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Proceedings Paper

Texture-preserved penalized weighted least-squares reconstruction of low-dose CT image via image segmentation and high-order MRF modeling
Author(s): Hao Han; Hao Zhang; Xinzhou Wei; William Moore; Zhengrong Liang
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Paper Abstract

In this paper, we proposed a low-dose computed tomography (LdCT) image reconstruction method with the help of prior knowledge learning from previous high-quality or normal-dose CT (NdCT) scans. The well-established statistical penalized weighted least squares (PWLS) algorithm was adopted for image reconstruction, where the penalty term was formulated by a texture-based Gaussian Markov random field (gMRF) model. The NdCT scan was firstly segmented into different tissue types by a feature vector quantization (FVQ) approach. Then for each tissue type, a set of tissue-specific coefficients for the gMRF penalty was statistically learnt from the NdCT image via multiple-linear regression analysis. We also proposed a scheme to adaptively select the order of gMRF model for coefficients prediction. The tissue-specific gMRF patterns learnt from the NdCT image were finally used to form an adaptive MRF penalty for the PWLS reconstruction of LdCT image. The proposed texture-adaptive PWLS image reconstruction algorithm was shown to be more effective to preserve image textures than the conventional PWLS image reconstruction algorithm, and we further demonstrated the gain of high-order MRF modeling for texture-preserved LdCT PWLS image reconstruction.

Paper Details

Date Published: 22 March 2016
PDF: 6 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97834F (22 March 2016); doi: 10.1117/12.2216428
Show Author Affiliations
Hao Han, State Univ. of New York at Stony Brook (United States)
Hao Zhang, State Univ. of New York at Stony Brook (United States)
Xinzhou Wei, New York City College of Technology (United States)
William Moore, State Univ. of New York at Stony Brook (United States)
Zhengrong Liang, State Univ. of New York at Stony Brook (United States)


Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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