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

无需连续动脉血采样的正电子发射断层扫描中统计可靠参数图像的高速算法

• 批准号: 13670977
• 负责人: KIMURA Yuichi
• 金额: $ 2.62万
• 依托单位: Tokyo Metropolitan Institute of Gerontology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13670977/
• 关键词: PET; arterial blood sampling; compartment model; kinetic analysis; clustering; independent component analysis; mixture Gaussian model; nuclear medicine

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.

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。形成参数图像的主要缺点是基于体素的 PET 数据中的噪声统计较差。 PET中大量的体素导致巨大的计算时间，并且使得参数化图像的形成不切实际。提出了基于统计模型的 CAKS（动力学聚类分析）聚类算法，对 PET 数据具有相似形状和功能的体素进行分类。在CAKS中，PET数据被投影到特征空间中并利用混合高斯模型。结果，参数化图像可以在 30 分钟内形成，并且估计值与基于 ROI 的有序估计值相对应。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具有可区分性，适用于独立成分分析的盲分离统计算法。经过专门设计的数据预处理方案以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活动的时间过程，并与测量的时间活动曲线相对应。

项目成果

Mika Naganawa, Yuichi Kimura, and Ayumu Matani: "Modification of ICA for Eatracting Blood Vessel-related Component in Nuclear Medicine: Contrast Function and Nonnegative Constraints"ICA2003. (2003)

Mika Naganawa、Yuichi Kimura 和 Ayumu Matani：“核医学中用于吸引血管相关成分的 ICA 的修改：对比函数和非负约束”ICA2003。

Yuichi Kimura, Kenji Ishii, Keiichi Oda, et al.: "Imaging Glucose Metabolism of Brain using PET and Clustering Analysis for Kinetics"Second Joint EMBS-EMBS Conference 2002. 5.1.3-5.1.6 (2002)

Yuichi Kimura、Kenji Ishii、Keiichi Oda 等人：“使用 PET 成像脑葡萄糖代谢和动力学聚类分析”第二届联合 EMBS-EMBS 会议 2002 年。 5.1.3-5.1.6 (2002)

Y Kimura, K Oda, K Ishii, and et. al.: "Formation Of Parametric Images In Positron Emission Tomography Using A Clustering-based Kinetic Analysis With Statistical Clustering"Proc. on IEEE EMBS01. No.543. (2001)

Y Kimura、K Oda、K Ishii 等。

Yuichi Kimura, Kenji Ishii, Keiichi Oda, et al.: "Imaging Glucose Metabolism of Brain using PET and Clustering Analysis for Kinetics"Second Joint EMBS-EMBS Conference 2002. 5.1.3-6 (2002)

Yuichi Kimura、Kenji Ishii、Keiichi Oda 等人：“使用 PET 成像脑葡萄糖代谢和动力学聚类分析”第二届联合 EMBS-EMBS 会议 2002. 5.1.3-6 (2002)

M Naganawa, A Matani, Y Kimura: "Extraction of Vessel-related Information From PET Images Without Continuous blood Sampling Using Modified Independent Component Analysis"Proc.on IEEE EMBS01. No.584. (2001)

M Naganawa、A Matani、Y Kimura：“使用改进的独立成分分析在不进行连续血液采样的情况下从 PET 图像中提取血管相关信息”Proc.on IEEE EMBS01。