Tensor Gradient L0-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19.
IEEE Trans Instrum Meas
; 70: 4503012, 2021.
Article
in English
| MEDLINE | ID: covidwho-1084599
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 [Formula see text]CT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
IEEE Trans Instrum Meas
Year:
2021
Document Type:
Article
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