Model-based Bayesian inference of brain oxygenation using quantitative BOLD

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

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.

Citation

Cherukara, M. T., Stone, A. J., Chappell, M. A., & Blockley, N. P. (2019). Model-based Bayesian inference of brain oxygenation using quantitative BOLD. NeuroImage, 202, Article 116106. https://doi.org/10.1016/j.neuroimage.2019.116106

• 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