Chinese Journal of Tissue Engineering Research ›› 2011, Vol. 15 ›› Issue (22): 4090-4093.doi: 10.3969/j.issn.1673-8225.2011.22.024

Previous Articles     Next Articles

Application of wavelet prior in OSL reconstruction algorithm

Xue Ying, Pan Jin-xiao, Kong Hui-hua   

  1. National Key Lab for Electronic Measurement and Technology, North University of China, Taiyuan  030051, Shanxi Province, China
  • Received:2010-12-22 Revised:2011-03-22 Online:2011-05-28 Published:2011-05-28
  • Contact: Pan Jin-xiao, Doctoral supervisor, Professor, National Key Lab for Electronic Measurement and Technology, North University of China, Taiyuan 030051, Shanxi Province, China panjx@nuc.edu.cn Correspondence to: Kong Hui-hua, Doctor, Lecturer, National Key Lab for Electronic Measurement and Technology, North University of China, Taiyuan 030051, Shanxi Province, China fengerkong@nuc.edu.cn
  • About author:Xue Ying★, Studying for master’s degree, National Key Lab for Electronic Measurement and Technology, North University of China, Taiyuan 030051, Shanxi Province, China xueying112611@163.com
  • Supported by:

    the National Natural Science Foundation of China, No. 60772102*, 61071193*; the Natural Science Foundation of Shanxi Province, No. 2010011002-1*

PDF

408

Abstract:

BACKGROUND: MAP (maximum a posteriori) statistical reconstruction methods can introduce appropriate prior knowledge in reconstruction process to remove the noises.
OBJECTIVE: To process the corresponding high frequency information, according to statistic characteristics of wavelet coefficients and energy balance principle, and to apply multi-scale wavelet prior to OSL reconstruction algorithm to remove noises.
METHODS: Most prior information can reduce noise in airspace. From “transform domain”, according to statistic characteristics of wavelet coefficients and energy balance principle of different scales, high frequency information is processed in wavelet domain. And processed wavelet coefficients are applied in wavelet reconstruction as a kind of multi-scale wavelet prior information to remove noises.
RESULTS AND CONCLUSION: The errors and noises of reconstructed images and testing model of OSL algorithm based on wavelet priori information is less than ML-EM algorithm, and correlation greater. Reconstructed images have become smoother, and visual effect is clearer. This shows that wavelet prior, based on wavelet coefficient statistical characteristic and energy balance principle, can effectively reduce the noises in CT reconstruction process.

CLC Number: