期刊
STATISTICS IN MEDICINE
卷 33, 期 6, 页码 1029-1041出版社
WILEY
DOI: 10.1002/sim.5997
关键词
DCE-MRI; elastic net; model selection; multi-compartment model; spatially penalized estimation
类别
资金
- Deutsche Forschungsgemeinschaft [DFG SCHM 2747/1-1]
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 apriori. 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 (c) 2013 John Wiley & Sons, Ltd.
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