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Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
https://tokushima-u.repo.nii.ac.jp/records/2012763
https://tokushima-u.repo.nii.ac.jp/records/2012763e6b8eac5-5dd2-4313-9b33-b9b7ca8b9914
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
algo_17_11_512.pdf (2.8 MB)
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| アイテムタイプ | 文献 / Documents(1) | |||||||||||||||
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| 公開日 | 2025-03-18 | |||||||||||||||
| アクセス権 | ||||||||||||||||
| アクセス権 | open access | |||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||
| 資源タイプ | ||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||
| 資源タイプ | journal article | |||||||||||||||
| 出版社版DOI | ||||||||||||||||
| 関連識別子 | https://doi.org/10.3390/a17110512 | |||||||||||||||
| 関連名称 | 10.3390/a17110512 | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence | |||||||||||||||
| 著者 |
兒島, 雄志
× 兒島, 雄志× Yamaguchi, Yusaku
× Abou Al-Ola, Omar M.
× 吉永, 哲哉
WEKO
1587
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| 抄録 | ||||||||||||||||
| 内容記述 | In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required. | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | extended power-divergence measure | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | computed tomography | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | iterative reconstruction | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | expectation-maximization algorithm | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題 | optimization | |||||||||||||||
| 書誌情報 |
en : Algorithms 巻 17, 号 11, p. 512, 発行日 2024-11-07 |
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| 収録物ID | ||||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||||
| 収録物識別子 | 19994893 | |||||||||||||||
| 出版者 | ||||||||||||||||
| 出版者 | MDPI | |||||||||||||||
| 権利情報 | ||||||||||||||||
| 権利情報 | 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/). | |||||||||||||||
| EID | ||||||||||||||||
| 識別子 | 415405 | |||||||||||||||
| 言語 | ||||||||||||||||
| 言語 | eng | |||||||||||||||
