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1 October 2011 Application of penalized least-squares algorithm in PET image reconstruction based a nonlocal quadratic prior
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Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 828542 (2011) https://doi.org/10.1117/12.913243
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhiguo Gui, Jiawei He, and Xiaobo Ma "Application of penalized least-squares algorithm in PET image reconstruction based a nonlocal quadratic prior", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828542 (1 October 2011); https://doi.org/10.1117/12.913243
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