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Abstract #5269

Bayesian Inference of Brain Oxygenation and Deoxygenated Blood Volume in Acute Stroke using Streamlined Quantitative BOLD

Matthew T Cherukara1, Alan J Stone2, Davide Carone3, Radim Licenik3, George WJ Harston3, James Kennedy3, Michael A Chappell1, and Nicholas P Blockley2

1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 2FMRIB Centre, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom, 3Acute Stroke Programme, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom

Streamlined Quantitative-BOLD provides a method for quantifying brain oxygen metabolism, in particular, deoxygenated blood volume and oxygen extraction fraction, based on linear fitting of values obtained from an asymmetric spin-echo sequence. It is possible that a curve-fitting approach may yield more robust values for these parameters. This study investigated the feasibility of estimating brain metabolic and vascular parameters through a Bayesian framework, through simulations, and analysis of patient data. It was found that under the current model, simultaneous estimation of oxygen extraction fraction and blood volume was not reliable, suggesting a limit to the model or acquisition protocol.

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