Abstract #2583

The Simultaneous Multiple-Voxel Processing of MRI Data using Bayesian Random Effects Modelling

Martin David King¹, Fernando Calamante², Chris A. Clark¹, David Gadian¹

¹Institute of Child Health, University College London, London, United Kingdom; ²Brain Research Institute, Melbourne, Australia

A common feature of many MRI data analyses in an independent, voxel-by-voxel treatment. In many applications this is expected to be inefficient in its use of the data, and improved parameter estimates should be attainable by adopting a statistical model in which image voxels are modelled as belonging to a population with an underlying statistical distribution. Among the methods that are well documented in the statistics literature is Bayesian spatial random effects modelling, implemented using Markov chain Monte Carlo. In this work we use dynamic susceptibility contrast data to illustrate the strengths of the Bayesian random effects modelling approach.