Application of pharmacokinetic models to projection data in positron emission tomography

In positron emission tomography (PET), coincidence detection of annihilation photons enables the measurement of radon transforms of the instantaneous activity concentration of labeled tracers in the human body. Using reconstruction algorithms, spatial maps of the activity distribution can be created and analyzed to reveal the pharmacokinetics of the labeled tracer. This thesis considers the possibility of applying pharmacokinetic modeling to the count rate data measured by the detectors, rather than reconstructed images. A new concept is proposed—parameter projections—radon transforms of the spatial distribution of the parameters of the model, which simplifies the problem considerably. Using this idea, a general linear least squares (GLLS) framework is developed and applied to the one and two tissue‐compartment models for [O‐15]water and [F‐18]FDG. Simulation models are developed from first principles to demonstrate the accuracy of these methods, requiring the validation of novel‐compartment based whole body models for [O‐15]water and [F‐18]FDG. Further, it is demonstrated that the variances of maps of the spatial variance of the parameters of the model, i.e., parametric images, can be calculated in projection space. It is clearly shown that the precision of the variance‐ and statistical parametric‐maps is higher than that obtained from estimates based on reconstructed images. Parameter projections permit faster analysis of results, avoiding lengthy reconstruction of large data sets, and allow access to robust statistical techniques for activation analysis through use of the known, Poisson distributed nature, of the measured projection data. [Measurements and analysis of thesis data carried out during the authors appointment at the PET Program, Paul Scherrer Institute, CH‐5232 Villigen PSI, Switzerland.]