  In the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance
Imaging (DCE-MRI) compartment models allow to describe the uptake of contrast
medium with biological meaningful kinetic parameters. As simple models often
fail to adequately describe the observed uptake behavior, more complex
compartment models have been proposed. However, the nonlinear regression
problem arising from more complex compartment models often suffers from
parameter redundancy. In this paper, we incorporate spatial smoothness on the
kinetic parameters of a two tissue compartment model by imposing Gaussian
Markov random field priors on them. We analyse to what extent this spatial
regularisation helps to avoid parameter redundancy and to obtain stable
parameter estimates. Choosing a full Bayesian approach, we obtain posteriors
and point estimates running Markov Chain Monte Carlo simulations. The proposed
approach is evaluated for simulated concentration time curves as well as for in
vivo data from a breast cancer study.
