FDOPA kinetics analysis in PET images for Parkinson´s disease diagnosis by use of particle swarm optimization

Positron emission tomography (PET), with many kinds of radioactive tracers, have been used widely for molecular imaging. In order to retrieve useful information and render a diagnosis from measured PET images, the compartment models that originated from the area of pharmacokinetics have been employed extensively for data analysis. The unknown parameters in the models are usually solved by use of curve-fitting approaches. However, the accuracy of the fitting methods are usually below satisfactory level when there exist a large number of unknown parameters. As a result, some small-valued parameters are often neglected to reduce the number of unknowns. But such a reduction can lead a loss of some important information. In this work, we propose the particle swarm optimization (PSO) method to analyze the modeling problems with a better efficacy and tested our method by solving the ¹⁸F Fluoro-3, 4-dihydroxyphenyl-l-alanine (FDOPA) kinetic model in Parkinson´s disease diagnosis.