Statistical stopping criteria for iterative maximum likelihood reconstruction of emission images

Abstract

Emission tomography involves the reconstruction of images representing the radionudide concentration throughout a patient's body. Maximum likelihood estimation yields excessively noisy images due to the non-uniqueness of the solution and the noise in the emission data. Many investigators have noted that better image reconstructions are obtained by stopping an iterative maximum likelihood algorithm early, well before convergence. This paper explains why better results can be obtained by suchan approach. A statistical criterion for determining an optimal stopping point developed in an earlier paper is compared with two statistics derived and applied here.