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Proceedings Paper

Application of penalized least-squares algorithm in PET image reconstruction based a nonlocal quadratic prior
Author(s): Zhiguo Gui; Jiawei He; Xiaobo Ma
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Paper Abstract

In this paper, we present a novel image reconstruction method based on penalized least squares (PLS) objective function for positron emission tomography (PET). Unlike usual PLS algorithm, the proposed method, which is called NL-PLS, combines a novel nonlocal quadratic prior with the classical least squares algorithm. The novel prior can not only solve the unfavorable oversmoothing effect produced by the simple quadratic membrane (QM) smoothing prior, but also partly eliminate blocky piecewise regions or so-called staircase artifacts produced by edge-preserving nonquadratic priors. What's more, we can easily confirm the convergence of the NL-PLS as the objective function' quadratic characteristic. The performance of the proposed NL-PLS method is evaluated in experiments using simulated data. The results show that the method is advantageous, compared with the Filter Back Projection (FBP) reconstruction and Maximum Likelihood (MLEM) reconstruction, and Bayesian constructions using the normal local priors.

Paper Details

Date Published: 1 October 2011
PDF: 8 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828542 (1 October 2011); doi: 10.1117/12.913243
Show Author Affiliations
Zhiguo Gui, North Univ. of China (China)
Jiawei He, North Univ. of China (China)
Xiaobo Ma, North Univ. of China (China)


Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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