南方医科大学学报2017，Vol.37Issue(12)：1577-1584,8.DOI:10.3969/j.issn.1673-4254.2017.12.03
基于动力学聚类与α散度测度的动态心肌PET图像因子分析
Kinetic cluster and α-divergence-based dynamic myocardial factorial analysis of positron-emission computed tomography images
摘要
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.关键词
因子分析/α散度/正电子发射计算机断层显像/动力学聚类Key words
factor analysis/α-divergence/positron emission computed tomography/kinetic cluster引用本文复制引用
王沛沛,路利军,曹双亮,李华勇,陈武凡..基于动力学聚类与α散度测度的动态心肌PET图像因子分析[J].南方医科大学学报,2017,37(12):1577-1584,8.基金项目
国家自然科学基金(81501541,61628105) （81501541,61628105）
广东省自然科学基金(2014A030310243,2016A030313577) （2014A030310243,2016A030313577）
国家重点研发计划(2016YFC0104003) （2016YFC0104003）
广州市珠江科技新星专项(201610010011)Supported by National Natural Science Foundation of China(81501541, 61628105). （201610010011）
