Meeting Banner
Abstract #0434

Improving oxygenation quantification from streamlined qBOLD data using amortized variational inference

Ivor J. A. Simpson1, Ashley McManamon 1, Alan J Stone2, Nicholas P Blockley3, Alessandro Colasanti4, and Mara Cercignani5
1Department of Informatics, University of Sussex, Brighton, United Kingdom, 2Department of Medical Physics and Clinical Engineering, St. Vincent's University Hospital, Dublin, Ireland, 3School of Life Sciences, University of Nottingham, Nottingham, United Kingdom, 4Brighton and Sussex Medical School, Brighton, United Kingdom, 5CUBRIC, Cardiff University, Cardiff, United Kingdom


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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here