Qin Qin, Huang Alan J, Hua Jun, Desmond John E, Stevens Robert D, van Zijl Peter C M
Russell H. Morgan Department of Radiology and Radiological Science Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
NMR Biomed. 2014 Feb;27(2):116-28. doi: 10.1002/nbm.3040. Epub 2013 Oct 16.
Measurement of the cerebral blood flow (CBF) with whole-brain coverage is challenging in terms of both acquisition and quantitative analysis. In order to fit arterial spin labeling-based perfusion kinetic curves, an empirical three-parameter model which characterizes the effective impulse response function (IRF) is introduced, which allows the determination of CBF, the arterial transit time (ATT) and T(1,eff). The accuracy and precision of the proposed model were compared with those of more complicated models with four or five parameters through Monte Carlo simulations. Pseudo-continuous arterial spin labeling images were acquired on a clinical 3-T scanner in 10 normal volunteers using a three-dimensional multi-shot gradient and spin echo scheme at multiple post-labeling delays to sample the kinetic curves. Voxel-wise fitting was performed using the three-parameter model and other models that contain two, four or five unknown parameters. For the two-parameter model, T(1,eff) values close to tissue and blood were assumed separately. Standard statistical analysis was conducted to compare these fitting models in various brain regions. The fitted results indicated that: (i) the estimated CBF values using the two-parameter model show appreciable dependence on the assumed T(1,eff) values; (ii) the proposed three-parameter model achieves the optimal balance between the goodness of fit and model complexity when compared among the models with explicit IRF fitting; (iii) both the two-parameter model using fixed blood T1 values for T(1,eff) and the three-parameter model provide reasonable fitting results. Using the proposed three-parameter model, the estimated CBF (46 ± 14 mL/100 g/min) and ATT (1.4 ± 0.3 s) values averaged from different brain regions are close to the literature reports; the estimated T(1,eff) values (1.9 ± 0.4 s) are higher than the tissue T1 values, possibly reflecting a contribution from the microvascular arterial blood compartment.
在全脑覆盖的情况下，测量脑血流量（CBF）在采集和定量分析方面都具有挑战性。为了拟合基于动脉自旋标记的灌注动力学曲线，引入了一个经验性的三参数模型，该模型表征了有效脉冲响应函数（IRF），从而能够确定CBF、动脉传输时间（ATT）和T(1,eff)。通过蒙特卡罗模拟，将所提出模型的准确性和精密度与更复杂的四参数或五参数模型进行了比较。在10名正常志愿者身上，使用临床3-T扫描仪，采用三维多激发梯度和自旋回波序列，在多个标记后延迟时间采集伪连续动脉自旋标记图像，以采样动力学曲线。使用三参数模型和包含两个、四个或五个未知参数的其他模型进行体素拟合。对于两参数模型，分别假设T(1,eff)值接近组织和血液的值。进行标准统计分析以比较这些拟合模型在不同脑区的情况。拟合结果表明：（i）使用两参数模型估计的CBF值对假设的T(1,eff)值有明显依赖性；（ii）在所提出的具有显式IRF拟合的模型中，三参数模型在拟合优度和模型复杂性之间实现了最佳平衡；（iii）使用固定血液T1值作为T(1,eff)的两参数模型和三参数模型都提供了合理的拟合结果。使用所提出的三参数模型，不同脑区平均估计的CBF（46±14 mL/100 g/min）和ATT（1.4±0.3 s）值接近文献报道；估计的T(1,eff)值（1.9±0.4 s）高于组织T1值，可能反映了微血管动脉血腔的贡献。
