Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2018-06-15 Cooperative journals: 《南方医科大学学报》
Abstract： 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.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2018-01-25 Cooperative journals: 《南方医科大学学报》
Abstract： 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,whichhasshowngoodperformanceinvisualevaluationandquantitativeevaluation.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2017-12-07 Cooperative journals: 《南方医科大学学报》
Abstract： Objective To propose a new method for dynamic positron emission tomographic (PET) image reconstruction using low rank and sparse penalty (L&S). Methods The L&S reconstruction model was established and the split Bregman method was used to solve the optimal cost function. The one-tissue compartment model was used to simulate a set of PET 82Rb myocardial perfusion image. The L&S reconstruction method was compared with maximum likelihood expectation maximization (MLEM) method, low-rank penalty method and sparse penalty method. Results The L&S reconstruction method had the smallest MSE and well maintained the feature information. The polar map created by L&S method was the most similar with the reference actual polar map. Conclusion L&S reconstruction method is better than the other three methods in both visual and quantitative analysis of the PET images.
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