Karolinska Institutet, Department of Clinical Neuroscience, Psychiatry Section, SE-171 76, Stockholm, Sweden.
Neuroimage. 2013 Apr 1;69:78-86. doi: 10.1016/j.neuroimage.2012.12.009. Epub 2012 Dec 14.
[(18)F]FEDAA1106 is expected to be used for evaluating the regional density of the peripheral benzodiazepine receptor (also called TSPO) in several neurodegenerative disorders. Regarding the quantification, direct binding potential (BP(ND)) has been reported to be preferable because of the variation of nondisplaceable distribution volume (V(ND)) among individuals. However, the precise calculation of BP(ND) is difficult in small regions or at voxel levels due to noise. Recently, a new graphical analysis (GA) was proposed to estimate V(ND) in a direct way. In this paper, we evaluated two types of GA for reliable quantification of BP(ND) in PET study with [(18)F]FEDAA1106 using computer simulations and human data.
In the simulations, time-activity curves were generated with various rate constants and noise levels, and the errors of BP(ND) estimated by GA were analyzed by comparing with true values calculated from rate constants given for the simulations. Thereafter, in a human study with [(18)F]FEDAA1106 for healthy volunteers, BP(ND) was estimated by two types of GA for region-of-interest (ROI) data. Parametric images of BP(ND) were generated by two types of GA with or without wavelet-denoising.
Simulations showed that BP(ND) by GA was well correlated with true values, despite an underestimation. GA reduced unreasonable estimates compared with a conventional nonlinear least-square fitting (NLS), although larger variation of BP(ND) estimates was observed. In a ROI-based analysis of data obtained in a human study, BP(ND)s estimated by GA were well correlated with those generated by NLS, though they were underestimated. Parametric BP(ND) images by GA could be improved with wavelet-denoising.
Graphical analysis could provide BP(ND) values with high stability and simple calculation in both ROI-based and voxel-based analyses of [(18)F]FEDAA1106 data.
[(18)F]FEDAA1106 预计将用于评估几种神经退行性疾病中周边苯二氮䓬受体（也称为 TSPO）的区域密度。关于定量，由于个体间不可置换分布容积（V（ND））的变化，直接结合潜力（BP（ND））已被报道为更可取。然而，由于噪声，在小区域或体素水平上准确计算 BP（ND）是困难的。最近，提出了一种新的图形分析（GA）方法来直接估计 V（ND）。在本文中，我们使用计算机模拟和人体数据评估了两种类型的 GA 用于可靠量化 [(18)F]FEDAA1106 PET 研究中的 BP（ND）。
在模拟中，使用各种速率常数和噪声水平生成时间-活性曲线，并通过比较与模拟中给出的速率常数计算的真实值来分析 GA 估计的 BP（ND）的误差。此后，在一项使用 [(18)F]FEDAA1106 的健康志愿者的人体研究中，通过两种类型的 GA 对感兴趣区（ROI）数据进行了 BP（ND）的估计。通过两种类型的 GA 生成了具有或不具有小波去噪的 BP（ND）参数图像。
尽管存在低估，但模拟表明 GA 中的 BP（ND）与真实值很好相关。与传统的非线性最小二乘拟合（NLS）相比，GA 减少了不合理的估计，尽管 BP（ND）估计的变化更大。在人体研究中获得的数据的 ROI 分析中，GA 估计的 BP（ND）与 NLS 生成的 BP（ND）很好相关，尽管它们被低估了。GA 的参数 BP（ND）图像可以通过小波去噪得到改善。
图形分析可以在 [(18)F]FEDAA1106 数据的 ROI 基础和体素基础分析中提供高稳定性和简单计算的 BP（ND）值。
