Skip to main content

Research Repository

Advanced Search

Model-based Bayesian inference of brain oxygenation using quantitative BOLD

Cherukara, Matthew T.; Stone, Alan J.; Chappell, Michael A.; Blockley, Nicholas P.

Model-based Bayesian inference of brain oxygenation using quantitative BOLD Thumbnail


Authors

Matthew T. Cherukara

Alan J. Stone

Profile Image

NIC BLOCKLEY Nicholas.Blockley@nottingham.ac.uk
Assistant Professor



Abstract

© 2019 The Authors Streamlined Quantitative BOLD (sqBOLD) is an MR technique that can non-invasively measure physiological parameters including Oxygen Extraction Fraction (OEF) and deoxygenated blood volume (DBV) in the brain. Current sqBOLD methodology rely on fitting a linear model to log-transformed data acquired using an Asymmetric Spin Echo (ASE) pulse sequence. In this paper, a non-linear model implemented in a Bayesian framework was used to fit physiological parameters to ASE data. This model makes use of the full range of available ASE data, and incorporates the signal contribution from venous blood, which was ignored in previous analyses. Simulated data are used to demonstrate the intrinsic difficulty in estimating OEF and DBV simultaneously, and the benefits of the proposed non-linear model are shown. In vivo data are used to show that this model improves parameter estimation when compared with literature values. The model and analysis framework can be extended in a number of ways, and can incorporate prior information from external sources, so it has the potential to further improve OEF estimation using sqBOLD.

Journal Article Type Article
Acceptance Date Aug 16, 2019
Online Publication Date Aug 17, 2019
Publication Date Nov 15, 2019
Deposit Date Aug 26, 2020
Publicly Available Date Sep 2, 2020
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1095-9572
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 202
Article Number 116106
DOI https://doi.org/10.1016/j.neuroimage.2019.116106
Public URL https://nottingham-repository.worktribe.com/output/3226077
Publisher URL https://www.sciencedirect.com/science/article/pii/S1053811919306974?via%3Dihub

Files





You might also like



Downloadable Citations