title: Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data.
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 dataset.
authors.affiliation: Department of Statistics, Ludwig-Maximilians-Universität, Munich, Germany.
authors.forename: Julia C
authors.initials: JC
authors.lastname: Sommer
authors.forename: Jan
authors.initials: J
authors.lastname: Gertheiss
authors.forename: Volker J
authors.initials: VJ
authors.lastname: Schmid
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy; D003198:Computer Simulation; D003287:Contrast Media; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data*; D015233:Models, Statistical
chemical_list: D003287:Contrast Media
keywords: DCE-MRI; elastic net; model selection; multi-compartment model; spatially penalized estimation