广东工业大学学报 ›› 2013, Vol. 30 ›› Issue (2): 42-46.doi: 10.3969/j.issn.1007-7162.2013.02.009

• 综合研究 • 上一篇    下一篇

基于PCA方法的PET图像多示踪剂分离

王振友,郑少杰,何远才,龙洁丽,苏娜,林洁芝   

  1. 广东工业大学 应用数学学院,广东 广州 510520
  • 收稿日期:2012-05-04 出版日期:2013-06-27 发布日期:2013-06-27
  • 作者简介:王振友(1979-),男,副教授,主要研究方向为医学图像处理、数学模型应用.
  • 基金资助:

    广东省大学生创新实验项目(1184510143)

The Application of Principal Component Analysis to the Separation of Multiple Tracers in PET Images

Wang Zhen-you, Zheng Shao-jie, He Yuan-cai, Long Jie-li, Su Na, Lin Jie-zhi   

  1. School of Applied Mathematics,Guangdong University of Technology, Guangzhou 510520,China
  • Received:2012-05-04 Online:2013-06-27 Published:2013-06-27

摘要: 将PCA方法应用在PET肿瘤图像的多示踪剂曲线分离.首先是对两种不同的示踪剂在带噪声的情况下提取贡献率达到95%以上的主分量,然后根据示踪剂注射时间的延迟和示踪剂的主分量建立方程组,再用最小二乘法求得主分量的系数,最后得出3种示踪剂的曲线.通过实验模拟进行了数据验证,结果表明该方法是可行的.经核算,求出的结果与原数据的一致性检验达到80%以上.

关键词: 主分量分析;正电子发射断层扫描;线性最小二乘法;多示踪剂;小波分析

Abstract: It studies the application of PCA to the separation of multiple tracers in PET tumour images. First, up-to-95% contribution rate principal components were extracted from two different tracers under noisy circumstances, and then an equation set was established, based on time difference between different tracers and their principal components. and injection -time delay and principal components of the tracers. Then, the coefficient of principal components was obtained via linear least square. Finally, three kinds of tracer curves were attained.  Experimental results show that the relative data are feasible, and the consistency between the result and raw data comes to a satisfactory 80%.

Key words: principal component analysis(PCA); positron emission tomography(PET); linear  least square; multiple tracer; wavelet analysis

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