[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.

Results: The model was applied to the 82RbPET myocardial perfusion simulation data and compared with the traditional model-based least squares measure and the minimal spatial overlap constraint. The experimental results showed that the proposed model performed better than the traditional model in terms of both accuracy and sensitivity.

Conclusion: This method can select the optimal measure by α value, and incorporate the prior information of the kinetic clustering of PET image pixels to obtain the accurate time-activity curves of the tissue, which has shown good performance in visual evaluation and quantitative evaluation.