DCE-MRI of the liver: effect of linear and nonlinear conversions on hepatic perfusion quantification and reproducibility.
DCE-MRI of the liver: effect of linear and nonlinear conversions on hepatic perfusion quantification and reproducibility.
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DOI：
10.1002/jmri.24341
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发表时间：
2014-07
影响因子：
4.4
通讯作者：
Taouli, Bachir
中科院分区：
文献类型：
--
作者：
Aronhime, Shimon;Calcagno, Claudia;Jajamovich, Guido H.;Dyvorne, Hadrien Arezki;Robson, Philip;Dieterich, Douglas;Fiel, M. Isabel;Martel-Laferriere, Valerie;Chatterji, Manjil;Rusinek, Henry;Taouli, Bachir
评价将MR信号强度（SI）转换为钆浓度（[Gd]）的不同方法对使用DCE-MRI测量的无模型和建模肝脏灌注参数的估计和再现性的影响。在这项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SPGR信号方程）将SI转换为[Gd]。[Gd]使用无模型参数和双输入单室模型分析对时间曲线。采用配对Wilcoxon检验比较两种转换方法获得的灌注参数。在6例患者中评估了灌注参数的重测和观察者间重现性。两种转换方法在以下参数方面存在显著差异：AUC60（60秒时的曲线下面积，p <0.001）、峰值钆浓度（Cpeak，p <0.001）、上升斜率（p <0.001）、Fp（门静脉流量，p=0.04）、总肝流量（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重现性都可以接受[平均变异系数（CV）范围为11.87-23.7%]，但上坡（CV = 37%）除外。在模型参数中，DV（分布容积）的CV <22%，PV和MTT的CV分别<21%和<29%。两种方法的其他建模参数的CV >30%。线性假设可用于无模型肝脏灌注参数的定量，而SPGR方程和T1标测可推荐用于建模肝脏灌注参数的定量。
To evaluate the effect of different methods to convert MR signal intensity (SI) to gadolinium concentration ([Gd]) on estimation and reproducibility of model-free and modeled hepatic perfusion parameters measured with DCE-MRI. In this IRB-approved prospective study, 23 DCE-MRI examinations of the liver were performed on 17 patients. SI was converted to [Gd] using linearity vs. non linearity assumptions (using SPGR signal equations). [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 inter-observer reproducibility of perfusion parameters were assessed in 6 patients. There were significant differences between the two conversion methods for the following parameters: AUC60 (area under the curve at 60 seconds, 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.
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影响因子：
5
作者：
Jones, RA;Easley, K;Grattan-Smith, JD
通讯作者：
Grattan-Smith, JD
影响因子：
6
作者：
Materne, R;Van Beers, BE;Horsmans, Y
通讯作者：
Horsmans, Y
影响因子：
25.7
作者：
Hsu, Chao-Yu;Shen, Ying-Chun;Shih, Tiffany Ting-Fang
通讯作者：
Shih, Tiffany Ting-Fang
影响因子：
2.9
作者：
Galbraith, SM;Lodge, MA;Padhani, AR
通讯作者：
Padhani, AR
影响因子：
2.5
作者：
Dobre, Mircea C.;Ugurbil, Kamil;Marjanska, Malgorzata
通讯作者：
Marjanska, Malgorzata