Synergetically generalized expectation maximization algorithm for ECT

A synergetically generalized expectation maximization (SGEM) reconstruction algorithm is proposed, which builds on the expectation maximization approach to maximize the data likelihood, but also takes additional account of multinomial probability models. The authors show how the maximum likelihood expectation maximization algorithm, transformed to its additive form, can be extended by several further additive terms to improve lesion contrast and smoothness of the reconstructed image. Based upon locally correlated Markov random field priors in the form of Gibbs functions, Bayesian reconstruction for maximum a posteriori estimation is applied to include prior models of isotope concentration. For simultaneous image reconstruction and segmentation, a mixture model for clustering of the data is applied in which mixture parameters are recalculated for each iteration. Additional filtering of the data during the expectation maximization process can be done using well-known models from filter theory. The authors describe the application of a nonlinear inhibition filter which is used by the human visual system. The method is illustrated by applications to data from SPECT scans.