Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, Utah, USA.
Magn Reson Med. 2009 Dec;62(6):1477-86. doi: 10.1002/mrm.22101.
A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with "truth" obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The "true" K(trans) values in tumor regions were not significantly different than the estimated values, 0.99 +/- 0.41 and 0.86 +/- 0.40 min(-1), respectively, P = 0.27. "True" k(ep) values also matched closely, 0.70 +/- 0.24 and 0.65 +/- 0.25 min(-1), P = 0.08. When only tissue curves free of significant vascular contribution are used (v(p) < 0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.
一种从动态对比增强（DCE）-MRI 数据同时估计动脉输入函数（AIF）和药代动力学模型参数的方法被开发出来。该算法使用参数化的函数形式来对 AIF 建模，并用 k-均值聚类将组织时间浓度测量值分类为一组特征曲线。迭代盲估计算法交替估计输入函数和药代动力学模型的参数。通过计算机模拟研究了算法对噪声和初始估计的敏感性。在 12 名肉瘤患者中，通过使用测量的 AIF 对模型回归，比较了药代动力学参数估计值与“真实”值。当动脉体素被包含在盲估计算法中时，得到的 AIF 与测量的输入函数相似。肿瘤区域的“真实”Ktrans 值与估计值无显著差异，分别为 0.99 +/- 0.41 和 0.86 +/- 0.40 min-1，P = 0.27。“真实”kep 值也非常接近，分别为 0.70 +/- 0.24 和 0.65 +/- 0.25 min-1，P = 0.08。当仅使用没有明显血管贡献的组织曲线时（v(p) < 0.05），得到的 AIF 显示出与大脑中动态磁化率对比成像中观察到的更局部 AIF 一致的显著延迟和弥散。
