LIST-MODE LIKELIHOOD - EM ALGORITHM AND IMAGE QUALITY ESTIMATION DEMONSTRATED ON 2-D PET

Using a theory of list-mode maximum-likelihood (ML) source reconstruct ion presented recently by Barrett et al. [1], this paper formulates a corresponding expectation-maximization (EM) algorithm, as well as a me thod for estimating noise properties at the ML estimate, List-mode ML is of interest in cases where the dimensionality of the measurement sp ace impedes a binning of the measurement data. It can be advantageous in cases where a better forward model can be obtained by including mor e measurement coordinates provided by a given detector. Different figu res of merit for the detector performance can be computed from the Fis her information matrix (FIM). This paper uses the observed FIM, which requires a single data set, thus, avoiding costly ensemble statistics, The proposed techniques are demonstrated for an idealized two-dimensi onal (2-D) positron emission tomography (PET) [2-D PET] detector. We c ompute from simulation data the improved image quality obtained by inc luding the time of flight of the coincident quanta.