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Sommer, Julia C.; Gertheiss, Jan and Schmid, Volker J. ORCID logoORCID: https://orcid.org/0000-0003-2195-8130 (8. October 2012): Spatially regularized estimation for the analysis of DCE-MRI data. Department of Statistics: Technical Reports, No.132 [PDF, 3MB]

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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 data set.

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