On the undecidability among kinetic models: From model selection to model averaging

Federico E. Turkheimer, Rainer Hinz, Vincent J. Cunningham

    Research output: Contribution to journalArticlepeer-review


    This article deals with the problem of model selection for the mathematical description of tracer kinetics in nuclear medicine. It stems from the consideration of some specific data sets where different models have similar performances. In these situations, it is shown that considerate averaging of a parameter's estimates over the entire model set is better than obtaining the estimates from one model only. Furthermore, it is also shown that the procedure of averaging over a small number of "good" models reduces the "generalization error," the error introduced when the model selected over a particular data set is applied to different conditions, such as subject populations with altered physiologic parameters, modified acquisition protocols, and different signal-to-noise ratios. The method of averaging over the entire model set uses Akaike coefficients as measures of an individual model's likelihood. To facilitate the understanding of these statistical tools, the authors provide an introduction to model selection criteria and a short technical treatment of Akaike's information-theoretic approach. The new method is illustrated and epitomized by a case example on the modeling of [11C]flumazenil kinetics in the brain, containing both real and simulated data.
    Original languageEnglish
    Pages (from-to)490-498
    Number of pages8
    JournalJournal of Cerebral Blood Flow and Metabolism
    Issue number4
    Publication statusPublished - 1 Apr 2003


    • Akaike information coefficient
    • Akaike weights
    • Kinetic modeling
    • Model averaging
    • Model selection
    • PET


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