Analysis of penalized likelihood reconstruction for PET kinetic quantification

Guobao Wang, Jinyi Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Quantification of tracer kinetics using dynamic positron emission tomography provides important information for understanding the physiological and biochemical processes in humans and animals. The 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 squares 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 also been developed to use the theoretical formulae to guide the selection of the regularization parameter in dynamic PET image reconstruction.

Original languageEnglish (US)
Title of host publicationIEEE Nuclear Science Symposium Conference Record
Number of pages11
StatePublished - 2007
Event2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC - Honolulu, HI, United States
Duration: Oct 27 2007Nov 3 2007


Other2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC
Country/TerritoryUnited States
CityHonolulu, HI


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

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering


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