Smoothing dynamic positron emission tomography time courses using functional principal components.
Smoothing dynamic positron emission tomography time courses using functional principal components.
复制标题
使用功能主成分平滑动态正电子发射断层扫描时间课程。
DOI:
10.1016/j.neuroimage.2009.03.051
复制
发表时间:
2009-08-01
期刊:
影响因子:
5.7
通讯作者:
Wang JL
中科院分区:
文献类型:
--
作者:
Jiang CR;Aston JA;Wang JL
A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a non-parametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows subsequent analysis by methods such as Spectral Analysis to be substantially improved in terms of their mean squared error.
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DOI:
10.1097/01.wcb.0000045042.03034.42
发表时间:
2002-12-01
影响因子:
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通讯作者:
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