Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm
The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because of its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although the good...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10320 |
| _version_ | 1827727303044497408 |
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| author | Xuru Li Xueqin Sun Fuzhong Li |
| author_facet | Xuru Li Xueqin Sun Fuzhong Li |
| author_sort | Xuru Li |
| collection | DOAJ |
| description | The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because of its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although the good results achieved by the prior image constrained compressed sensing (PICCS) method, there may be some unsatisfactory results in the reconstructed images because of the image gradient L<sub>1</sub>-norm used in the original PICCS model, which leads to the image suffering from step artifacts and over-smoothing of the edge as a result. To address the above-mentioned problem, this paper proposes a novel improved PICCS algorithm (NPICCS) for SVCT reconstruction. The proposed algorithm utilizes the advantages of PICCS, which could recover more details. Moreover, the algorithm introduces the L<sub>0</sub>-norm of image gradient regularization into the framework, which overcomes the disadvantage of conventional PICCS, and enhances the capability to retain edge and fine image detail. The split Bregman method has been used to resolve the proposed mathematical model. To verify the effectiveness of the proposed method, a large number of experiments with different angles are conducted. Final experimental results show that the proposed algorithm has advantages in edge preservation, noise suppression, and image detail recovery. |
| first_indexed | 2024-03-10T23:04:25Z |
| format | Article |
| id | doaj.art-6ed99c109d8f4524ada3c1b5f852196f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:04:25Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6ed99c109d8f4524ada3c1b5f852196f2023-11-19T09:26:07ZengMDPI AGApplied Sciences2076-34172023-09-0113181032010.3390/app131810320Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing AlgorithmXuru Li0Xueqin Sun1Fuzhong Li2School of Software, Shanxi Agricultural University, Taigu 030800, ChinaShanxi Key Laboratory of Signal Capturing and Processing, North University of China, Taiyuan 030051, ChinaSchool of Software, Shanxi Agricultural University, Taigu 030800, ChinaThe problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because of its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although the good results achieved by the prior image constrained compressed sensing (PICCS) method, there may be some unsatisfactory results in the reconstructed images because of the image gradient L<sub>1</sub>-norm used in the original PICCS model, which leads to the image suffering from step artifacts and over-smoothing of the edge as a result. To address the above-mentioned problem, this paper proposes a novel improved PICCS algorithm (NPICCS) for SVCT reconstruction. The proposed algorithm utilizes the advantages of PICCS, which could recover more details. Moreover, the algorithm introduces the L<sub>0</sub>-norm of image gradient regularization into the framework, which overcomes the disadvantage of conventional PICCS, and enhances the capability to retain edge and fine image detail. The split Bregman method has been used to resolve the proposed mathematical model. To verify the effectiveness of the proposed method, a large number of experiments with different angles are conducted. Final experimental results show that the proposed algorithm has advantages in edge preservation, noise suppression, and image detail recovery.https://www.mdpi.com/2076-3417/13/18/10320computed tomography (CT)sparse-view reconstructionprior image constrained compressed sensingimage gradient L<sub>0</sub>-norm |
| spellingShingle | Xuru Li Xueqin Sun Fuzhong Li Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm Applied Sciences computed tomography (CT) sparse-view reconstruction prior image constrained compressed sensing image gradient L<sub>0</sub>-norm |
| title | Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm |
| title_full | Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm |
| title_fullStr | Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm |
| title_full_unstemmed | Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm |
| title_short | Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm |
| title_sort | sparse view computed tomography reconstruction based on a novel improved prior image constrained compressed sensing algorithm |
| topic | computed tomography (CT) sparse-view reconstruction prior image constrained compressed sensing image gradient L<sub>0</sub>-norm |
| url | https://www.mdpi.com/2076-3417/13/18/10320 |
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