Vector Extrapolated Fast Maximum Likelihood Estimation Algorithm For Emission Tomography

A new class of fast maximum likelihood estimation (MLE) algorithms for emission computed tomography (ECT) is developed. In these cyclic iterative algorithms, vector extrapolation techniques are integrated with the iterations in gradient-based MLE algorithms, with the objective of accelerating the convergence of the base iterations. This results in a substantial reduction in the effective number of base iterations required for obtaining an emission density estimate of specified quality. The mathematical theory behind the minimal polynomial and reduced rank vector extrapolation techniques, in the context of emission tomography, is presented. With the EM and EM search algorithms in the base iterations, these extrapolation techniques are implemented in a positron emission tomography system. Using computer experiments, with measurements taken from simulated phantoms, the new algorithms are evaluated. It is shown that, with minimal additional computations, the proposed approach results in substantial improvement in reconstruction, in terms of both qualitative visual performance and quantitative measures of likelihood and residual error, of the image