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Publication Detail
Improved Parameter-Estimation With MRI-Constrained PET Kinetic Modeling: A Simulation Study
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Erlandsson K, Liljeroth M, Atkinson D, Arridge S, Ourselin S, Hutton BF
  • Publication date:
  • Pagination:
    2464, 2470
  • Journal:
    IEEE Transactions on Nuclear Science
  • Volume:
  • Issue:
  • Status:
  • Print ISSN:
© 1963-2012 IEEE.Kinetic analysis can be applied both to dynamic PET and dynamic contrast enhanced (DCE) MRI data. We have investigated the potential of MRI-constrained PET kinetic modeling using simulated [18F]2-FDG data for skeletal muscle. The volume of distribution, Ve, for the extra-vascular extra-cellular space (EES) is the link between the two models: It can be estimated by DCE-MRI, and then used to reduce the number of parameters to estimate in the PET model. We used a 3 tissue-compartment model with 5 rate constants (3TC5k), in order to distinguish between EES and the intra-cellular space (ICS). Time-activity curves were generated by simulation using the 3TC5k model for 3 different Ve values under basal and insulin stimulated conditions. Noise was added and the data were fitted with the 2TC3k model and with the 3TC5k model with and without Ve constraint. One hundred noise-realisations were generated at 4 different noise-levels. The results showed reductions in bias and variance with Ve constraint in the 3TC5k model. We calculated the parameter k3″, representing the combined effect of glucose transport across the cellular membrane and phosphorylation, as an extra outcome measure. For k3″, the average coefficient of variation was reduced from 52% to 9.7%, while for k3 in the standard 2TC3k model it was 3.4%. The accuracy of the parameters estimated with our new modeling approach depends on the accuracy of the assumed Ve value. In conclusion, we have shown that, by utilising information that could be obtained from DCE-MRI in the kinetic analysis of [18F]2-FDG-PET data, it is in principle possible to obtain better parameter estimates with a more complex model, which may provide additional information as compared to the standard model.
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