A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. Simulations.
A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. Simulations.
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DOI：
10.1088/0031-9155/55/16/011
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发表时间：
2010-08-21
影响因子：
3.5
通讯作者：
DiBella EV
中科院分区：
文献类型：
--
作者：
Schabel MC;Fluckiger JU;DiBella EV
定量药代动力学建模方法与动态对比增强磁共振成像（DCE-MRI）结合的广泛采用，使人们越来越认识到获得准确的患者特异性动脉输入功能（AIF）测量值的重要性。理想情况下，DCE-MRI研究使用在目标组织局部动脉中直接测量的AIF，沿着测量的组织浓度曲线，以定量确定药代动力学参数。然而，与AIF测量相关的许多技术和实际困难使得使用人口平均AIF数据成为AIF测量的一种流行的（如果不是最佳的）替代方法。在这项工作中，我们提出了一种新的算法，用于确定AIF仅从测量的组织浓度曲线的特点。该Monte Carlo盲估计（MCBE）算法通过非线性优化从M个候选曲线的较大池中绘制的D个浓度-时间曲线的子集估计AIF，对多个（Q）子集进行估计，并对这些重复估计值进行统计平均。MCBE算法可以被视为以前发表的方法的推广，这些方法采用浓度-时间曲线的聚类并且仅估计一次AIF。在成像和组织参数的生理和实验现实范围内进行了广泛的计算机模拟，并研究了选择不同D和Q值的影响。我们发现，该算法是强大的，计算效率高，并能够准确地估计AIF，即使相对较高的噪声水平，长的采样间隔，和低多样性的组织曲线。通过引入自举初始化，我们进一步证明了盲估计AIF的能力，这些AIF的形状与总体平均初始猜测有很大的偏差。Ktranss、kep、vp和ve的药代动力学参数估计值均显示，对于时间采样率为4秒且浓度测量噪声水平为σ = 0.04 mM.A的测量值，相对偏倚和不确定性均小于10%。配套文件讨论了MCBE算法在8例恶性脑肿瘤患者中采集的DCE-MRI数据中的应用。
Widespread adoption of quantitative pharmacokinetic modeling methods in conjunction with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has led to increased recognition of the importance of obtaining accurate patient-specific arterial input function (AIF) measurements. Ideally, DCE-MRI studies use an AIF directly measured in an artery local to the tissue of interest, along with measured tissue concentration curves, to quantitatively determine pharmacokinetic parameters. However, the numerous technical and practical difficulties associated with AIF measurement have made the use of population-averaged AIF data a popular, if suboptimal, alternative to AIF measurement. In this work, we present and characterize a new algorithm for determining the AIF solely from the measured tissue concentration curves. This Monte Carlo Blind Estimation (MCBE) algorithm estimates the AIF from subsets of D concentration-time curves drawn from a larger pool of M candidate curves via nonlinear optimization, doing so for multiple (Q) subsets and statistically averaging these repeated estimates. The MCBE algorithm can be viewed as a generalization of previously published methods that employ clustering of concentration-time curves and only estimate the AIF once. Extensive computer simulations were performed over physiologically- and experimentally-realistic ranges of imaging and tissue parameters, and the impact of choosing different values of D and Q was investigated. We found the algorithm to be robust, computationally-efficient, and capable of accurately estimating the AIF even for relatively high noise levels, long sampling intervals, and low diversity of tissue curves. With the incorporation of boostrapping initialization, we further demonstrated the ability to blindly estimate AIFs that deviate substantially in shape from the population-averaged initial guess. Pharmacokinetic parameter estimates for Ktrans, kep, vp, and ve all showed relative biases and uncertainties of less than 10% for measurements having a temporal sampling rate of 4 seconds and a concentration measurement noise level of σ = 0.04 mM. A companion paper discusses the application of the MCBE algorithm to DCE-MRI data acquired in eight patients with malignant brain tumors.
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影响因子：
3.3
作者：
Calamante, F;Willats, L;Connelly, A
通讯作者：
Connelly, A
影响因子：
3.3
作者：
Hodgson, Richard J.;Connolly, Sylvia;Moots, Robert
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Moots, Robert
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作者：
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Smith, Michael
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作者：
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通讯作者：
Conturo, Thomas E.
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3.3
作者：
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通讯作者：
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