Cheng Hai-Ling Margaret
The Research Institute & Diagnostic Imaging, The Hospital for Sick Children and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
J Magn Reson Imaging. 2009 Oct;30(4):864-72. doi: 10.1002/jmri.21916.
To propose a modified initial area under the uptake curve (mIAUC) dynamic contrast-enhanced (DCE)-MRI approach to achieve better distinction of underlying physiology.
The mIAUC is formulated on common characteristics of tissue contrast uptake curves observed over a wide range of physiological conditions. The new metrics, IAUC(Ktrans) and IAUC(ve), are related to the transfer constant (K(trans)) and interstitial volume (v(e)), respectively. Tissue uptake curves were simulated over a range of physiological values and analyzed using the proposed mIAUC, conventional IAUC, and Tofts' pharmacokinetic model.
IAUC(Ktrans) and IAUC(ve) are highly correlated to the true K(trans) and v(e) (rho = 0.97 and 0.95, respectively), approaching the performance of Tofts' model based on a 1.5-s sampled arterial input function (AIF) (rho = 0.98 and 0.98) under noise conditions typical in DCE-MRI experiments. Lower correlations were obtained with conventional IAUC(60) and IAUC(120) (rho = 0.82 and 0.61) and Tofts' parameters fitted using a biexponential AIF (rho = 0.81 and 0.90).
The proposed mIAUC approach retains advantages associated with nonmodel based methods (robust to noise and model fit failure, obviates need for an AIF) while providing better distinction of underlying physiological parameters. It can be a valuable alternative to pharmacokinetic modelling in the analysis of DCE-MRI data.
提出一种改良的摄取曲线下初始面积（mIAUC）动态对比增强（DCE）-MRI方法，以更好地区分潜在生理状态。
mIAUC是根据在广泛生理条件下观察到的组织对比摄取曲线的共同特征制定的。新指标IAUC(Ktrans)和IAUC(ve)分别与转移常数（Ktrans）和细胞外间隙容积（ve）相关。在一系列生理值范围内模拟组织摄取曲线，并使用所提出的mIAUC、传统IAUC和Tofts药代动力学模型进行分析。
IAUC(Ktrans)和IAUC(ve)与真实的Ktrans和ve高度相关（分别为ρ = 0.97和0.95），在DCE-MRI实验典型的噪声条件下，接近基于1.5秒采样动脉输入函数（AIF）的Tofts模型的性能（ρ = 0.98和0.98）。传统的IAUC(60)和IAUC(120)以及使用双指数AIF拟合的Tofts参数的相关性较低（ρ = 0.82和0.61以及ρ = 0.81和0.90）。
所提出的mIAUC方法保留了基于非模型方法的优点（对噪声和模型拟合失败具有鲁棒性，无需AIF），同时能更好地区分潜在生理参数。在DCE-MRI数据分析中，它可能是药代动力学建模的一种有价值的替代方法。
