Sommer, Julia C.; Gertheiss, Jan; Schmid, Volker J.
(10. October 2012):
Spatially regularized estimation for the analysis of DCE-MRI data.
Department of Statistics: Technical Reports, No.132
This is the latest version of this item.
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.
Available Versions of this Item