Spatially regularized estimation for the analysis of dynamic contrast‐enhanced magnetic resonance imaging data
Spatially regularized estimation for the analysis of dynamic contrast‐enhanced magnetic resonance imaging data
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DOI：
10.1002/sim.5997
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发表时间：
2014-03
影响因子：
2
通讯作者：
Julia C. Sommer;J. Gertheiss;Volker J Schmid
中科院分区：
文献类型：
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作者：
Julia C. Sommer;J. Gertheiss;Volker J Schmid
不同复杂性的竞争房室模型已用于动态对比增强磁共振成像数据的定量分析。我们提出了一个空间弹性网的方法，允许估计每个体素的车厢的数量，使模型的复杂性是不固定的先验。考虑多房室方法，将其转化为限制性最小二乘模型选择问题。这是通过使用一组基函数为一组给定的候选速率常数。基函数的形式来自动力学模型，因此描述了特定隔室的贡献。使用空间弹性网络估计，我们选择了一组稀疏的基函数，每个体素，因此，速率常数的车厢。空间惩罚考虑了图像的体素结构，并且比单独处理体素的惩罚执行得更好。所提出的估计方法进行评估模拟图像和应用到体内数据集。版权所有© 2013约翰威利父子有限公司.
Competing compartment models of different complexities have been used for the quantitative analysis of dynamic contrast‐enhanced magnetic resonance imaging data. We present a spatial elastic net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi‐compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial elastic net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in vivo dataset. Copyright © 2013 John Wiley & Sons, Ltd.