A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo results.
A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo results.
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DOI：
10.1088/0031-9155/55/16/012
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发表时间：
2010-08-21
影响因子：
3.5
通讯作者：
Salzman KL
中科院分区：
文献类型：
--
作者：
Schabel MC;DiBella EV;Jensen RL;Salzman KL
在示踪剂动力学成像实验中，准确地量化药物动力学模型参数需要相应地准确地确定动脉输入函数(AIF)。尽管在动态增强磁共振成像(DCE-MRI)、动态正电子发射断层扫描(PET)和灌注计算机断层扫描(CT)等多种方式中直接测量患者特定AIF的方法上花费了大量的努力，但基本和技术上的困难使这一目标的持续和可靠的实现变得遥不可及。在这里，我们验证了一种新的确定AIF的算法-蒙特卡洛盲估计法(MCBE)(详细描述，并在配套论文中通过广泛的模拟)，通过比较8名脑瘤患者在DCE-MRI研究中测量的AIF与盲估测结果。使用一组正常脑组织的浓度-时间曲线池，用MCBE方法计算的盲法AIF与测量的AIF非常相似，8名患者中有6名观察到的拟合残差在统计学上显著减少。盲法和实测法的药代动力学参数之间的偏差是误差的主要来源。在所有8名患者中，平均偏差在KTRANS为+7%，在KEPP为0%，在VP为11%，在VE为+10%。相应的不确定度(与最佳拟合线的中位数绝对偏差)分别为：KTRANS为0.0043分钟−1，KEPS为0.0491分钟−1，VP为0.29%，VE为0.45%。使用已发表的人群平均AIF，在四个参数中的三个(KTRANS的−23%，KEP的−22%，VE的−63%)中，Ve的偏倚保持不变，并导致所有四个参数的更大的不确定度(KTRANS的0.0083分钟−1，KTRANS的0.1038−1，VE的0.31%，Ve的0.95%)。当根据肿瘤组织的一个区域计算盲区AIF时，所有8名患者的拟合残差都有统计学意义的降低，尽管这些盲区AIF与测量的AIF有较大的偏差。在正常脑和肿瘤组织盲AIF之间观察到的均方根拟合残差的减少表明，肿瘤的局部血供与正常脑组织的局部血供明显不同，所提出的方法能够区分这两者。我们已经证明了将MCBE算法应用于在脑中获取的DCE-MRI数据的可行性，发现与测量的AIF基本一致，并且相对于使用总体平均的AIF，偏差和不确定性减少了。这些结果表明，在难以或不可能获得高质量的动脉输入功能数据的情况下，MCBE算法是直接测量AIF的有用的替代方案。
Accurate quantification of pharmacokinetic model parameters in tracer kinetic imaging experiments requires correspondingly accurate determination of the arterial input function (AIF). Despite significant effort expended on methods of directly measuring patient-specific AIFs in modalities as diverse as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), dynamic positron emission tomography (PET), and perfusion computed tomography (CT), fundamental and technical difficulties have made consistent and reliable achievement of that goal elusive. Here, we validate a new algorithm for AIF determination, the Monte Carlo Blind Estimation (MCBE) method (which is described in detail and characterized by extensive simulations in a companion paper), by comparing AIFs measured in DCE-MRI studies of eight brain tumor patients with results of blind estimation. Blind AIFs calculated with the MCBE method using a pool of concentration-time curves from a region of normal brain tissue were found to be quite similar to the measured AIFs, with statistically significant decreases in fit residuals observed in six of eight patients. Biases between the blind and measured pharmacokinetic parameters were the dominant source of error. Averaged over all eight patients, the mean biases were +7% in Ktrans, 0% in kep, −11% in vp, and +10% in ve. Corresponding uncertainties (median absolute deviation from best fit line) were 0.0043 min−1 in Ktrans, 0.0491 min−1 in kep, 0.29% in vp, and 0.45% in ve. Use of a published population-averaged AIF resulted in larger mean biases in three of the four parameters (−23% in Ktrans, −22% in kep, −63% in vp), with the bias in ve unchanged, and led to larger uncertainties in all four parameters (0.0083 min−1 in Ktrans, 0.1038 min−1 in kep, 0.31% in vp, and 0.95% in ve). When blind AIFs were calculated from a region of tumor tissue, statistically significant decreases in fit residuals were observed in all eight patients despite larger deviations of these blind AIFs from the measured AIFs. The observed decrease in root-mean-square fit residuals between the normal brain and tumor tissue blind AIFs suggests that the local blood supply in tumors is measurably different from that in normal brain tissue and that the proposed method is able to discriminate between the two. We have shown the feasibility of applying the MCBE algorithm to DCE-MRI data acquired in brain, finding generally good agreement with measured AIFs and decreased biases and uncertainties relative to use of a population-averaged AIF. These results demonstrate that the MCBE algorithm is a useful alternative to direct AIF measurement in cases where acquisition of high-quality arterial input function data is difficult or impossible.
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影响因子：
8.8
作者：
Leach MO;Brindle KM;Evelhoch JL;Griffiths JR;Horsman MR;Jackson A;Jayson GC;Judson IR;Knopp MV;Maxwell RJ;McIntyre D;Padhani AR;Price P;Rathbone R;Rustin GJ;Tofts PS;Tozer GM;Vennart W;Waterton JC;Williams SR;Workman P;Pharmacodynamic/Pharmacokinetic Technologies Advisory Committee, Drug Development Office, Cancer Research UK
通讯作者：
Pharmacodynamic/Pharmacokinetic Technologies Advisory Committee, Drug Development Office, Cancer Research UK
影响因子：
3.5
作者：
Schabel MC;Fluckiger JU;DiBella EV
通讯作者：
DiBella EV
影响因子：
3.3
作者：
Calamante, F;Willats, L;Connelly, A
通讯作者：
Connelly, A
影响因子：
4.4
作者：
Ko, Linda;Salluzzi, Marina;Smith, Michael
通讯作者：
Smith, Michael
影响因子：
3.3
作者：
Port, RE;Knopp, MV;Brix, G
通讯作者：
Brix, G