Peng Jyh-Ying, Aston John A D, Gunn Roger N, Liou Cheng-Yuan, Ashburner John
Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan.
IEEE Trans Med Imaging. 2008 Sep;27(9):1356-69. doi: 10.1109/TMI.2008.922185.
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework using an over-complete exponential basis set and sparse Bayesian learning. The technique is applicable to analyses requiring either a plasma or reference tissue input function and produces estimates of the system's macro-parameters and model order. In addition, the Bayesian approach returns the posterior distribution which allows for some characterisation of the error component. The method is applied to the estimation of parametric images of neuroreceptor radioligand studies.
本文提出了一种使用稀疏贝叶斯学习分析动态正电子发射断层扫描(PET)数据的方法。在一个隔室框架中,使用过完备指数基集和稀疏贝叶斯学习来估计参数。该技术适用于需要血浆或参考组织输入函数的分析,并能生成系统宏观参数和模型阶数的估计值。此外,贝叶斯方法返回后验分布,这使得可以对误差分量进行一些特征描述。该方法应用于神经受体放射性配体研究的参数图像估计。