Application of expectation maximization algorithms for image resolution improvement in a small animal PET system

Pietro Antich, Robert Parkey, Serguei Seliounine, Nikolai Slavine, Edward Tsyganov, Alexander Zinchenko

Research output: Contribution to journalArticle

17 Scopus citations


Modern positron emission tomography (PET) systems, offering high counting rate capabilities, high sensitivity, and near-submillimeter coordinate resolution, require fast image reconstruction software that can operate on list-mode data and take into account most of finite resolution effects such as photon scattering, positron range in tissue, and detector features. It has already been demonstrated that the expectation maximization (EM) method with extended system matrix modeling looks very attractive for image resolution recovery in PET imaging studies. In this paper, the performance of EM-based algorithms (in particular, their ability to improve the image resolution) is evaluated for a small animal PET imager with several phantoms. The achievement of a substantial decrease in processing time using an EM deblurring procedure is shown, as is an approach to successfully treat what are essentially nonspace-invariant resolution effects within a shift-invariant model.

Original languageEnglish (US)
Pages (from-to)684-690
Number of pages7
JournalIEEE Transactions on Nuclear Science
Issue number3 I
Publication statusPublished - Jun 2005



  • Iterative image reconstruction
  • Positron emission tomography (PET)
  • Small animal imaging

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Nuclear Energy and Engineering

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