正电子发射成像是一种有效的生理功能性成像手段，但是由于投影数据中噪声大而给重建带来困难，为此提出了利用其他高质量的解剖成像结果的分割模板先验来进行完全Bayesian重建以提高重建效果，分割模板先验可以表示为包含超验参数的Markov场形式，但是它的非凸性和超验参数的存在使得无法用常规的方法得到最大后验估计，为此采用动态后验模拟算法计算后验平均估计，基于满足条件分布的动态后验模拟法可以同时更新象素的密度和超验参数，并且容易得到重建的方差和置信区间，将这种方法和似然估计、最大后验估计结果进行比较，重建的结果无论在空间分辨率和抑制噪声方面都有取得了好的效果。

Positron emission tomography(PET) is a noisy functional medical imaging model. In this paper a fully Bayesian PET reconstruction method is presented for combining a segmented anatomical membrane a priori. The segmented anatomical membrane a priori is based on the fact that the radiopharmaceutical activity is similar throughout each region and the anatomical information can be obtained from other imaging modalities such as CT or MRI. The prior distributions are formed as some kind of Markov random field. Due the non convex and the hyper parameters in the prior, it is difficult to use point estimator such as maximum a posteriori(MAP). So we used Dynamic Markov chain Monte Carlo posterior simulation method to get a minimum mean square error(MMSE) estimator which update the hyper parameters as well as density data. The variances and credit area of the reconstruction results can be easy gotten by MMSE. We compared the reconstruction result of ML, MAP and MMSE, and find that the segmented anatomical membrane a priori exhibit improved the noise and resolution properties and Dynamic Markov chain Monte Carlo is mostly suitable for fully Bayesian reconstruction.