Department of Nuclear Medicine & PET Research, VU University Medical Centre, Amsterdam, The Netherlands.
Neuroimage. 2010 Jan 1;49(1):433-41. doi: 10.1016/j.neuroimage.2009.07.046. Epub 2009 Jul 28.
The purpose of the present study was to evaluate the performance of various parametric methods for quantification of [(18)F]FDDNP studies. All parametric methods tested were based on the use of a reference tissue and they were compared with the simplified reference tissue model (SRTM), as previously it has been shown that SRTM is the method of choice for analysing [(18)F]FDDNP studies, even when an arterial plasma input function is available. The following parametric methods were evaluated: receptor parametric mapping (basis function implementation of SRTM; with and without fixing the reference tissue efflux rate constant k'(2)), reference Logan and several multi-linear reference tissue methods (again with and without fixing k'(2)). Simulations were used to assess the effects of variation in relative flow (R(1)), fractional blood volume (V(b)) and binding potential (BP(ND)) on precision and accuracy of estimated BP(ND). For clinical data, best performance was obtained using receptor parametric mapping (RPM2) and one of the multi-linear reference tissue models (MRTM2), with k'(2) being fixed in both methods. These models showed good correlation with SRTM, their BP(ND) results were less affected by noise and images showed good contrast. Furthermore, in simulations, RPM2 and MRTM2 provided the most accurate and precise BP(ND) estimates. RPM2 and MRTM2 are the methods of choice for parametric analysis of [(18)F]FDDNP studies.
本研究旨在评估各种参数化方法在 [(18)F]FDDNP 研究定量分析中的性能。所有测试的参数化方法均基于参考组织的使用，并与简化参考组织模型（SRTM）进行了比较，因为先前已经表明，即使存在动脉血浆输入函数，SRTM 也是分析 [(18)F]FDDNP 研究的首选方法。评估了以下参数化方法：受体参数映射（SRTM 的基函数实现；有和没有固定参考组织流出率常数 k'(2)）、参考 Logan 和几种多线性参考组织方法（同样有和没有固定 k'(2)）。模拟用于评估相对血流量 (R(1))、血容量分数 (V(b)) 和结合势 (BP(ND)) 变化对估计 BP(ND)的精度和准确性的影响。对于临床数据，使用受体参数映射 (RPM2) 和一种多线性参考组织模型 (MRTM2) 获得了最佳性能，这两种方法都固定了 k'(2)。这些模型与 SRTM 具有良好的相关性，它们的 BP(ND)结果受噪声的影响较小，图像对比度良好。此外，在模拟中，RPM2 和 MRTM2 提供了最准确和精确的 BP(ND)估计。RPM2 和 MRTM2 是 [(18)F]FDDNP 研究参数分析的首选方法。
