High speed algorithm for statistically reliable parametric image in positron emission tomography without continuous arterial blood sampling

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Voxel-based kinetic analysis in PET can visualize various functionalities in a living tissue. Major drawbacks in formation of an parametic image are bad noise statistics in voxel-based PET data. A large amount of voxels in PET causes a huge calculation time, and it makes formation of a parametric image inpractical. The statistical model-based clustering algorithm of CAKS (Clustering Analysis for Kinetics) is proposed to categorize voxels whose PET data has a similar shape and has similar functionalities. In CAKS, PET data was projected in a feature space and mixture Gaussian model was ulitized. As results, a parametric image can be formed in 30 minutes and the estimates were correspond to the estimates by an ordinal ROI-based estimation. Arterial blood sampling is other problem to apply CAKS in a clinical situation. The spatial distribution of tissue and blood vessel in a brain is distinguishable, and statistical algorithm for a blind separation of independent component analysis was applicable. After specialy designed data preprocessing schemes to emphasize the difference between spatial distribution of vessel and tissue, a proposed algorithm can estimate time course of activity in arterial blood and it corresponded to a measured time activity curve.