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 positronemission computed tomography images

目的建立一种基于动力学聚类与α散度测度的因子分析方法,分析从动态心肌PET图像中无创地提取血输入函数及局部组织的时间活度曲线。方法通过最小化动态图像与因子模型间的α散度将动态PET图像做初步的分解,得到初始因子与因子图像,然后联合PET像素动力学聚类的先验信息解决因子分析模型中解的非唯一性问题,将初始因子与因子图像通过空间变换生成具有生理意义的组织活度曲线及组织空间分布。结果与传统的最小二乘法测度和最小化因子图像间重叠程度约束模型相比,本模型对噪声的敏感性较低,提取出的结果的精确性较高。结论通过选取最优的α值作为因子分析模型的测度,并引入PET图像像素的动力学聚类信息,能精确地获得血输入函数及局部组织的时间活度曲线,在视觉评价及量化评价均具有优质表现。

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 ^(82)RbPET 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.