3 October 2016 A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT
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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.
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Hiroyuki Kudo, Hiroyuki Kudo, Fukashi Yamazaki, Fukashi Yamazaki, Takuya Nemoto, Takuya Nemoto, Keita Takaki, 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); doi: 10.1117/12.2236788; https://doi.org/10.1117/12.2236788
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