title: Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data.
abstract: BACKGROUND
  For the statistical analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data, compartment models are a commonly used tool. By these models, the observed uptake of contrast agent in some tissue over time is linked to physiologic properties like capillary permeability and blood flow. Up to now, models of different complexity have been used, and it is still unclear which model should be used in which situation. In previous studies, it has been found that for DCE-MRI data, the number of compartments differs for different types of tissue, and that in cancerous tissue, it might actually differ over a region of voxels of one DCE-MR image.
  
  
  OBJECTIVE
  To find the appropriate number of compartments and estimate the parameters of a regression model for each voxel in an DCE-MR image. With that, tumors in an DCE-MR image can be located, and for example therapy success can be assessed.
  
  
  METHODS
  The observed uptake of contrast agent in a voxel of an image of some tissue is described by a concentration time curve. This curve can be modeled using a nonlinear regression model. We present a boosting approach with nonlinear regression as base procedure, which allows us to estimate the number of compartments and the related parameters for each voxel of an DCE-MR image. In addition, a spatially regularized version of this approach is proposed.
  
  
  RESULTS
  With the proposed approach, the number of compartments - and with that the complexity of the model - per voxel is not fixed but data-driven, which allows us to fit models of adequate complexity to the concentration time curves of all voxels. The parameters of the model remain nevertheless interpretable because of the underlying compartment model.
  
  
  CONCLUSIONS
  The proposed boosting approaches outperform all competing methods considered in this paper regarding the correct localization of tumors in DCE-MR images as well as the spatial homogeneity of the estimated number of compartments across the image, and the definition of the tumor edge.
authors.forename: M
authors.initials: M
authors.lastname: Feilke
authors.forename: B
authors.initials: B
authors.lastname: Bischl
authors.affiliation: Volker J. Schmid, Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstr. 33, 80539 München, Germany, E-mail: volker.schmid@lmu.de.
authors.forename: V J
authors.initials: VJ
authors.lastname: Schmid
authors.forename: J
authors.initials: J
authors.lastname: Gertheiss
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D003198:Computer Simulation; D003287:Contrast Media / Q000737:chemistry; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D017711:Nonlinear Dynamics
chemical_list: D003287:Contrast Media
keywords: Statistical computing; algorithms; biological models; computer-assisted image processing; magnetic resonance imaging; regression analysis; spatial regularization; statistical models