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A streamlined acquisition for mapping baseline brain oxygenation using quantitative BOLD.

Abstract:

Quantitative BOLD (qBOLD) is a non-invasive MR technique capable of producing quantitative measurements of the haemodynamic and metabolic properties of the brain. Here we propose a refinement of the qBOLD methodology, dubbed streamlined-qBOLD, in order to provide a clinically feasible method for mapping baseline brain oxygenation. In streamlined-qBOLD confounding signal contributions are minimised during data acquisition through the application of (i) a Fluid Attenuated Inversion Recovery (FLAIR) preparation to remove cerebral spinal fluid (CSF) signal contamination, (ii) a Gradient Echo Slice Excitation Profile Imaging (GESEPI) acquisition to reduce the effect of macroscopic magnetic field gradients and (iii) an Asymmetric Spin Echo (ASE) pulse sequence to directly measure the reversible transverse relaxation rate, R2′. Together these features simplify the application of the qBOLD model, improving the robustness of the resultant parametric maps. A theoretical optimisation framework was used to optimise acquisition parameters in relation to signal to noise ratio. In a healthy subject group (n = 7) apparent elevations in R2′ caused by partial volumes of CSF were shown to be reduced with the application of CSF nulling. Significant decreases in R2′ (p < 0.001) and deoxygenated blood volume (p < 0.01) were seen in cortical grey matter, across the group, with the application of CSF suppression. Quantitative baseline brain oxygenation parameter maps were calculated using qBOLD modelling and compared with literature values.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.neuroimage.2016.11.057

Authors


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Institution:
University of Oxford
Oxford college:
Pembroke College
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author


Publisher:
Elsevier
Journal:
Neuroimage More from this journal
Volume:
147
Pages:
79-88
Publication date:
2016-11-30
Acceptance date:
2016-11-22
DOI:
ISSN:
1053-8119 and 1095-9572


Language:
English
Keywords:
Pubs id:
pubs:666410
UUID:
uuid:b12bd419-58a0-4de5-9d31-7843671abfdf
Local pid:
pubs:666410
Source identifiers:
666410
Deposit date:
2017-01-05

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