A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework using an over-complete exponential basis set and sparse Bayesian learning. The technique is applicable to analyses requiring either a plasma or reference tissue input function and… (More)
Acknowledgements. We thank B. Charlesworth for input, counsel and support, and J. Greenberg, T. Morton and J. Mach for assistance with the manuscript.