Aronhime Shimon, Calcagno Claudia, Jajamovich Guido H, Dyvorne Hadrien Arezki, Robson Philip, Dieterich Douglas, Fiel M Isabel, Martel-Laferriere Valérie, Chatterji Manjil, Rusinek Henry, Taouli Bachir
Translational and Molecular Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
J Magn Reson Imaging. 2014 Jul;40(1):90-8. doi: 10.1002/jmri.24341. Epub 2013 Nov 4.
To evaluate the effect of different methods to convert magnetic resonance (MR) signal intensity (SI) to gadolinium concentration ([Gd]) on estimation and reproducibility of model-free and modeled hepatic perfusion parameters measured with dynamic contrast-enhanced (DCE)-MRI.
In this Institutional Review Board (IRB)-approved prospective study, 23 DCE-MRI examinations of the liver were performed on 17 patients. SI was converted to [Gd] using linearity vs. nonlinearity assumptions (using spoiled gradient recalled echo [SPGR] signal equations). The [Gd] vs. time curves were analyzed using model-free parameters and a dual-input single compartment model. Perfusion parameters obtained with the two conversion methods were compared using paired Wilcoxon test. Test-retest and interobserver reproducibility of perfusion parameters were assessed in six patients.
There were significant differences between the two conversion methods for the following parameters: AUC60 (area under the curve at 60 s, P < 0.001), peak gadolinium concentration (Cpeak, P < 0.001), upslope (P < 0.001), Fp (portal flow, P = 0.04), total hepatic flow (Ft, P = 0.007), and MTT (mean transit time, P < 0.001). Our preliminary results showed acceptable to good reproducibility for all model-free parameters for both methods (mean coefficient of variation [CV] range, 11.87-23.7%), except for upslope (CV = 37%). Among modeled parameters, DV (distribution volume) had CV <22% with both methods, PV and MTT showed CV <21% and <29% using SPGR equations, respectively. Other modeled parameters had CV >30% with both methods.
Linearity assumption is acceptable for quantification of model-free hepatic perfusion parameters while the use of SPGR equations and T1 mapping may be recommended for the quantification of modeled hepatic perfusion parameters.
评估将磁共振(MR)信号强度(SI)转换为钆浓度([Gd])的不同方法对动态对比增强(DCE)-MRI测量的无模型和建模肝脏灌注参数估计及可重复性的影响。
在这项经机构审查委员会(IRB)批准的前瞻性研究中,对17例患者进行了23次肝脏DCE-MRI检查。使用线性与非线性假设(使用扰相梯度回波[SPGR]信号方程)将SI转换为[Gd]。使用无模型参数和双输入单室模型分析[Gd]随时间的曲线。使用配对Wilcoxon检验比较两种转换方法获得的灌注参数。在6例患者中评估灌注参数的重测和观察者间的可重复性。
两种转换方法在以下参数上存在显著差异:AUC60(60秒时曲线下面积,P < 0.001)、钆峰值浓度(Cpeak,P < 0.001)、上升斜率(P < 0.001)、Fp(门静脉血流,P = 0.04)、肝脏总血流(Ft,P = 0.007)和MTT(平均通过时间,P < 0.001)。我们的初步结果表明,两种方法的所有无模型参数的可重复性均可接受至良好(平均变异系数[CV]范围为11.87 - 23.7%),上升斜率除外(CV = 37%)。在建模参数中,两种方法的DV(分布容积)的CV < 22%,使用SPGR方程时PV和MTT的CV分别< 21%和< 29%。其他建模参数两种方法的CV均> 30%。
线性假设对于无模型肝脏灌注参数的量化是可接受的,而对于建模肝脏灌注参数的量化,可能建议使用SPGR方程和T1映射。