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为了研究和评价用68GaEDTA动态PET研究和评价脑肿瘤定量分析的可靠性和灵敏度,我们在本文中提出了估计容积分布(distributionvolume:DV)和血脑屏障渗透率(k1)分布的线性参数成像模型。我们还用F统计学方法实现了把肿瘤从正常组织中分割出来的方法。用一个三参数双腔室模型描述用PET测量的数据。用于估计DV(=K1/k2)和K1的主要计算公式为:Cpet=(K1+k2Vp)t0∫Cpds-k2t0∫Cpetds+VpCp和t0∫Cpetds=(DV+Vp)t0∫Cpds-(1/k2)Cpet+(Vp/k2)Cp,这里的k2是脑内通过血脑屏障到脑外的渗透率。在参数成像中我们采用了一个可靠和如棒的基于像素的局域线性回归算法用于产生DV和K1图像。同样基于像素自由度为2和k-2的F统计学方法采用的计算公式为:F=(((k-2)k/(2(k2-1)))D2,这里的D2=(x-μ)’S-1(x-μ),而μ和S分别表示脑肿瘤对侧正常区域内采集的样品的平均值和协方差,这些样品是按照二维空间狖(K1,DV)狚采集的,而常数k是取样的总数。在不同水平上阈值α,就可以得到不同置信度下的F统计图像。用这个方法对11个肿瘤病人进行了68GaEDTA动态PET研究。研究结果表明:所有的DV,K1和F图像的质量都很好。而且用于产生DV,K1和不同置信度下的F图像的算法效率很高,容易实现。本研究方法中研究和发展的方法提供了一个有效的集成多维生?
To investigate and evaluate the reliability and sensitivity of 68GaEDTA dynamic PET in the quantitative analysis of brain tumors, we proposed a linear parametric imaging model that estimates distributionvolume (DV) and blood-brain barrier permeability (k1) distributions . We also used the F-statistic method to separate the tumor from the normal tissue. A three-parameter, two-chamber model is used to describe the data measured with PET. The main formula used to estimate DV (= K1 / k2) and K1 is: Cpet = (K1 + k2Vp) t0∫Cpds-k2t0∫Cpetds + VpCp and t0∫Cpetds = (DV + Vp) t0∫Cpds- (1 / k2) Cpet + (Vp / k2) Cp, where k2 is the rate of brain penetration through the blood-brain barrier in the brain. In parametric imaging we used a robust and robust pixel-based local linear regression algorithm for generating DV and K1 images. Also based on the F-statistic method for pixel degrees of freedom of 2 and k-2, the formula used is: F = (((k-2) k / (2 (k2-1))) D2 where D2 = -μ) ’S-1 (x-μ), and μ and S denote the mean and covariance of samples collected in the normal contralateral brain regions, respectively. These samples are plotted in terms of two-dimensional space 狖 (K1, DV) , And the constant k is the total number of samples. The F-statistic images with different confidence levels can be obtained at different levels of the threshold value α.Using this method, 68GaEDTA dynamic PET studies of 11 tumor patients were carried out.The results show that: all The quality of DV, K1 and F images are very good, and the algorithm for generating DV, K1 and F images with different confidence levels is very efficient and easy to implement.The methods researched and developed in this method provide an effective Integrated multi-dimensional life?