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- Publisher Website: 10.1109/TIM.2021.3050190
- Scopus: eid_2-s2.0-85099730968
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Article: Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19
Title | Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19 |
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Authors | |
Keywords | Chest CT Coronavirus Disease 2019 (COVID-19) low-dose computed tomography (CT) tensor gradient L₀-norm |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=19 |
Citation | IEEE Transactions on Instrumentation and Measurement, 2021, v. 70, p. article no. 4503012 How to Cite? |
Abstract | Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L0-norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the μCT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM. |
Description | Bronze open access |
Persistent Identifier | http://hdl.handle.net/10722/299311 |
ISSN | 2023 Impact Factor: 5.6 2023 SCImago Journal Rankings: 1.536 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wu, W | - |
dc.contributor.author | Shi, J | - |
dc.contributor.author | Yu, H | - |
dc.contributor.author | Wu, W | - |
dc.contributor.author | Vardhanabhuti, V | - |
dc.date.accessioned | 2021-05-10T07:00:00Z | - |
dc.date.available | 2021-05-10T07:00:00Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Instrumentation and Measurement, 2021, v. 70, p. article no. 4503012 | - |
dc.identifier.issn | 0018-9456 | - |
dc.identifier.uri | http://hdl.handle.net/10722/299311 | - |
dc.description | Bronze open access | - |
dc.description.abstract | Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L0-norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the μCT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=19 | - |
dc.relation.ispartof | IEEE Transactions on Instrumentation and Measurement | - |
dc.rights | IEEE Transactions on Instrumentation and Measurement. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Chest CT | - |
dc.subject | Coronavirus Disease 2019 (COVID-19) | - |
dc.subject | low-dose computed tomography (CT) | - |
dc.subject | tensor gradient L₀-norm | - |
dc.title | Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19 | - |
dc.type | Article | - |
dc.identifier.email | Vardhanabhuti, V: varv@hku.hk | - |
dc.identifier.authority | Vardhanabhuti, V=rp01900 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1109/TIM.2021.3050190 | - |
dc.identifier.scopus | eid_2-s2.0-85099730968 | - |
dc.identifier.hkuros | 322349 | - |
dc.identifier.volume | 70 | - |
dc.identifier.spage | article no. 4503012 | - |
dc.identifier.epage | article no. 4503012 | - |
dc.identifier.isi | WOS:000617752900009 | - |
dc.publisher.place | United States | - |