Development of the algorithm for quantitative neuroreceptor imaging using PET without serial arterial blood sampling and reference region

开发使用 PET 进行定量神经感受器成像的算法，无需连续动脉血采样和参考区域

基本信息

批准号：
18591373
负责人：
KIMURA Yuichi
金额：
$ 2.53万
依托单位：
National Institute of Radiological Sciences (2007)Tokyo Metropolitan Institute of Gerontology (2006)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18591373/
关键词：
PET molecular imaging neuroreceptor medical image nuclear medicine 医用画像処理

项目摘要

This study aims at developing algorithms for fully quantitative neuroreceptor imaging using PET without serial arterial blood sampling and reference regions. For omission of serial arterial blood sampling, variational Bayesian approach and an intersectional searching approach were investigated based on Logan graphical analysis (LGA). As a result, dynamic studies using FDG, MPDX and TMSX were accomplished without serial arterial blood sampling. The variational Bayesian approach introduces nonnegative constraints to an algorithm based on independent component analysis which has been developed by my group. The approaches improved an estimation performance of a time history of radioactivity in arterial blood (pTAC), and it made an estimated blood volume image, which was another outcome of the algorithm, physiologically reliable. Other algorithm based on LGA is to estimate pTAC using the operational equation of LGA, for which the terms including pTAC were canceled out using pTAC is common among regions. This algorithm was sensitive for noise in PET data due to its mathematical framework. In order to realize reliable pTAC estimation, some data preprocessing procedures were developed using clustering based on a kinetics of administered radiopharmaceutical For the omission of reference region, a feasibility of voxel-based compartment model analysis was investigated. However, it was not practical due to bad noise statistics in voxel-based PET data. Then, total distribution volumes (VT) was estimated for each voxels to decrease the number of estimated rate constants using LGA. The VT estimates by LGA is underestimated under the existence of large noise in PET data. MAP based estimation approach was incorporated with LGA, and fast and statistically reliable voxel-based VT estimation was archived.

本研究旨在开发使用 PET 进行完全定量神经感受器成像的算法，无需连续动脉血采样和参考区域。为了省略连续动脉血采样，基于洛根图形分析（LGA）研究了变分贝叶斯方法和交叉搜索方法。因此，无需连续动脉血采样即可完成使用 FDG、MPDX 和 TMSX 的动态研究。变分贝叶斯方法向我的小组开发的基于独立成分分析的算法引入了非负约束。这些方法提高了动脉血放射性时间历程 (pTAC) 的估计性能，并且使估计的血量图像（该算法的另一个结果）在生理上可靠。基于LGA的其他算法是使用LGA的运算方程来估计pTAC，其中使用pTAC消除包括pTAC的项在区域之间是常见的。由于其数学框架，该算法对 PET 数据中的噪声很敏感。为了实现可靠的 pTAC 估计，使用基于施用放射性药物动力学的聚类开发了一些数据预处理程序。为了省略参考区域，研究了基于体素的室模型分析的可行性。然而，由于基于体素的 PET 数据中的噪声统计数据不佳，该方法并不实用。然后，估计每个体素的总分布体积 (VT)，以减少使用 LGA 估计的速率常数的数量。在 PET 数据存在较大噪声的情况下，LGA 估计的 VT 被低估。基于 MAP 的估计方法与 LGA 相结合，并归档了快速且统计可靠的基于体素的 VT 估计。