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arXiv · 1609.06041

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
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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 flter 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, Fukashi Yamazaki, Takuya Nemoto, Keita Takaki. 2016-09-20. A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT. https://doi.org/10.1117/12.2236788

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