ID | 117151 |
Title Alternative | 拡張した冪指数型ダイバージェンス測度を用いて動的に選択したスパースな投影ビューからのブロック反復再構成
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Author |
Ishikawa, Kazuki
Tokushima University
Yamaguchi, Yusaku
Shikoku Medical Center for Children and Adults, National Hospital Organization
Abou Al-Ola, Omar M.
Tanta University
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Keywords | power-divergence measure
computed tomography
iterative reconstruction
ordered-subsets algorithm
block-iterative reconstruction
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Content Type |
Thesis or Dissertation
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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.
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Journal Title |
Entropy
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ISSN | 10994300
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Publisher | MDPI
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Volume | 24
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Issue | 5
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Start Page | 740
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Published Date | 2022-05-23
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Remark | 内容要旨・審査要旨・論文本文の公開
本論文は,著者Kazuki Ishikawaの学位論文として提出され,学位審査・授与の対象となっている。 |
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) | |
URL ( Publisher's Version ) | |
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language |
eng
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TextVersion |
ETD
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MEXT report number | 甲第3772号
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Diploma Number | 甲保第62号
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Granted Date | 2024-02-21
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Degree Name |
Doctor of Health Science
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Grantor |
Tokushima University
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departments |
Medical Sciences
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