Streamlined qBOLD acquisitions enable experimentally straightforward observations of brain metabolism. High quality R2’ maps are easily derived; however, the oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously defined from noisy data. Accordingly, standard approaches yield noisy and underestimated OEF maps and overestimate DBV.
This work uses synthetic data to learn models for voxelwise prior distributions, which are subsequently leveraged in an amortized variational Bayesian inference model. We demonstrate our approach enables inference of smooth OEF and DBV maps, with a physiologically realistic distribution, and illustrate voxelwise differences in OEF between subjects at rest and undergoing hyperventilations.