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

• Material Type: 博士論文
• Title: Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure
• Author/Editor: Ishikawa, Kazuki; Yamaguchi, Yusaku; Abou Al-Ola, Omar M.; Kojima, Takeshi; Yoshinaga, Tetsuya
• Author Heading: Ishikawa, Kazuki; Yamaguchi, Yusaku; Abou Al-Ola, Omar M.; Kojima, Takeshi; Yoshinaga, Tetsuya
• Publication, Distribution, etc.: MDPI
• Publication Date: 2022-05-23; 24 5
• Publication Date (W3CDTF): 2022-05-23
• Alternative Title: 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
• Degree grantor/type: 徳島大学
• Date Granted: 2024-02-21
• Date Granted (W3CDTF): 2024-02-21
• Dissertation Number: 甲第3772号
• Degree Type: 博士（保健学）
• Text Language Code: eng
• Subject Heading: power-divergence measure; computed tomography; iterative reconstruction; ordered-subsets algorithm; block-iterative reconstruction
• Target Audience: 一般
• Note (General): 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.
• Persistent ID (NDL): info:ndljp/pid/13587802; https://dl.ndl.go.jp/pid/13587802
• Collection: 障害者向け資料
• Collection (Materials For Handicapped People:1): テキストデータ
• Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• Acquisition Basis: 博士論文（自動収集）
• Date Accepted (W3CDTF): 2024-05-03T22:07:42+09:00
• Format (IMT): application/pdf
• Access Restrictions: 国立国会図書館内限定公開
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• Periodical Title (URI): http://repo.lib.tokushima-u.ac.jp/117151
• Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp