ABSTRACT
Nuclear imaging technologies produce non-invasive measures of a broad range of physiological functions, using externally detected electromagnetic radiation originating from radiopharmaceuticals administered to the subject. Kinetic models for Positron emission tomography (PET) typically derive from the one-, two-, or three-compartment model with a model input function. The scarcity of data in kinetic modelling lends itself naturally to Bayesian modelling, where inclusion of priors can provide better estimates. Development of neurochemical assays that capture temporal signatures is critical because the neurotransmitter dynamics may encode both normal and abnormal cognitive or behavioural functions in the brain. The spline smoothed data can be considered as sample means at each data point and should be nearly sufficient for the parameters of interest. This chapter generates simulated data using neurotransmitter PET (ntPET), and the model linear parametric-neurotransmitter PET (lp-ntPET) to the simulated data, since the latter is a simplification of the former. It considers the application of ABC to the problem of neurotransmitter response modelling.
