A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT

Hiroyuki Kudo; Fukashi Yamazaki; Takuya Nemoto; Keita Takaki

doi:10.1117/12.2236788

3 October 2016 A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT

Hiroyuki Kudo, Fukashi Yamazaki, Takuya Nemoto, Keita Takaki

Author Affiliations +

Proceedings Volume 9967, Developments in X-Ray Tomography X; 996711 (2016) https://doi.org/10.1117/12.2236788
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Hiroyuki Kudo, Fukashi Yamazaki, Takuya Nemoto, and Keita Takaki "A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT", Proc. SPIE 9967, Developments in X-Ray Tomography X, 996711 (3 October 2016); https://doi.org/10.1117/12.2236788

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