Fully Bayesian Multi-model Inference for Parameter Estimation in DCE-MRI

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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