Abstract #2339
Fully Bayesian Multi-model Inference for Parameter Estimation in DCE-MRI
Tammo Rukat 1 and Stefan A Reinsberg 1
1
Department of Physics and Astronomy,
University of British Columbia, Vancouver, British
Columbia, Canada
A fully Bayesian model mixing method for the estimation
of haemodynamic parameters from DCE-MRI is being
assessed. In particularly we examine the capability of
weighing models of different complexity, such that the
resulting parameter can be expected to be more accurate
than the estimate from any single model. The
Watanabe-Akaike information criterion (WAIC) is derived
from the posterior likelihood distributions of the model
parameters, which was sampled by adaptive MCMC. WAIC
serves to calculate model mixing weights. This method is
shown to be superior to the choice of any single model.
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