Schmid Volker J, Whitcher Brandon, Yang Guang-Zhong
Institute for Biomedical Engineering, Imperial College, South Kensington, London SW7 2AZ, United Kingdom.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):679-86. doi: 10.1007/11866565_83.
Current approaches to quantitative analysis of DCE-MRI with non-linear models involve the convolution of an arterial input function (AIF) with the contrast agent concentration at a voxel or regional level. Full quantification provides meaningful biological parameters but is complicated by the issues related to convergence, (de-)convolution of the AIF, and goodness of fit. To overcome these problems, this paper presents a penalized spline smoothing approach to model the data in a semi-parametric way. With this method, the AIF is convolved with a set of B-splines to produce the design matrix, and modeling of the resulting deconvolved biological parameters is obtained in a way that is similar to the parametric models. Further kinetic parameters are obtained by fitting a non-linear model to the estimated response function and detailed validation of the method, both with simulated and in vivo data is
当前使用非线性模型对动态对比增强磁共振成像（DCE-MRI）进行定量分析的方法，涉及在体素或区域水平将动脉输入函数（AIF）与造影剂浓度进行卷积。完整的定量分析可提供有意义的生物学参数，但会因与收敛、AIF的（去）卷积以及拟合优度相关的问题而变得复杂。为克服这些问题，本文提出一种惩罚样条平滑方法，以半参数方式对数据进行建模。使用该方法时，将AIF与一组B样条进行卷积以生成设计矩阵，并以类似于参数模型的方式对所得去卷积后的生物学参数进行建模。通过将非线性模型拟合到估计的响应函数来获得进一步的动力学参数，并且使用模拟数据和体内数据对该方法进行了详细验证。
