Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
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 include...
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MDPI AG
2024-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/512 |
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| author | Takeshi Kojima Yusaku Yamaguchi Omar M. Abou Al-Ola Tetsuya Yoshinaga |
| author_facet | Takeshi Kojima Yusaku Yamaguchi Omar M. Abou Al-Ola Tetsuya Yoshinaga |
| author_sort | Takeshi Kojima |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-60158e46f26f4a3ea37ca65dface2dd1 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-60158e46f26f4a3ea37ca65dface2dd12024-11-26T17:45:28ZengMDPI AGAlgorithms1999-48932024-11-01171151210.3390/a17110512Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power DivergenceTakeshi Kojima0Yusaku Yamaguchi1Omar M. Abou Al-Ola2Tetsuya Yoshinaga3Institute of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, JapanShikoku Medical Center for Children and Adults, National Hospital Organization, 2-1-1 Senyu, Zentsuji 765-8507, JapanFaculty of Science, Tanta University, El-Giesh St., Tanta 31527, EgyptInstitute of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8509, JapanIn 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.https://www.mdpi.com/1999-4893/17/11/512extended power-divergence measurecomputed tomographyiterative reconstructionexpectation-maximization algorithmoptimization |
| spellingShingle | Takeshi Kojima Yusaku Yamaguchi Omar M. Abou Al-Ola Tetsuya Yoshinaga Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence Algorithms extended power-divergence measure computed tomography iterative reconstruction expectation-maximization algorithm optimization |
| title | Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence |
| title_full | Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence |
| title_fullStr | Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence |
| title_full_unstemmed | Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence |
| title_short | Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence |
| title_sort | optimizing parameters for enhanced iterative image reconstruction using extended power divergence |
| topic | extended power-divergence measure computed tomography iterative reconstruction expectation-maximization algorithm optimization |
| url | https://www.mdpi.com/1999-4893/17/11/512 |
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