Zhou Yun, Resnick Susan M, Ye Weiguo, Fan Hong, Holt Daniel P, Klunk William E, Mathis Chester A, Dannals Robert, Wong Dean F
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287-0807, USA.
Neuroimage. 2007 Jun;36(2):298-312. doi: 10.1016/j.neuroimage.2007.03.004. Epub 2007 Mar 16.
Reference tissue model (RTM) is a compartmental modeling approach that uses reference tissue time activity curve (TAC) as input for quantification of ligand-receptor dynamic PET without blood sampling. There are limitations in applying the RTM for kinetic analysis of PET studies using [11C]Pittsburgh compound B ([11C]PIB). For region of interest (ROI) based kinetic modeling, the low specific binding of [11C]PIB in a target ROI can result in a high linear relationship between the output and input. This condition may result in amplification of errors in estimates using RTM. For pixel-wise quantification, due to the high noise level of pixel kinetics, the parametric images generated by RTM with conventional linear or nonlinear regression may be too noisy for use in clinical studies.
We applied RTM with parameter coupling and a simultaneous fitting method as a spatial constraint for ROI kinetic analysis. Three RTMs with parameter coupling were derived from a classical compartment model with plasma input: an RTM of 4 parameters (R(1), k'(2R), k(4), BP) (RTM4P); an RTM of 5 parameters (R(1), k(2R), NS, k(6), BP) (RTM5P); and a simplified RTM (SRTM) of 3 parameters (R(1), k'(2R), BP) (RTM3P). The parameter sets [k'(2R), k(4)], [k(2R), NS, k(6)], and k'(2R) are coupled among ROIs for RTM4P, RTM5P, and RTM3P, respectively. A linear regression with spatial constraint (LRSC) algorithm was applied to the SRTM for parametric imaging. Logan plots were used to estimate the distribution volume ratio (DVR) (=1+BP (binding potential)) in ROI and pixel levels. Ninety-minute [11C]PIB dynamic PET was performed in 28 controls and 6 individuals with mild cognitive impairment (MCI) on a GE Advance scanner. ROIs of cerebellum (reference tissue) and 15 other regions were defined on coregistered MRIs.
The coefficients of variation of DVR estimates from RTM3P obtained by the simultaneous fitting method were lower by 77-89% (in striatum, frontal, occipital, parietal, and cingulate cortex) as compared to that by conventional single ROI TAC fitting method. There were no significant differences in both TAC fitting and DVR estimates between the RTM3P and the RTM4P or RTM5P. The DVR in striatum, lateral temporal, frontal and cingulate cortex for MCI group was 25% to 38% higher compared to the control group (p < or = 0.05), even in this group of individuals with generally low PIB retention. The DVR images generated by the SRTM with LRSC algorithm had high linear correlations with those from the Logan plot (R2 = 0.99).
In conclusion, the RTM3P with simultaneous fitting method is shown to be a robust compartmental modeling approach that may be useful in [11C]PIB PET studies to detect early markers of Alzheimer's disease where specific ROIs have been hypothesized. In addition, the SRTM with LRSC algorithm may be useful in generating R(1) and DVR images for pixel-wise quantification of [11C]PIB dynamic PET.
参考组织模型（RTM）是一种房室建模方法，它使用参考组织时间-活度曲线（TAC）作为输入，无需采血即可对配体-受体动态正电子发射断层显像（PET）进行定量分析。在将RTM应用于使用[11C]匹兹堡化合物B（[11C]PIB）的PET研究的动力学分析时存在局限性。对于基于感兴趣区域（ROI）的动力学建模，[11C]PIB在目标ROI中的低特异性结合可能导致输出与输入之间的高线性关系。这种情况可能会导致使用RTM估计时误差放大。对于逐像素定量，由于像素动力学的噪声水平较高，使用传统线性或非线性回归的RTM生成的参数图像可能噪声过大，无法用于临床研究。
我们将具有参数耦合和同时拟合方法的RTM作为空间约束应用于ROI动力学分析。从具有血浆输入的经典房室模型中推导了三种具有参数耦合的RTM：一种4参数RTM（R(1)、k'(2R)、k(4)、BP）（RTM4P）；一种5参数RTM（R(1)、k(2R)、NS、k(6)、BP）（RTM5P）；以及一种3参数简化RTM（SRTM）（R(1)、k'(2R)、BP）（RTM3P）。参数集[k'(2R)、k(4)]、[k(2R)、NS、k(6)]和k'(2R)分别在ROI之间进行耦合，用于RTM4P、RTM5P和RTM3P。将具有空间约束的线性回归（LRSC）算法应用于SRTM进行参数成像。使用Logan图在ROI和像素水平估计分布容积比（DVR）（=1+BP（结合潜能））。在GE Advance扫描仪上对28名对照者和6名轻度认知障碍（MCI）个体进行了90分钟的[11C]PIB动态PET检查。在配准的磁共振成像（MRI）上定义了小脑（参考组织）和其他15个区域的ROI。
与传统的单ROI TAC拟合方法相比，通过同时拟合方法获得的RTM3P的DVR估计变异系数在纹状体、额叶、枕叶、顶叶和扣带回皮质中降低了77-89%。RTM3P与RTM4P或RTM5P之间在TAC拟合和DVR估计方面均无显著差异。即使在这组PIB保留率普遍较低的个体中，MCI组纹状体、颞叶外侧、额叶和扣带回皮质的DVR也比对照组高25%至38%（p≤0.05）。使用LRSC算法的SRTM生成的DVR图像与Logan图生成的图像具有高度线性相关性（R2 = 0.99）。
总之，具有同时拟合方法的RTM3P被证明是一种稳健的房室建模方法，在使用[11C]PIB PET研究检测阿尔茨海默病早期标志物（其中已假设特定ROI）时可能有用。此外，具有LRSC算法的SRTM在生成用于[11C]PIB动态PET逐像素定量的R(1)和DVR图像方面可能有用。
