ArticlePloS one2015
ℓ0 Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Morphing from the TV-Norm to theBiomedical journal of scientific & technical research · 2024Article
- Image reconstruction from data over two orthogonal arcs of limited-angular ranges.Medical physics · 2022Article
- Directional-TV algorithm for image reconstruction from limited-angular-range data.Medical image analysis · 2021Article
- An Algorithm ofInternational journal of biomedical imaging · 2020Article
- Low-dose spectral CT reconstruction using image gradientApplied mathematical modelling · 2018Article
- Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review.Medical physics · 2018Review
- 4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction.Physics in medicine and biology · 2018Article
- Article
- Multi-Mounted X-Ray Computed Tomography.PloS one · 2016Article
- Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction.PloS one · 2016Article
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Authors and funding
2 authors.
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Abstract
In medical and industrial applications of computed tomography (CT) imaging, limited by the scanning environment and the risk of excessive X-ray radiation exposure imposed to the patients, reconstructing high quality CT images from limited projection data has become a hot topic. X-ray imaging in limited scanning angular range is an effective imaging modality to reduce the radiation dose to the patients. As the projection data available in this modality are incomplete, limited-angle CT image reconstruction is actually an ill-posed inverse problem. To solve the problem, image reconstructed by conventional filtered back projection (FBP) algorithm frequently results in conspicuous streak artifacts and gradual changed artifacts nearby edges. Image reconstruction based on total variation minimization (TVM) can significantly reduce streak artifacts in few-view CT, but it suffers from the gradual changed artifacts nearby edges in limited-angle CT. To suppress this kind of artifacts, we develop an image reconstruction algorithm based on ℓ0 gradient minimization for limited-angle CT in this paper. The ℓ0-norm of the image gradient is taken as the regularization function in the framework of developed reconstruction model. We transformed the optimization problem into a few optimization sub-problems and then, solved these sub-problems in the manner of alternating iteration. Numerical experiments are performed to validate the efficiency and the feasibility of the developed algorithm. From the statistical analysis results of the performance evaluations peak signal-to-noise ratio (PSNR) and normalized root mean square distance (NRMSD), it shows that there are significant statistical differences between different algorithms from different scanning angular ranges (p<0.0001). From the experimental results, it also indicates that the developed algorithm outperforms classical reconstruction algorithms in suppressing the streak artifacts and the gradual changed artifacts nearby edges simultaneously.
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