fMRI time series analysis based on stationary wavelet and spectrum analysis

查看全文 作　　者：ZHI [1,2,3]Lianhe;ZHAO [4]Xia;SHAN [1]Baoci;PENG [5]Silong;YAN [1]Qiang;YUAN [1]Xiuli;TANG [1,6]Xiaowei

机构地区：[1]Key Laboratory of Nuclear Analysis Techniques, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China;[2]Graduate School of Chinese Academy of Sciences, Beijing 100049, China;[3]Department of Physics, Zhoukou Normal University, Zhoukou 466100, China;[4]Research Imaging Center, University of Texas Health Science Center, San Antonio, Texas 78229, USA;[5]National ASIC Design and Engineering Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China;[6]Department of Physics, Zhejiang University, Hangzhou 310027, China

出　　处：《Progress in Natural Science:Materials International》索引2006年第16卷第11期,共6页

基　　金：Supported by National Natural Science Foundation of China ( Grant No . 30570508) and National Basic Research Program of China(2006CB705700) 摘　　要：The low signal to noise ratio (SNR) of functional MRI (fMRI) prefers more sensitive data analysis methods. Based on stationary wavelet transform and spectrum analysis, a new method with high detective sensitivity was developed for analyzing fMRI time series, which does not require any prior assumption of the characteristics of noises. In the proposed method, every component of fMRI time series in the different time-frequency scales of stationary wavelet transform was discerned by the spectrum analysis, then the components from noises were removed using the stationary wavelet transform, finally the components of real brain activation were detected by cross-correlation analysis. The results obtained from both simulated and in vivo visual experiments illustrated that the proposed method has much higher sensitivity than the traditional cross-correlation method. 关键词：FMRI 固定小波变换光谱分析数据分析噪声比核磁共振成像

相关文献

参考文献（27）
引证文献（3）
耦合文献（0）

参考文献(27)

引证文献(3)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

fMRI time series analysis based on stationary wavelet and spectrum analysis

参考文献(27)

引证文献(3)

用户提示