Linear regression with spatial constraint to generate parametric images of ligand-receptor dynamic PET studies with a simplified reference tissue model
Linear regression with spatial constraint to generate parametric images of ligand-receptor dynamic PET studies with a simplified reference tissue model
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DOI：
10.1016/s1053-8119(03)00017-x
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发表时间：
2003-04-01
期刊：
影响因子：
5.7
通讯作者：
Wong, DF
中科院分区：
文献类型：
--
作者：
Zhou, Y;Endres, CJ;Wong, DF
对于配体-受体动态正电子发射断层扫描（PET）研究的定量分析，通常希望应用参考组织方法，从而消除对动脉血液采样的需要。一种常见的技术是应用简化的参考组织模型（SRTM）。该方法的应用通常基于SRTM方程的解析解，其参数由非线性回归估计。在这项研究中，我们推导出，基于相同的假设，用于推导SRTM，一组新的积分形式的运算方程，参数直接估计传统的加权线性回归（WLR）。此外，一个线性回归与空间约束（LRSC）算法的参数成像，以减少高噪声水平的影响，在像素时间活动曲线，是典型的PET动态数据。为了比较，传统的加权非线性回归与马夸特算法（WNLRM）和非线性岭回归与空间约束（NLRRSC）也实现了使用SRTM方程的非线性解析解。与其他三种方法相比，LRSC降低了估计参数的均方根误差百分比，特别是在较高的噪声水平。对于结合电位（BP）的估计，WLR和LRSC即使在高噪声水平下也显示出相似的方差，但LRSC产生较小的偏差。人体实验结果表明，LRSC能产生高质量的参数化图像，WLR、WNLRM和NLRRSC产生的R-1和k（2）图像的方差可降低30%~ 60%。WLR和LRSC生成的BP图像质量在视觉上相当，WNLRM生成的BP图像的方差可以通过WLR或LRSC减少10%-40%。使用WLR获得的BP估计值比LRSC估计值低3%-5%。我们得出结论，新的线性方程产生可靠的、计算效率高的和鲁棒的LRSC算法，以生成配体-受体动态PET研究的参数图像。(C)2003 Elsevier Science（美国）。All rights reserved.
For the quantitative analysis of ligand-receptor dynamic positron emission tomography (PET) studies, it is often desirable to apply reference tissue methods that eliminate the need for arterial blood sampling. A common technique is to apply a simplified reference tissue model (SRTM). Applications of this method are generally based on an analytical solution of the SRTM equation with parameters estimated by nonlinear regression. In this study, we derive, based on the same assumptions used to derive the SRTM, a new set of operational equations of integral form with parameters directly estimated by conventional weighted linear regression (WLR). In addition, a linear regression with spatial constraint (LRSC) algorithm is developed for parametric imaging to reduce the effects of high noise levels in pixel time activity curves that are typical of PET dynamic data. For comparison, conventional weighted nonlinear regression with the Marquardt algorithm (WNLRM) and nonlinear ridge regression with spatial constraint (NLRRSC) were also implemented using the nonlinear analytical solution of the SRTM equation. In contrast to the other three methods, LRSC reduces the percent root mean square error of the estimated parameters, especially at higher noise levels. For estimation of binding potential (BP), WLR and LRSC show similar variance even at high noise levels, but LRSC yields a smaller bias. Results from human studies demonstrate that LRSC produces high-quality parametric images, The variance of R-1 and k(2) images generated by WLR, WNLRM, and NLRRSC can be decreased 30%-60% by using LRSC. The quality of the BP images generated by WLR and LRSC is visually comparable, and the variance of BP images generated by WNLRM can be reduced 10%-40% by WLR or LRSC. The BP estimates obtained using WLR are 3%-5% lower than those estimated by LRSC, We conclude that the new linear equations yield a reliable, computationally efficient, and robust LRSC algorithm to generate parametric images of ligand-receptor dynamic PET studies. (C) 2003 Elsevier Science (USA). All rights reserved.