Lavini Cristina
Department of Radiology, Academic Medical Center, University of Amsterdam, 1100-DE Amsterdam, The Netherlands.
Magn Reson Imaging. 2015 Feb;33(2):222-35. doi: 10.1016/j.mri.2014.10.004. Epub 2014 Oct 13.
Pharmacokinetic modeling in Dynamic Contrast Enhanced (DCE)-MRI is an elegant and useful method that provides valuable insight into angiogenesis in cancer and inflammatory diseases. Despite its widespread use, the reliability of the model results is still questioned, as many factors hamper the calculation of the model's parameters, resulting in the poor reproducibility and accuracy of the method. Pharmacokinetic modeling relies on the knowledge of inputs such as the Arterial Input Function (AIF) and of the tissue contrast agent concentration, both of which are difficult to accurately measure. Any errors in the measurement of either of the inputs propagate into the calculated pharmacokinetic model parameters (PMPs), and the significance of the effect depends on the source of the measurement error. In this work we systematically investigate the effect of the incorrect estimation of the parameters describing the inputs of the model on the calculated PMPs when using Tofts' model. Furthermore, we analyze the dependence of these errors on the native values of the PMPs. We show that errors on the measurement of the native T1 as well as errors on the parameters describing the initial peak of the AIF have the largest impact on the calculated PMPs. The parameter whose error has the least effect is the one describing the slow decay of the AIF. The effect of input parameter (IP) errors on the calculated PMPs is found to be dependent on the native set of PMPs: this is particularly true for the errors in the flip angle, and for the errors in parameters describing the initial AIF peak. Conversely the effect of T1 and AIF scaling errors on the calculated PMPs is only slightly dependent on the native PMPs.
动态对比增强（DCE）-MRI中的药代动力学建模是一种优雅且有用的方法，它能为癌症和炎症性疾病中的血管生成提供有价值的见解。尽管其被广泛应用，但模型结果的可靠性仍受到质疑，因为许多因素阻碍了模型参数的计算，导致该方法的重现性和准确性较差。药代动力学建模依赖于诸如动脉输入函数（AIF）和组织造影剂浓度等输入信息，而这两者都难以准确测量。任何一个输入测量中的误差都会传播到计算出的药代动力学模型参数（PMPs）中，且这种影响的显著性取决于测量误差的来源。在这项工作中，我们系统地研究了在使用Tofts模型时，对描述模型输入的参数进行错误估计对计算出的PMPs的影响。此外，我们分析了这些误差对PMPs原始值的依赖性。我们表明，原始T1测量中的误差以及描述AIF初始峰值的参数误差对计算出的PMPs影响最大。误差影响最小的参数是描述AIF缓慢衰减的那个参数。发现输入参数（IP）误差对计算出的PMPs的影响取决于PMPs的原始集：对于翻转角误差和描述AIF初始峰值的参数误差尤其如此。相反，T1和AIF缩放误差对计算出的PMPs的影响仅略微依赖于原始PMPs。
