Work

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

Year: 2016

Type: conference-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.

Source: Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

DOI: 10.1117/12.2236788

Authors: Hiroyuki Kudo, Fukashi Yamazaki, Takuya Nemoto, Keita Takaki

Institution: University of Tsukuba

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

Award: Constructing Foundations of Super Compressed Sensing and its Applications to Various Tomographic Imaging Modalities