Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.

X-ray computed tomography (CT) iterative image reconstruction from sparse-view projection data has been an important research topic for radiation reduction in clinic. In this paper, to relieve the requirement of misalignment reduction operation of the prior image constrained compressed sensing (PICC...

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Autores principales:	Jing Huang, Yunwan Zhang, Jianhua Ma, Dong Zeng, Zhaoying Bian, Shanzhou Niu, Qianjin Feng, Zhengrong Liang, Wufan Chen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/568d4b82e8a44a7c92e80f2616e12294
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spelling	oai:doaj.org-article:568d4b82e8a44a7c92e80f2616e122942021-11-18T08:45:56ZIterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.1932-620310.1371/journal.pone.0079709https://doaj.org/article/568d4b82e8a44a7c92e80f2616e122942013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24260288/?tool=EBIhttps://doaj.org/toc/1932-6203X-ray computed tomography (CT) iterative image reconstruction from sparse-view projection data has been an important research topic for radiation reduction in clinic. In this paper, to relieve the requirement of misalignment reduction operation of the prior image constrained compressed sensing (PICCS) approach introduced by Chen et al, we present an iterative image reconstruction approach for sparse-view CT using a normal-dose image induced total variation (ndiTV) prior. The associative objective function of the present approach is constructed under the penalized weighed least-square (PWLS) criteria, which contains two terms, i.e., the weighted least-square (WLS) fidelity and the ndiTV prior, and is referred to as "PWLS-ndiTV". Specifically, the WLS fidelity term is built based on an accurate relationship between the variance and mean of projection data in the presence of electronic background noise. The ndiTV prior term is designed to reduce the influence of the misalignment between the desired- and prior- image by using a normal-dose image induced non-local means (ndiNLM) filter. Subsequently, a modified steepest descent algorithm is adopted to minimize the associative objective function. Experimental results on two different digital phantoms and an anthropomorphic torso phantom show that the present PWLS-ndiTV approach for sparse-view CT image reconstruction can achieve noticeable gains over the existing similar approaches in terms of noise reduction, resolution-noise tradeoff, and low-contrast object detection.Jing HuangYunwan ZhangJianhua MaDong ZengZhaoying BianShanzhou NiuQianjin FengZhengrong LiangWufan ChenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e79709 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jing Huang Yunwan Zhang Jianhua Ma Dong Zeng Zhaoying Bian Shanzhou Niu Qianjin Feng Zhengrong Liang Wufan Chen Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.
description	X-ray computed tomography (CT) iterative image reconstruction from sparse-view projection data has been an important research topic for radiation reduction in clinic. In this paper, to relieve the requirement of misalignment reduction operation of the prior image constrained compressed sensing (PICCS) approach introduced by Chen et al, we present an iterative image reconstruction approach for sparse-view CT using a normal-dose image induced total variation (ndiTV) prior. The associative objective function of the present approach is constructed under the penalized weighed least-square (PWLS) criteria, which contains two terms, i.e., the weighted least-square (WLS) fidelity and the ndiTV prior, and is referred to as "PWLS-ndiTV". Specifically, the WLS fidelity term is built based on an accurate relationship between the variance and mean of projection data in the presence of electronic background noise. The ndiTV prior term is designed to reduce the influence of the misalignment between the desired- and prior- image by using a normal-dose image induced non-local means (ndiNLM) filter. Subsequently, a modified steepest descent algorithm is adopted to minimize the associative objective function. Experimental results on two different digital phantoms and an anthropomorphic torso phantom show that the present PWLS-ndiTV approach for sparse-view CT image reconstruction can achieve noticeable gains over the existing similar approaches in terms of noise reduction, resolution-noise tradeoff, and low-contrast object detection.
format	article
author	Jing Huang Yunwan Zhang Jianhua Ma Dong Zeng Zhaoying Bian Shanzhou Niu Qianjin Feng Zhengrong Liang Wufan Chen
author_facet	Jing Huang Yunwan Zhang Jianhua Ma Dong Zeng Zhaoying Bian Shanzhou Niu Qianjin Feng Zhengrong Liang Wufan Chen
author_sort	Jing Huang
title	Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.
title_short	Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.
title_full	Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.
title_fullStr	Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.
title_full_unstemmed	Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.
title_sort	iterative image reconstruction for sparse-view ct using normal-dose image induced total variation prior.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/568d4b82e8a44a7c92e80f2616e12294
work_keys_str_mv	AT jinghuang iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT yunwanzhang iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT jianhuama iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT dongzeng iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT zhaoyingbian iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT shanzhouniu iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT qianjinfeng iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT zhengrongliang iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior AT wufanchen iterativeimagereconstructionforsparseviewctusingnormaldoseimageinducedtotalvariationprior
_version_	1718421322285973504

Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.

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