权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

本研究的目的是开发算法，完全定量神经受体成像使用PET没有连续动脉血液采样和参考区域。针对连续动脉血样本的遗漏，基于Logan图分析（LGA），研究了变分贝叶斯方法和交叉搜索方法。因此，使用FDG、MPDX和TMSX的动态研究无需连续动脉血液采样即可完成。变分贝叶斯方法引入了非负约束的算法的基础上独立成分分析，这已经开发了我的小组。该方法改善了动脉血中放射性时间历程（pTAC）的估计性能，并且其使得作为算法的另一结果的估计血容量图像在生理上可靠。基于LGA的其他算法是使用LGA的运算方程来估计pTAC，其中包括pTAC的项被使用pTAC在区域之间是共同的来抵消。由于其数学框架，该算法对PET数据中的噪声敏感。为了实现可靠的pTAC估计，一些数据的预处理程序，使用聚类的基础上管理的放射性药物的动力学。为省略参考区域，基于体素的房室模型分析的可行性进行了研究。然而，由于基于体素的PET数据中的不良噪声统计，这是不实用的。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数据中存在较大噪声的情况下，LGA估计的VT被低估。将基于MAP的估计方法与LGA相结合，实现了快速且统计可靠的基于体素的VT估计。