ROBUST RECONSTRUCTION OF PHYSIOLOGICAL PARAMETERS FROM DYNAMIC PET DATA

The primary goal of dynamic positron emission tomography (PET) is to quantify the physiological and biological processes through tracer kinetics analysis. However, accurate quantification of such functional processes with PET is difficult to achieve because of the fundamental difficulties related to uncertainties in the imaging system and the measurement data. We introduce a novel and efficient strategy whereby compartmental tracer model parameters can be identified based on the H _infin principles. The system equation is constructed from particular tracer kinetic models, with the number and relationship between tissue compartments dictated by the physiological and biochemical properties of the process under study. And the observation equation on measurement data is formed based on the PET imaging mechanism. Once we have the state space representation for the PET dynamic system, a robust system identification paradigm is adopted to estimate the tracer kinetics parameters from PET sinogram data directly. It is derived and extended from the H_infin filtering principles and is particularly powerful for real-world situations where the types and levels of the disturbances are unknown. Specifically, we show the results of applying this strategy to synthetic phantom data for accuracy assessment