Revisiting stopping rules for iterative methods used in emission tomography

Hongbin Guo, Rosemary Renaut

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The expectation maximization algorithm is commonly used to reconstruct images obtained from positron emission tomography sinograms. For images with acceptable signal to noise ratios, iterations are terminated prior to convergence. A new quantitative and reproducible stopping rule is designed and validated on simulations using a Monte-Carlo generated transition matrix with a Poisson noise distribution on the sinogram data. Iterations are terminated at the solution which yields the most probable estimate of the emission densities while matching the sinogram data. It is more computationally efficient and more accurate than the standard stopping rule based on the Pearson's χ2 test.

Original languageEnglish (US)
Pages (from-to)398-406
Number of pages9
JournalComputerized Medical Imaging and Graphics
Volume35
Issue number5
DOIs
StatePublished - Jul 1 2011

Keywords

  • Expectation maximization
  • Image reconstruction
  • PET
  • Stopping rule
  • U

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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