Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data
Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data
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DOI：
10.3414/me14-01-0131
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发表时间：
2015-11
影响因子：
1.7
通讯作者：
M. Feilke;B. Bischl;Volker J Schmid;J. Gertheiss
中科院分区：
文献类型：
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作者：
M. Feilke;B. Bischl;Volker J Schmid;J. Gertheiss
摘要背景：对于动态对比增强磁共振成像(DCE-MRI)数据的统计分析，隔室模型是常用的工具。通过这些模型，观察到的造影剂随时间在某些组织中的摄取与毛细血管通透性和血流等生理特性有关。到目前为止，已经使用了不同复杂性的模型，在哪种情况下应该使用哪种模型仍然不清楚。在以前的研究中，已经发现对于DCE-MRI数据，不同类型的组织的间隔数目不同，并且在癌症组织中，实际上可能在一个DCE-MR图像的体素区域上不同。目的：为DCE-MR图像中的每个体素找到合适的间隔数目并估计回归模型的参数。有了这一点，DCE-MR图像中的肿瘤就可以定位，例如可以评估治疗成功。方法：用浓度-时间曲线描述对比剂在某些组织图像体素中的摄取情况。这条曲线可以用非线性回归模型来建模。我们提出了一种以非线性回归为基本步骤的Boosting方法，它允许我们估计DCE-MR图像的每个体素的隔室数量和相关参数。此外，还提出了该方法的空间正则化版本。结果：在所提出的方法中，每个体素的间隔数目以及模型的复杂性不是固定的，而是由数据驱动的，这使得我们能够将足够复杂的模型拟合到所有体素的浓度时间曲线。然而，由于基本的隔室模型，模型的参数仍然是可解释的。结论：在DCE-MR图像中肿瘤的正确定位、图像上估计的间隔数目的空间均质性以及肿瘤边缘的清晰度方面，所提出的增强方法优于本文中考虑的所有竞争方法。
Summary Background: For the statistical analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data, compartment models are a commonly used tool. By these models, the observed uptake of contrast agent in some tissue over time is linked to physiologic properties like capillary permeability and blood flow. Up to now, models of different complexity have been used, and it is still unclear which model should be used in which situation. In previous studies, it has been found that for DCE-MRI data, the number of compartments differs for different types of tissue, and that in cancerous tissue, it might actually differ over a region of voxels of one DCE-MR image. Objectives: To find the appropriate number of compartments and estimate the parameters of a regression model for each voxel in an DCE-MR image. With that, tumors in an DCE-MR image can be located, and for example therapy success can be assessed. Methods: The observed uptake of contrast agent in a voxel of an image of some tissue is described by a concentration time curve. This curve can be modeled using a nonlinear regression model. We present a boosting approach with nonlinear regression as base procedure, which allows us to estimate the number of compartments and the related parameters for each voxel of an DCE-MR image. In addition, a spatially regularized version of this approach is proposed. Results: With the proposed approach, the number of compartments – and with that the complexity of the model – per voxel is not fixed but data-driven, which allows us to fit models of adequate complexity to the concentration time curves of all voxels. The parameters of the model remain nevertheless interpretable because of the underlying compartment model. Conclusions: The proposed boosting approaches outperform all competing methods considered in this paper regarding the correct localization of tumors in DCE-MR images as well as the spatial homogeneity of the estimated number of compartments across the image, and the definition of the tumor edge.