Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction
Spectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/9/1/35 |
| _version_ | 1797491103407013888 |
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| author | Xuru Li Xueqin Sun Yanbo Zhang Jinxiao Pan Ping Chen |
| author_facet | Xuru Li Xueqin Sun Yanbo Zhang Jinxiao Pan Ping Chen |
| author_sort | Xuru Li |
| collection | DOAJ |
| description | Spectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy channel. Tensor dictionary learning (TDL)-based methods have achieved better performance, but usually lose image edge information and details, especially from an under-sampling dataset. To address this problem, this paper proposes a method termed TDL with an enhanced sparsity constraint for spectral CT reconstruction. The proposed algorithm inherits the superiority of TDL by exploring the correlation of spectral CT images. Moreover, the method designs a regularization using the <i>L</i><sub>0</sub>-norm of the image gradient to constrain images and the difference between images and a prior image in each energy channel simultaneously, further improving the ability to preserve edge information and subtle image details. The split-Bregman algorithm has been applied to address the proposed objective minimization model. Several numerical simulations and realistic preclinical mice are studied to assess the effectiveness of the proposed algorithm. The results demonstrate that the proposed method improves the quality of spectral CT images in terms of noise elimination, edge preservation, and image detail recovery compared to the several existing better methods. |
| first_indexed | 2024-03-10T00:42:22Z |
| format | Article |
| id | doaj.art-daa7863609284d309e8026927254bca6 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-10T00:42:22Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-daa7863609284d309e8026927254bca62023-11-23T15:06:22ZengMDPI AGPhotonics2304-67322022-01-01913510.3390/photonics9010035Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT ReconstructionXuru Li0Xueqin Sun1Yanbo Zhang2Jinxiao Pan3Ping Chen4State Key Laboratory for Electronic Testing Technology, North University of China, Taiyuan 030051, ChinaState Key Laboratory for Electronic Testing Technology, North University of China, Taiyuan 030051, ChinaPing An Technology, U.S. Research Laboratory, Palo Alto, CA 94306, USAState Key Laboratory for Electronic Testing Technology, North University of China, Taiyuan 030051, ChinaState Key Laboratory for Electronic Testing Technology, North University of China, Taiyuan 030051, ChinaSpectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy channel. Tensor dictionary learning (TDL)-based methods have achieved better performance, but usually lose image edge information and details, especially from an under-sampling dataset. To address this problem, this paper proposes a method termed TDL with an enhanced sparsity constraint for spectral CT reconstruction. The proposed algorithm inherits the superiority of TDL by exploring the correlation of spectral CT images. Moreover, the method designs a regularization using the <i>L</i><sub>0</sub>-norm of the image gradient to constrain images and the difference between images and a prior image in each energy channel simultaneously, further improving the ability to preserve edge information and subtle image details. The split-Bregman algorithm has been applied to address the proposed objective minimization model. Several numerical simulations and realistic preclinical mice are studied to assess the effectiveness of the proposed algorithm. The results demonstrate that the proposed method improves the quality of spectral CT images in terms of noise elimination, edge preservation, and image detail recovery compared to the several existing better methods.https://www.mdpi.com/2304-6732/9/1/35spectral computed tomographyprior imagetensor dictionary<i>L</i><sub>0</sub>-norm of image gradient |
| spellingShingle | Xuru Li Xueqin Sun Yanbo Zhang Jinxiao Pan Ping Chen Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction Photonics spectral computed tomography prior image tensor dictionary <i>L</i><sub>0</sub>-norm of image gradient |
| title | Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction |
| title_full | Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction |
| title_fullStr | Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction |
| title_full_unstemmed | Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction |
| title_short | Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction |
| title_sort | tensor dictionary learning with an enhanced sparsity constraint for sparse view spectral ct reconstruction |
| topic | spectral computed tomography prior image tensor dictionary <i>L</i><sub>0</sub>-norm of image gradient |
| url | https://www.mdpi.com/2304-6732/9/1/35 |
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