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ID 114383
Author
Yamaguchi, Yusaku Shikoku Medical Center for Children and Adults
Content Type
Journal Article
Description
Iterative reconstruction (IR) algorithms based on the principle of optimization are known for producing better reconstructed images in computed tomography. In this paper, we present an IR algorithm based on minimizing a symmetrized Kullback-Leibler divergence (SKLD) that is called Jeffreys’ J-divergence. The SKLD with iterative steps is guaranteed to decrease in convergence monotonically using a continuous dynamical method for consistent inverse problems. Specifically, we construct an autonomous differential equation for which the proposed iterative formula gives a first-order numerical discretization and demonstrate the stability of a desired solution using Lyapunov’s theorem. We describe a hybrid Euler method combined with additive and multiplicative calculus for constructing an effective and robust discretization method, thereby enabling us to obtain an approximate solution to the differential equation.We performed experiments and found that the IR algorithm derived from the hybrid discretization achieved high performance.
Journal Title
Mathematical Problems in Engineering
ISSN
1024123X
15635147
NCID
AA11947206
Publisher
Hindawi
Volume
2018
Start Page
8973131
Published Date
2018-07-17
Rights
Copyright © 2018 Ryosuke Kasai et al.This is an open access article distributed under the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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DOI (Published Version)
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language
eng
TextVersion
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departments
Medical Sciences