| Item type |
文献 / Documents(1) |
| 公開日 |
2025-03-19 |
| 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/jimaging10080178 |
|
|
関連名称 |
10.3390/jimaging10080178 |
| 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 |
Iterative Tomographic Image Reconstruction Algorithm Based on Extended Power Divergence by Dynamic Parameter Tuning |
| 著者 |
Yabuki, Ryuto
Yamaguchi, Yusaku
Abou Al-Ola, Omar M.
兒島, 雄志
吉永, 哲哉
|
| 抄録 |
|
|
内容記述 |
Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques. |
| キーワード |
|
|
主題 |
extended power divergence |
| 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 |
optimization |
| キーワード |
|
|
主題 |
dynamic parameter tuning |
| 書誌情報 |
en : Journal of Imaging
巻 10,
号 8,
p. 178,
発行日 2024-07-23
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| 収録物ID |
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|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2313433X |
| 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 |
410046 |
| 言語 |
|
|
言語 |
eng |
jim_10_8_178.pdf (2.8 MB)
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