Likelihood maximization for list-mode emission tomographic image reconstruction.
The maximum a posteriori (MAP) Bayesian iterative algorithm using priors that are gamma distributed, due to Lange, Bahn and Little, is extended to include parameter choices that fall outside the gamma distribution model. Special cases of the resulting iterative method include the expectation maximization maximum likelihood (EMML) method based on the Poisson model in emission tomography, as well as algorithms obtained by Parra and Barrett and by Huesman et al. that converge to maximum likelihood and maximum conditional likelihood estimates of radionuclide intensities for list-mode emission tomography. The approach taken here is optimization-theoretic and does not rely on the usual expectation maximization (EM) formalism. Block-iterative variants of the algorithms are presented. A self-contained, elementary proof of convergence of the algorithm is included.
Byrne
C
C
Department of Mathematical Sciences, University of Massachusetts at Lowell, 01854, USA. Charles_Byrne@uml.edu
D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008432:Mathematical Computing; D008962:Models, Theoretical; D016012:Poisson Distribution; D014055:Tomography, Emission-Computed; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*