Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction
Energy spectrum computed tomography (CT) technology based on photon-counting detectors has been widely used in many applications such as lesion detection, material decomposition, and so on. But severe noise in the reconstructed images affects the accuracy of these applications. The method based on t...
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MDPI AG
2025-05-01
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| author | Xuru Li Kun Wang Yan Chang Yaqin Wu Jing Liu |
| author_facet | Xuru Li Kun Wang Yan Chang Yaqin Wu Jing Liu |
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| description | Energy spectrum computed tomography (CT) technology based on photon-counting detectors has been widely used in many applications such as lesion detection, material decomposition, and so on. But severe noise in the reconstructed images affects the accuracy of these applications. The method based on tensor decomposition can effectively remove noise by exploring the correlation of energy channels, but it is difficult for traditional tensor decomposition methods to describe the problem of tensor sparsity and low-rank properties of all expansion modules simultaneously. To address this issue, an algorithm for spectral CT reconstruction based on photon-counting detectors is proposed, which combines Kronecker-Basis-Representation (KBR) tensor decomposition and total variational (TV) regularization (namely KBR-TV). The proposed algorithm uses KBR tensor decomposition to unify the sparse measurements of traditional tensor spaces, and constructs a third-order tensor cube through non-local image similarity matching. At the same time, the TV regularization term is introduced into the independent energy spectrum image domain to enhance the sparsity constraint of single-channel images, effectively reduce artifacts, and improve the accuracy of image reconstruction. The proposed objective minimization model has been tackled using the split-Bregman algorithm. To evaluate the algorithm’s performance, both numerical simulations and realistic preclinical mouse studies were conducted. The ultimate findings indicate that the KBR-TV method offers superior enhancement in the quality of spectral CT images in comparison to several existing methods. |
| format | Article |
| id | doaj-art-ea1ec7b84a02460eb898963ea5d79613 |
| institution | OA Journals |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Photonics |
| spelling | doaj-art-ea1ec7b84a02460eb898963ea5d796132025-08-20T02:33:55ZengMDPI AGPhotonics2304-67322025-05-0112549210.3390/photonics12050492Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography ReconstructionXuru Li0Kun Wang1Yan Chang2Yaqin Wu3Jing Liu4School of Software, Shanxi Agricultural University, Taigu 030800, ChinaSchool of Software, Shanxi Agricultural University, Taigu 030800, ChinaSchool of Software, Shanxi Agricultural University, Taigu 030800, ChinaSchool of Software, Shanxi Agricultural University, Taigu 030800, ChinaSchool of Software, Shanxi University, Taiyuan 030032, ChinaEnergy spectrum computed tomography (CT) technology based on photon-counting detectors has been widely used in many applications such as lesion detection, material decomposition, and so on. But severe noise in the reconstructed images affects the accuracy of these applications. The method based on tensor decomposition can effectively remove noise by exploring the correlation of energy channels, but it is difficult for traditional tensor decomposition methods to describe the problem of tensor sparsity and low-rank properties of all expansion modules simultaneously. To address this issue, an algorithm for spectral CT reconstruction based on photon-counting detectors is proposed, which combines Kronecker-Basis-Representation (KBR) tensor decomposition and total variational (TV) regularization (namely KBR-TV). The proposed algorithm uses KBR tensor decomposition to unify the sparse measurements of traditional tensor spaces, and constructs a third-order tensor cube through non-local image similarity matching. At the same time, the TV regularization term is introduced into the independent energy spectrum image domain to enhance the sparsity constraint of single-channel images, effectively reduce artifacts, and improve the accuracy of image reconstruction. The proposed objective minimization model has been tackled using the split-Bregman algorithm. To evaluate the algorithm’s performance, both numerical simulations and realistic preclinical mouse studies were conducted. The ultimate findings indicate that the KBR-TV method offers superior enhancement in the quality of spectral CT images in comparison to several existing methods.https://www.mdpi.com/2304-6732/12/5/492photon-counting detectorspectral computed tomographyCT reconstructionKBR tensor decompositiontotal variation |
| spellingShingle | Xuru Li Kun Wang Yan Chang Yaqin Wu Jing Liu Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction Photonics photon-counting detector spectral computed tomography CT reconstruction KBR tensor decomposition total variation |
| title | Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction |
| title_full | Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction |
| title_fullStr | Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction |
| title_full_unstemmed | Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction |
| title_short | Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction |
| title_sort | combining kronecker basis representation tensor decomposition and total variational constraint for spectral computed tomography reconstruction |
| topic | photon-counting detector spectral computed tomography CT reconstruction KBR tensor decomposition total variation |
| url | https://www.mdpi.com/2304-6732/12/5/492 |
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