PET贝叶斯神经网络重建算法

从ML—EM重建算法入手，分析了贝叶斯模型的一些关键点，针对采用传统方法求解MAP问题的局限性，提出一种用于正电子成像的贝叶斯神经网络(BNN)重建算法，为了保留边缘信息，引入了二进制的保边缘变量，并应用共轭神经网络求解，模拟的重建结果表明，应用这种算法可以得到比ML—EM算法更好的重建图像。

An algorithm for bayesian neural network reconstruction in PET imaging

Some key aspects in the Bayesian Maximum Likelihood- Expectation Maximization Method (ML- EM)for positron emission tomography(PET)imaging were investigated.In order to overcome the limitation of traditional solutions to estimate Maximum a Posteriori(MAP),a Bayesian neural network(BNN) algorithm was proposed for PET imaging.In addition to real- valued source intensities,binary variables were introduced to protect the information of the edges.These two different kinds of variables can be obtained by a coupled gradient network composed of two interacting recurrent networks corresponding to the two kinds of variables respectively.Compared with ML- EM reconstruction,the BNN results showed higher quality.