ArticleIEEE transactions on instrumentation and measurement2021
Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19.
Article in IEEE transactions on instrumentation and measurement, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 21 citations in OpenAlex.
- Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests.Diagnostics (Basel, Switzerland) · 2023Article
- McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.Applied soft computing · 2022Article
- A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models.Scientific reports · 2022Article
- Deep Generative Learning-Based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests.IEEE transactions on instrumentation and measurement · 2022Article
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
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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 L
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