Summary
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. Objectives: 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.
Keywords
Statistical computing - computer-assisted image processing - statistical models -
algorithms - regression analysis - spatial regularization - biological models - magnetic
resonance imaging
