Development and evaluation of MRI based Bayesian image reconstruction methods for PET

MRI based Bayesian image reconstruction methods for positron emission tomography reconstruction with the aim at improving image quality was developed. The line site map from MRI as a priori is made up of either a modified Markov random field or Canny edge detector with the Gaussian smoothing filter. It is used in maximum a posteriori (MAP) algorithm by a weighted line site method. We evaluate and compare the performance of these reconstruction methods. The results show that the Bayesian methods produce reconstructed images with less noise and better spatial resolution than those produced by the maximum likelihood-expectation maximization method.