High speed algorithm for statistically reliable parametric image in positron emission tomography without continuous arterial blood sampling

PET中基于体素的动力学分析可以可视化活组织中的各种功能。参数图像形成的主要缺点是基于体素的PET数据中的不良噪声统计。PET中大量的体素导致巨大的计算时间，并且其使得形成参数化图像不切实际。提出了一种基于统计模型的动态聚类分析算法CAKS（Clustering Analysis for Kinetics），用于对PET数据中具有相似形状和相似功能的体素进行分类。在CAKS中，PET数据在特征空间中进行投影，并利用混合高斯模型。结果，可以在30分钟内形成参数图像，并且估计值与基于有序ROI的估计值相对应。动脉血采样是在临床情况下应用CAKS的另一个问题。脑内组织和血管的空间分布是可区分的，独立成分分析的盲分离统计算法是适用的。经过专门设计的数据预处理方案，强调血管和组织的空间分布之间的差异，所提出的算法可以估计动脉血液中的活动的时间过程，它对应于测量的时间活动曲线。

Voxel-based kinetic analysis in PET can visualize various functionalities in a living tissue. Major drawbacks in formation of an parametic image are bad noise statistics in voxel-based PET data. A large amount of voxels in PET causes a huge calculation time, and it makes formation of a parametric image inpractical. The statistical model-based clustering algorithm of CAKS (Clustering Analysis for Kinetics) is proposed to categorize voxels whose PET data has a similar shape and has similar functionalities. In CAKS, PET data was projected in a feature space and mixture Gaussian model was ulitized. As results, a parametric image can be formed in 30 minutes and the estimates were correspond to the estimates by an ordinal ROI-based estimation. Arterial blood sampling is other problem to apply CAKS in a clinical situation. The spatial distribution of tissue and blood vessel in a brain is distinguishable, and statistical algorithm for a blind separation of independent component analysis was applicable. After specialy designed data preprocessing schemes to emphasize the difference between spatial distribution of vessel and tissue, a proposed algorithm can estimate time course of activity in arterial blood and it corresponded to a measured time activity curve.