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Spatially regularized estimation for the analysis of dynamic contrast‐enhanced magnetic resonance imaging data
Author(s) -
Sommer Julia C.,
Gertheiss Jan,
Schmid Volker J.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5997
Subject(s) - voxel , estimator , computer science , a priori and a posteriori , contrast (vision) , basis (linear algebra) , data set , set (abstract data type) , algorithm , basis function , mathematics , artificial intelligence , statistics , mathematical analysis , philosophy , geometry , epistemology , programming language
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. Copyright © 2013 John Wiley & Sons, Ltd.