[Kinetic cluster and α-divergence-based dynamic myocardial factorial analysis of positron-emission computed tomography images].

Objective: We purpose a novel factor analysis method based on kinetic cluster and α-divergence measure for extracting the blood input function and the time-activity curve of the regional tissue from dynamic myocardial positron emission computed tomography(PET) images.

Methods: Dynamic PET images were decomposed into initial factors and factor images by minimizing the α-divergence between the factor model and actual image data. The kinetic clustering as a priori constraint was then incorporated into the model to solve the nonuniqueness problem, and the tissue time-activity curves and the tissue space distributions with physiological significance were generated.