List - Mode Likelihood : EM Algorithm
نویسندگان
چکیده
| Using a theory of list-mode Maximum Likelihood (ML) source reconstruction presented recently by Bar-rett et al. 1], this paper formulates a corresponding Expectation Maximization (EM) algorithm, as well as a method for estimating noise properties at the ML estimate. List-mode ML is of interest in cases where the dimensionality of the measurement space impedes a binning of the measurement data. It can be advantageous in cases where a better forward model can be obtained by including more measurement coordinates provided by a given detector. Different gures of merit for the detector performance can be computed from the Fisher information matrix. This paper uses the observed Fisher information matrix, which requires a single data set, thus avoiding costly ensemble statistics. The proposed techniques are demonstrated for an idealized two-dimensional Positron Emission Tomography (2D PET) detector. We compute from simulation data the improved image quality obtained by including the time of ight of the coincident quanta.
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