Feilke M, Bischl B, Schmid V J, Gertheiss J
Volker J. Schmid, Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstr. 33, 80539 München, Germany, E-mail:
Methods Inf Med. 2016;55(1):31-41. doi: 10.3414/ME14-01-0131. Epub 2015 Nov 18.
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.
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.
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.
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.
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.
对于动态对比增强磁共振成像（DCE-MRI）数据的统计分析，房室模型是一种常用工具。通过这些模型，在某些组织中观察到的造影剂随时间的摄取与诸如毛细血管通透性和血流等生理特性相关联。到目前为止，已经使用了不同复杂度的模型，并且仍然不清楚在何种情况下应使用哪种模型。在先前的研究中，已经发现对于DCE-MRI数据，不同类型组织的房室数量不同，并且在癌组织中，在一幅DCE-MR图像的体素区域内实际可能也不同。
为DCE-MR图像中的每个体素找到合适的房室数量并估计回归模型的参数。借此，可以定位DCE-MR图像中的肿瘤，例如可以评估治疗效果。
通过浓度-时间曲线描述在某组织图像的一个体素中观察到的造影剂摄取情况。该曲线可以使用非线性回归模型进行建模。我们提出一种以非线性回归为基本程序的提升方法，这使我们能够估计DCE-MR图像中每个体素的房室数量和相关参数。此外，还提出了该方法的空间正则化版本。
使用所提出的方法，每个体素的房室数量以及模型的复杂度不是固定的，而是由数据驱动的，这使我们能够为所有体素的浓度-时间曲线拟合具有适当复杂度的模型。由于潜在的房室模型，模型参数仍然是可解释的。
在所提出的提升方法在DCE-MR图像中肿瘤的正确定位、图像中估计的房室数量的空间均匀性以及肿瘤边缘的定义方面优于本文中考虑的所有竞争方法。
