Abstract
We present a nonnegative Elastic Net approach for the analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging data. 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 some compartment. Using the Elastic Net estimator, we chose clusters of basis functions, and hence, rate constants of compartments. As further challenge, the estimator has to be restricted to positive regression parameters, which correspond to transfer rates of the compartments. The proposed estimation method is applied to an in-vivo data set.
Dokumententyp: | Paper |
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Keywords: | Compartment Model, DCE-MRI, Elastic Net, Regularized Estimation |
Fakultät: | Mathematik, Informatik und Statistik > Statistik > Technische Reports
Mathematik, Informatik und Statistik > Statistik > Lehrstühle/Arbeitsgruppen > Bioimaging |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
500 Naturwissenschaften und Mathematik > 510 Mathematik 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
URN: | urn:nbn:de:bvb:19-epub-11809-6 |
Sprache: | Englisch |
Dokumenten ID: | 11809 |
Datum der Veröffentlichung auf Open Access LMU: | 29. Sep. 2010, 15:15 |
Letzte Änderungen: | 04. Nov. 2020, 12:52 |