Shan Bowei
School of Information Engineering, Chang'an University, Shaanxi 710064, China.
Biomed Res Int. 2016;2016:4851401. doi: 10.1155/2016/4851401. Epub 2016 Aug 18.
We proposed a nonparametric Bayesian model based on variational Bayes algorithm to estimate the response functions in dynamic medical imaging. In dynamic renal scintigraphy, the impulse response or retention functions are rather complicated and finding a suitable parametric form is problematic. In this paper, we estimated the response functions using nonparametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness. These assumptions were used within hierarchical priors of the variational Bayes algorithm. We performed our algorithm on the real online dataset of dynamic renal scintigraphy. The results demonstrated that this algorithm improved the estimation of response functions with nonparametric priors.
我们提出了一种基于变分贝叶斯算法的非参数贝叶斯模型，用于估计动态医学成像中的响应函数。在动态肾闪烁显像中，脉冲响应或滞留函数相当复杂，找到合适的参数形式存在问题。在本文中，我们使用非参数贝叶斯先验估计响应函数。这些先验被设计为有利于函数的理想特性，如稀疏性或平滑性。这些假设在变分贝叶斯算法的分层先验中使用。我们在动态肾闪烁显像的真实在线数据集上执行了我们的算法。结果表明，该算法利用非参数先验改进了响应函数的估计。
