this problem. The algorithm derivation is outlined as follows. First, the original minimization problem is reformulated into the saddle point (primal-dual) problem by using the Lagrangian duality, to which we apply the first-order primal-dual iterative methods. Second, we precondition the iteration formula using the ramp filter of Filtered Backprojection (FBP) reconstruction algorithm in such a way that the problem solution is not altered. The resulting algorithm resembles the structure of so-called iterative FBP algorithm, and it converges to the exact minimizer of cost function very fast.

Paper Details

Date Published: 3 October 2016
PDF: 16 pages
Proc. SPIE 9967, Developments in X-Ray Tomography X, 996711 (3 October 2016); doi: 10.1117/12.2236788
Show Author Affiliations
Hiroyuki Kudo, Univ. of Tsukuba (Japan)
JST-ERATO, Momose Quantum-Beam Phase Imaging (Japan)
Fukashi Yamazaki, Univ. of Tsukuba (Japan)
Takuya Nemoto, Univ. of Tsukuba (Japan)
Keita Takaki, Univ. of Tsukuba (Japan)

Published in SPIE Proceedings Vol. 9967:
Developments in X-Ray Tomography X
Stuart R. Stock; Bert Müller; Ge Wang, Editor(s)

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