Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization
CT image reconstruction algorithm based on generalized total variation (TGV) can overcome the staircase effect of total variation (TV) regularization, thereby protecting the structural features of the reconstructed image transition region. Although the TGV reconstruction method is superior to the TV...
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| Format: | Article |
| Language: | English |
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Editorial Office of Computerized Tomography Theory and Application
2025-01-01
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| Series: | CT Lilun yu yingyong yanjiu |
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| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2023.170 |
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| author | Min JIANG Hongwei TAO Kai CHENG |
| author_facet | Min JIANG Hongwei TAO Kai CHENG |
| author_sort | Min JIANG |
| collection | DOAJ |
| description | CT image reconstruction algorithm based on generalized total variation (TGV) can overcome the staircase effect of total variation (TV) regularization, thereby protecting the structural features of the reconstructed image transition region. Although the TGV reconstruction method is superior to the TV reconstruction method, it still ignores the role of non-local self-similar prior information in restoring CT image details. To overcome the aforementioned limitations of TGV reconstruction method, we introduce a non-local TGV (NLTGV) regularization term and propose a sparse view CT reconstruction algorithm based on NLTGV regularization. The proposed method can not only utilize non-local variational information of different orders to protect image structural features but can also utilize non-local self-similarity to restore the details of the reconstructed image. Owing to the inclusion of dual non-smooth terms in the reconstruction model, solving it directly is difficult. Therefore, we proposed an optimization algorithm based on convex set projection, which decomposes the problem into several sub-problems to be solved. The simulation and experimental results show that the proposed NLTGV regularization reconstruction method can effectively improve the quality of reconstructed images compared with other variational reconstruction methods. |
| format | Article |
| id | doaj-art-6f87f097628f4965b15549b2917bb30f |
| institution | Kabale University |
| issn | 1004-4140 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj-art-6f87f097628f4965b15549b2917bb30f2025-01-21T09:14:43ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402025-01-0134112913910.15953/j.ctta.2023.1702023.170Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation RegularizationMin JIANG0Hongwei TAO1Kai CHENG2College of Computer and Communication Engineer, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaCollege of Computer and Communication Engineer, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaZhejiang Lab, Hangzhou 311121, ChinaCT image reconstruction algorithm based on generalized total variation (TGV) can overcome the staircase effect of total variation (TV) regularization, thereby protecting the structural features of the reconstructed image transition region. Although the TGV reconstruction method is superior to the TV reconstruction method, it still ignores the role of non-local self-similar prior information in restoring CT image details. To overcome the aforementioned limitations of TGV reconstruction method, we introduce a non-local TGV (NLTGV) regularization term and propose a sparse view CT reconstruction algorithm based on NLTGV regularization. The proposed method can not only utilize non-local variational information of different orders to protect image structural features but can also utilize non-local self-similarity to restore the details of the reconstructed image. Owing to the inclusion of dual non-smooth terms in the reconstruction model, solving it directly is difficult. Therefore, we proposed an optimization algorithm based on convex set projection, which decomposes the problem into several sub-problems to be solved. The simulation and experimental results show that the proposed NLTGV regularization reconstruction method can effectively improve the quality of reconstructed images compared with other variational reconstruction methods.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2023.170x-ray ctsparse view samplingnon-local generalized total variationprojection on convex setsplit bregman algorithm |
| spellingShingle | Min JIANG Hongwei TAO Kai CHENG Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization CT Lilun yu yingyong yanjiu x-ray ct sparse view sampling non-local generalized total variation projection on convex set split bregman algorithm |
| title | Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization |
| title_full | Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization |
| title_fullStr | Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization |
| title_full_unstemmed | Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization |
| title_short | Sparse View CT Reconstruction Algorithm Based on Non-Local Generalized Total Variation Regularization |
| title_sort | sparse view ct reconstruction algorithm based on non local generalized total variation regularization |
| topic | x-ray ct sparse view sampling non-local generalized total variation projection on convex set split bregman algorithm |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2023.170 |
| work_keys_str_mv | AT minjiang sparseviewctreconstructionalgorithmbasedonnonlocalgeneralizedtotalvariationregularization AT hongweitao sparseviewctreconstructionalgorithmbasedonnonlocalgeneralizedtotalvariationregularization AT kaicheng sparseviewctreconstructionalgorithmbasedonnonlocalgeneralizedtotalvariationregularization |