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ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography

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 limi...

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Autores principales: Yu, Wei, Zeng, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4497654/
https://www.ncbi.nlm.nih.gov/pubmed/26158543
http://dx.doi.org/10.1371/journal.pone.0130793
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author Yu, Wei
Zeng, Li
author_facet Yu, Wei
Zeng, Li
author_sort Yu, Wei
collection PubMed
description 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 ℓ (0) gradient minimization for limited-angle CT in this paper. The ℓ (0)-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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spelling pubmed-44976542015-07-14 ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography Yu, Wei Zeng, Li PLoS One Research Article 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 ℓ (0) gradient minimization for limited-angle CT in this paper. The ℓ (0)-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. Public Library of Science 2015-07-09 /pmc/articles/PMC4497654/ /pubmed/26158543 http://dx.doi.org/10.1371/journal.pone.0130793 Text en © 2015 Yu, Zeng http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Yu, Wei
Zeng, Li
ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
title ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
title_full ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
title_fullStr ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
title_full_unstemmed ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
title_short ℓ (0) Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
title_sort ℓ (0) gradient minimization based image reconstruction for limited-angle computed tomography
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4497654/
https://www.ncbi.nlm.nih.gov/pubmed/26158543
http://dx.doi.org/10.1371/journal.pone.0130793
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