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Abstract #1925

A Bayesian Random Effects Model for Enhancing Resolution in Diffusion MRI

Martin David King1, Daniel C. Alexander2, David G. Gadian1, Chris A. Clark1

1Institute of Child Health, University College London, London, United Kingdom; 2Computer Science, University College London, London, United Kingdom

Poor spatial resolution is a limitation in various diffusion MRI applications, including tractography. A Bayesian latent variables random effects model has been developed for increasing effective spatial resolution, based on a Markov random field treatment in which intrinsic Gaussian autoregressive priors are assigned to the fibre spherical coordinates. The model is used to separate crossing-fibres at the junction between the cingulum and corpus callosum, using diffusion MRI data acquired with a moderate b-value and 20 directions. The analyses were performed using Markov chain Monte Carlo simulation. Results demonstrate that a satisfactory separation of the crossing components can be obtained.