List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET

Lucas Parra, Harrison H. Barrett

Research output: Contribution to journalArticle

254 Scopus citations

Abstract

Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al. [1], this paper formulates a corresponding expectationmaximization (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 figures of merit for the detector performance can be computed from the Fisher 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-dimensional (2-D) positron emission tomography (PET) [2-D PET] detector. We compute from simulation data the improved image quality obtained by including the time of flight of the coincident quanta.

Original languageEnglish (US)
Pages (from-to)228-235
Number of pages8
JournalIEEE Transactions on Medical Imaging
Volume17
Issue number2
DOIs
StatePublished - Jan 1 1998
Externally publishedYes

Keywords

  • Em algorithm
  • List-mode data
  • Maximumlikelihood
  • Pet reconstruction
  • Time-of-flight pet

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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