Chinese Journal of Tissue Engineering Research ›› 2010, Vol. 14 ›› Issue (52): 9760-9763.doi: 10.3969/j.issn.1673-8225.2010. 52.018

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Positron emission tomography image reconstruction algorithm based on an exponential Markov random field prior model

Liu Yi, Gui Zhi-guo, Zhang Quan, Shi Hai-jie   

  1. National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan  030051, Shanxi Province, China
  • Online:2010-12-24 Published:2010-12-24
  • Contact: Gui Zhi-guo, Doctor, Professor, National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, Shanxi Province, China guizhiguo@nuc.edu.cn
  • About author:Liu Yi★, Studying for master’s degree, National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, Shanxi Province, China liuyi1987827728@163.com
  • Supported by:

    the Natural Science Foundation Program of Shanxi Province, No. 2009011020-2*; the Science and Technology Development Program of Shanxi Provincial High Institutes, No. 20081024*

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Abstract:

BACKGROUND: Maximum likelihood expectation maximization (MLEM) algorithm, the classical algorithm in PET reconstruction, is superior to filtered back projection reconstruction with its better performance in resolution and noise characteristic. However, with the increasing iterations, the noisy influence of reconstruction image increases.
OBJECTIVE: To propose a maximum a posteriori (MAP) reconstruction algorithm based on exponential prior distribution for noise suppression
METHODS: Exponent prior distribution replaces the Gaussian of traditional MAP, and the reconstruction image is tested with signal-to-noise and root mean squared error.
RESULTS AND CONCLUSION: Results show that the proposed method performed well for noise suppression, and preferably keep the edges of reconstruction image without excessive smoothing.

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