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ID 117151
Author
Ishikawa, Kazuki Tokushima University
Yamaguchi, Yusaku Shikoku Medical Center for Children and Adults, National Hospital Organization
Abou Al-Ola, Omar M. Tanta University
Keywords
power-divergence measure
computed tomography
iterative reconstruction
ordered-subsets algorithm
block-iterative reconstruction
Content Type
Journal Article
Description
Iterative reconstruction of density pixel images from measured projections in computed tomography has attracted considerable attention. The ordered-subsets algorithm is an acceleration scheme that uses subsets of projections in a previously decided order. Several methods have been proposed to improve the convergence rate by permuting the order of the projections. However, they do not incorporate object information, such as shape, into the selection process. We propose a block-iterative reconstruction from sparse projection views with the dynamic selection of subsets based on an estimating function constructed by an extended power-divergence measure for decreasing the objective function as much as possible. We give a unified proposition for the inequality related to the difference between objective functions caused by one iteration as the theoretical basis of the proposed optimization strategy. Through the theory and numerical experiments, we show that nonuniform and sparse use of projection views leads to a reconstruction of higher-quality images and that an ordered subset is not the most effective for block-iterative reconstruction. The two-parameter class of extended power-divergence measures is the key to estimating an effective decrease in the objective function and plays a significant role in constructing a robust algorithm against noise.
Journal Title
Entropy
ISSN
10994300
Publisher
MDPI
Volume
24
Issue
5
Start Page
740
Published Date
2022-05-23
Rights
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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DOI (Published Version)
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language
eng
TextVersion
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departments
Medical Sciences