中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (52): 9760-9763.doi: 10.3969/j.issn.1673-8225.2010. 52.018

• 骨与关节图像与影像 bone and joint imaging • 上一篇    下一篇

一种基于指数型先验分布的正电子发射断层图像重建算法

刘  祎，桂志国，张  权，石海杰   

  1. 中北大学电子测试技术国家重点实验室，山西省太原市  030051
  • 出版日期:2010-12-24 发布日期:2010-12-24
  • 通讯作者: 桂志国，博士，教授，中北大学电子测试技术国家重点实验室，山西省太原市 030051 guizhiguo@nuc.edu.cn
  • 作者简介:刘祎★，女，1987年生，河南省睢县人，中北大学在读硕士，主要从事图像处理，医学图像重建方面的研究。 liuyi1987827728@163.com
  • 基金资助:

    山西省自然科学基金重点项目(2009011020-2)，山西省高等学校科技开发项目资助(20081024)。

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*

PDF

460

被引次数

5

摘要：

背景：最大似然估计算法是正电子发射断层图像重建的经典算法，能够在信息量不足的情况下获得分辨率和噪声特性均优于滤波反投影重建的重建结果。但是MLEM算法具有不稳定性，即随迭代次数的增加，图像噪声反而会增加。
目的：针对MLEM算法的图像噪声问题，提出一种基于指数型先验分布约束的MAP重建算法。
方法：将指数先验分布代替传统MAP重建中的高斯先验，并用信噪比和归一化均方误差来判断重建质量。
结果与结论：实验证明，该算法不仅能够抑制噪声，而且能够保持重建图像的边缘，不会造成过分平滑。

关键词: 正电子断层成像, MLEM算法, 图像噪声, MAP 重建, 指数型先验分布

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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