A COMPUTATIONAL STUDY OF THE FOCUS-OF-ATTENTION EM-ML ALGORITHM FOR PET RECONSTRUCTION

The expectation-maximization maximum-likelihood (EM-ML) algorithm for image reconstruction in positron emission tomography (PET) essentially solves a large linear system of equations. In this paper, we study co mputational aspects of a recently developed preprocessing scheme for f ocusing the attention, and thus the computational resources, on a subs et of the equations and unknowns in order to reduce the storage, compu tation, and communication requirements of the EM-ML algorithm. The app roach is completely data-driven and uses no prior anatomic knowledge. The experimental results are obtained from runs on a small network of workstations using simulated phantom data as well as data obtained fro m a clinical ECAT 921 PET scanner. (C) 1998 Published by Elsevier Scie nce B.V. All rights reserved.