A1106
Title: Bayesian image analysis in Fourier space for neuroimaging
Authors: John Kornak - University of California, San Francisco (United States) [presenting]
Karl Young - University of California San Francisco (United States)
Eric Friedman - International Computer Science Institute Berkeley (United States)
Konstantinos Bakas - King Abdullah University of Science and Technology (Saudi Arabia)
Hernando Ombao - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia)
Abstract: For more than 30 years now, Bayesian image analysis has been a leading approach to image reconstruction and enhancement. The idea of the approach is to balance a priori expectations of image characteristics (the prior) with a model for the image degradation process (the likelihood). The conventional Bayesian modelling approach as defined in image space, implements priors that describe inter-dependence between spatial locations on the image lattice (commonly through Markov random field, MRF, models) and can therefore be difficult to model and compute. Bayesian image analysis in Fourier space (BIFS) provides for an alternate approach that can generate a wide range of models, including ones with similar properties to conventional models but with a reduced computational burden; the originally complex high-dimensional estimation problem in image space can be similarly modelled as a series of (trivially parallelizable) independent one-dimensional problems in Fourier space. Development of different prior models in Fourier space will be examined, and it is illustrated with neuroimaging applications of BIFS applied to 1) longitudinal structural MRI for evaluating brain tumour evolution and 2) diagnosis of frontotemporal dementia based on perfusion-weighted MRI and PET.
