Spatially regularized estimation for the analysis of DCE-MRI data

Sommer, Julia C. and Gertheiss, Jan and Schmid, Volker J. (2012): Spatially regularized estimation for the analysis of DCE-MRI data. Department of Statistics: Technical Reports, No.132

Abstract

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.

• Item Type: Paper (Technical Report)
• Keywords: DCE-MRI; Elastic Net; Model Selection; Multi-compartment Model; Spatially Penalized Estimation
• Collections: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Subjects: 500 Science > 500 Science
• Language: English