Analysis of penalized likelihood image reconstruction for dynamic PET quantification

Guobao Wang, Jinyi Qi

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Quantification of tracer kinetics using dynamic positron emission tomography (PET) provides important information for understanding the physiological and biochemical processes in humans and animals. A common procedure is to reconstruct a sequence of dynamic images first, and then apply kinetic analysis to the time activity curve of a region of interest derived from the reconstructed images. Obviously, the choice of image reconstruction method and its parameters affect the accuracy of the time activity curve and hence the estimated kinetic parameters. This paper analyzes the effects of penalized likelihood image reconstruction on tracer kinetic parameter estimation. Approximate theoretical expressions are derived to study the bias, variance, and ensemble mean squared error of the estimated kinetic parameters. Computer simulations show that these formulae predict correctly the changes of these statistics as functions of the regularization parameter. It is found that the choice of the regularization parameter has a significant impact on kinetic parameter estimation, indicating proper selection of image reconstruction parameters is important for dynamic PET. A practical method has been developed to use the theoretical formulae to guide the selection of the regularization parameter in dynamic PET image reconstruction.

Original languageEnglish (US)
Article number4781573
Pages (from-to)608-620
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume28
Issue number4
DOIs
StatePublished - Apr 2009

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Keywords

  • Image reconstruction
  • Noise analysis
  • Penalized maximum likelihood
  • Tracer kinetic modeling

ASJC Scopus subject areas

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

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