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.

这项研究旨在开发用于使用无串行动脉抽血和参考区域的PET进行完全定量神经受体成像的算法。为了根据洛根图形分析（LGA）研究了省略动脉抽血的省略，变异贝叶斯方法和交叉搜索方法进行了研究。结果，使用FDG，MPDX和TMSX的动态研究是在没有串行动脉血液采样的情况下完成的。变分贝叶斯方法基于我的小组开发的独立组件分析，将非负约束引入算法。该方法改善了动脉血液（PTAC）放射性时间史的估计表现，并做出了估计的血量图像，这是该算法的另一个结果，生理上可靠。基于LGA的其他算法是使用LGA的操作方程来估算PTAC，在该方程中，使用PTAC将包括PTAC在内的术语在该区域中很常见。由于其数学框架，该算法对PET数据中的噪声非常敏感。为了实现可靠的PTAC估计，使用基于施用放射性药物的动力学来开发了一些数据预处理程序，用于省略参考区域，研究了基于素的隔室模型分析的可行性。但是，由于基于体素的PET数据中的噪声统计不佳，这是不切实际的。然后，估计每个体素的总分布量（VT），以减少使用LGA的估计速率常数数量。在PET数据中存在大噪声下，LGA的VT估计值被低估了。基于地图的估计方法与LGA合并，并存档了基于快速和统计上可靠的体素VT估计。