Hierarchical Bayesian Microstructure Modelling Improves Voxelwise Quantification of Blood-Brain Barrier Water Exchange Rates

Motivation: Blood-brain barrier (BBB) water exchange (WEX) imaging techniques are increasingly used to quantify BBB dysfunction. However, WEX imaging is highly noise sensitive, which is typically addressed by averaging data spatially or across subjects. Goal(s): To obtain robust, subject-specific, voxel-wise WEX quantification from BBB filter exchange imaging (FEXI) data. Approach: We implement a hierarchical Bayesian model fitting method, which, by introducing a Gaussian prior for model parameters (estimated from the data), reduces sensitivity to voxel-wise noise. Results: Relative to conventional least-squares estimation, Bayesian model fitting improves parameter estimation qualitatively and quantitatively in synthetic and in ten test-retest volunteer datasets. Impact: Robust, subject-specific, voxel-wise WEX quantification from BBB filter exchange imaging (FEXI) data will enable localised BBB dysfunction to be identified in neurological disease, potentially enabling earlier diagnosis or discrimination between diseases.