From Event: SPIE Optical Engineering + Applications, 2016
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.
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 (Presented at SPIE Optical Engineering + Applications: August 30, 2016; Published: 3 October 2016); https://doi.org/10.1117/12.2236788.
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