Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization.
- Publisher:
- PERGAMON-ELSEVIER SCIENCE LTD
- Publication Type:
- Journal Article
- Citation:
- Comput Biol Med, 2022, 151, (Pt A), pp. 106080
- Issue Date:
- 2022-12
Closed Access
Filename | Description | Size | |||
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1-s2.0-S0010482522007880-main.pdf | Published version | 9.56 MB | Adobe PDF |
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Wu, W | |
dc.contributor.author | Yu, H | |
dc.contributor.author | Liu, F | |
dc.contributor.author |
Zhang, J https://orcid.org/0000-0001-5048-5606 |
|
dc.contributor.author | Vardhanabhuti, V | |
dc.date.accessioned | 2023-03-21T01:11:29Z | |
dc.date.available | 2022-09-03 | |
dc.date.available | 2023-03-21T01:11:29Z | |
dc.date.issued | 2022-12 | |
dc.identifier.citation | Comput Biol Med, 2022, 151, (Pt A), pp. 106080 | |
dc.identifier.issn | 0010-4825 | |
dc.identifier.issn | 1879-0534 | |
dc.identifier.uri | http://hdl.handle.net/10453/167875 | |
dc.description.abstract | It is challenging to obtain good image quality in spectral computed tomography (CT) as the photon-number for the photon-counting detectors is limited for each narrow energy bin. This results in a lower signal to noise ratio (SNR) for the projections. To handle this issue, we first formulate the weight bidirectional image gradient with L0-norm constraint of spectral CT image. Then, as a new regularizer, bidirectional image gradient with L0-norm constraint is introduced into the tensor decomposition model, generating the Spectral-Image Tensor and Bidirectional Image-gradient Minimization (SITBIM) algorithm. Finally, the split-Bregman method is employed to optimize the proposed SITBIM mathematical model. The experiments on the numerical mouse phantom and real mouse experiments are designed to validate and evaluate the SITBIM method. The results demonstrate that the SITBIM can outperform other state-of-the-art methods (including TVM, TV + LR, SSCMF and NLCTF). INDEX TERMS: -spectral CT, image reconstruction, tensor decomposition, unidirectional image gradient, image similarity. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | |
dc.relation.ispartof | Comput Biol Med | |
dc.relation.isbasedon | 10.1016/j.compbiomed.2022.106080 | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | 08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences | |
dc.subject.classification | Biomedical Engineering | |
dc.subject.mesh | Mice | |
dc.subject.mesh | Animals | |
dc.subject.mesh | Image Processing, Computer-Assisted | |
dc.subject.mesh | Phantoms, Imaging | |
dc.subject.mesh | Tomography, X-Ray Computed | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Signal-To-Noise Ratio | |
dc.subject.mesh | Animals | |
dc.subject.mesh | Mice | |
dc.subject.mesh | Tomography, X-Ray Computed | |
dc.subject.mesh | Phantoms, Imaging | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Image Processing, Computer-Assisted | |
dc.subject.mesh | Signal-To-Noise Ratio | |
dc.subject.mesh | Mice | |
dc.subject.mesh | Animals | |
dc.subject.mesh | Image Processing, Computer-Assisted | |
dc.subject.mesh | Phantoms, Imaging | |
dc.subject.mesh | Tomography, X-Ray Computed | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Signal-To-Noise Ratio | |
dc.title | Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization. | |
dc.type | Journal Article | |
utslib.citation.volume | 151 | |
utslib.location.activity | United States | |
utslib.for | 08 Information and Computing Sciences | |
utslib.for | 09 Engineering | |
utslib.for | 11 Medical and Health Sciences | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute | |
utslib.copyright.status | closed_access | * |
dc.date.updated | 2023-03-21T01:11:12Z | |
pubs.issue | Pt A | |
pubs.publication-status | Published | |
pubs.volume | 151 | |
utslib.citation.issue | Pt A |
Abstract:
It is challenging to obtain good image quality in spectral computed tomography (CT) as the photon-number for the photon-counting detectors is limited for each narrow energy bin. This results in a lower signal to noise ratio (SNR) for the projections. To handle this issue, we first formulate the weight bidirectional image gradient with L0-norm constraint of spectral CT image. Then, as a new regularizer, bidirectional image gradient with L0-norm constraint is introduced into the tensor decomposition model, generating the Spectral-Image Tensor and Bidirectional Image-gradient Minimization (SITBIM) algorithm. Finally, the split-Bregman method is employed to optimize the proposed SITBIM mathematical model. The experiments on the numerical mouse phantom and real mouse experiments are designed to validate and evaluate the SITBIM method. The results demonstrate that the SITBIM can outperform other state-of-the-art methods (including TVM, TV + LR, SSCMF and NLCTF). INDEX TERMS: -spectral CT, image reconstruction, tensor decomposition, unidirectional image gradient, image similarity.
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