Matthew R. Orton1, David J. Collins1,
Dow-Mu Koh2, Michael Germuska1, Martin O. Leach1
1CR-UK and EPSRC Cancer Imaging Centre,
Institute of Cancer Research, Sutton, Surrey, United Kingdom; 2Department
of Radiology, Royal Marsden NHS Foundation Trust, Sutton, Surrey, United
Kingdom
Many
models have been proposed for describing diffusion-weighted data, but as the
environment of the diffusion process is known to be very complex in
biological systems, choosing an appropriate model is difficult. We present a Bayesian methodology for
estimating the posterior probability (uncertainty) of a given selection of
diffusion models, applied to clinical DWI data. This is of interest to indicate statistical
model uncertainty, and therefore uncertainty in the interpretation of the
data. By penalising over complicated
models, this methodology provides diffusion metrics that are more stable, and
therefore more sensitive to a wider range of treatment effects.