A GENERALIZED EM ALGORITHM FOR 3-D BAYESIAN RECONSTRUCTION FROM POISSON DATA USING GIBBS PRIORS
A GENERALIZED EM ALGORITHM FOR 3-D BAYESIAN RECONSTRUCTION FROM POISSON DATA USING GIBBS PRIORS
复制标题
DOI：
10.1109/42.24868
复制
发表时间：
1989-06-01
影响因子：
10.6
通讯作者：
LEAHY, R
中科院分区：
文献类型：
--
作者：
HEBERT, T;LEAHY, R
一个广义期望最大化（GEM）算法的贝叶斯重建，基于局部相关的马尔可夫随机场先验的形式吉布斯函数和泊松数据模型。对于算法的M步，导出了一种坐标梯度上升形式。该算法减少到EM最大似然算法的马尔可夫随机场先验趋于均匀分布。三种不同的吉布斯函数先验进行了检查。重建的3-D图像从泊松模型的单光子发射计算机断层扫描。< >
A generalized expectation-maximization (GEM) algorithm is developed for Bayesian reconstruction, based on locally correlated Markov random-field priors in the form of Gibbs functions and on the Poisson data model. For the M-step of the algorithm, a form of coordinate gradient ascent is derived. The algorithm reduces to the EM maximum-likelihood algorithm as the Markov random-field prior tends towards a uniform distribution. Three different Gibbs function priors are examined. Reconstructions of 3-D images obtained from the Poisson model of single-photon-emission computed tomography are presented.< >