Paper
6 March 2013 Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior
Yunwan Zhang, Jianhua Ma, Jing Huang, Hua Zhang, Zhaoying Bian, Dong Zeng, Qianjin Feng, Zhengrong Liang, Wufan Chen
Author Affiliations +
Proceedings Volume 8668, Medical Imaging 2013: Physics of Medical Imaging; 86685E (2013) https://doi.org/10.1117/12.2007958
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Sparse-view x-ray computed tomography (CT) imaging still is an interesting topic in CT field. In this paper, a new iterative image reconstruction approach for sparse-view CT with a normal-dose image was presented. The proposed cost-function which is under the criteria of penalized weighed least-square (PWLS) for CT image reconstruction mainly contains two terms, i.e., fidelity term and prior term. For the fidelity term, the weights of weighed least-square term are determined by considering the relationship between the variance and mean of the projection data in the presence of electronic background noise. For the prior term, a normal-dose image induced total variation (ndiTV) prior is proposed as an extension of the PICCS algorithm introduced by Chen et al 2008, which can relieve the requirement of misalignment reduction of the PICCS algorithm. For simplicity, the present approach is referred to as “PWLS-ndiTV”. Qualitative and quantitative evaluations were carried out on the present PWLS-ndiTV approach. Experimental results show that the present PWLS-ndiTV approach can achieve significant gains than the existing similar methods in noise and artifacts suppression.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunwan Zhang, Jianhua Ma, Jing Huang, Hua Zhang, Zhaoying Bian, Dong Zeng, Qianjin Feng, Zhengrong Liang, and Wufan Chen "Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior", Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 86685E (6 March 2013); https://doi.org/10.1117/12.2007958
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KEYWORDS
X-ray computed tomography

Image restoration

CT reconstruction

Neodymium

Data modeling

Reconstruction algorithms

Signal attenuation

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