Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization.

Wu, W; Yu, H; Liu, F; Zhang, J; Vardhanabhuti, V

Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization.

Wu, W Yu, H Liu, F Zhang, J

Vardhanabhuti, V

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Comput Biol Med, 2022, 151, (Pt A), pp. 106080
Issue Date:: 2022-12

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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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http://hdl.handle.net/10453/167875