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Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure

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 improv...

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Autores principales: Ishikawa, Kazuki, Yamaguchi, Yusaku, Abou Al-Ola, Omar M., Kojima, Takeshi, Yoshinaga, Tetsuya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141439/
https://www.ncbi.nlm.nih.gov/pubmed/35626623
http://dx.doi.org/10.3390/e24050740
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author Ishikawa, Kazuki
Yamaguchi, Yusaku
Abou Al-Ola, Omar M.
Kojima, Takeshi
Yoshinaga, Tetsuya
author_facet Ishikawa, Kazuki
Yamaguchi, Yusaku
Abou Al-Ola, Omar M.
Kojima, Takeshi
Yoshinaga, Tetsuya
author_sort Ishikawa, Kazuki
collection PubMed
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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spelling pubmed-91414392022-05-28 Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure Ishikawa, Kazuki Yamaguchi, Yusaku Abou Al-Ola, Omar M. Kojima, Takeshi Yoshinaga, Tetsuya Entropy (Basel) Article 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. MDPI 2022-05-23 /pmc/articles/PMC9141439/ /pubmed/35626623 http://dx.doi.org/10.3390/e24050740 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. 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/).
spellingShingle Article
Ishikawa, Kazuki
Yamaguchi, Yusaku
Abou Al-Ola, Omar M.
Kojima, Takeshi
Yoshinaga, Tetsuya
Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure
title Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure
title_full Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure
title_fullStr Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure
title_full_unstemmed Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure
title_short Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure
title_sort block-iterative reconstruction from dynamically selected sparse projection views using extended power-divergence measure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141439/
https://www.ncbi.nlm.nih.gov/pubmed/35626623
http://dx.doi.org/10.3390/e24050740
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