Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/64383
Title: Modeling hyperoxia-induced BOLD signal dynamics to estimate cerebral blood flow, volume and mean transit time
Contributor(s): MacDonald, M Ethan (author); Berman, Avery J L (author); Mazerolle, Erin L (author); Williams, Rebecca J  (author)orcid ; Pike, G Bruce (author)
Publication Date: 2018-09
DOI: 10.1016/j.neuroimage.2018.05.066
Handle Link: https://hdl.handle.net/1959.11/64383
Abstract: 

A new method is proposed for obtaining cerebral perfusion measurements whereby blood oxygen level dependent (BOLD) MRI is used to dynamically monitor hyperoxia-induced changes in the concentration of deoxygenated hemoglobin in the cerebral vasculature. The data is processed using kinetic modeling to yield perfusion metrics, namely: cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). Ten healthy human subjects were continuously imaged with BOLD sequence while a hyperoxic (70% O2) state was interspersed with baseline periods of normoxia. The BOLD time courses were fit with exponential uptake and decay curves and a biophysical model of the BOLD signal was used to estimate oxygen concentration functions. The arterial input function was derived from end-tidal oxygen measurements, and a deconvolution operation between the tissue and arterial concentration functions was used to yield CBF. The venous component of the CBV was calculated from the ratio of the integrals of the estimated tissue and arterial concentration functions. Mean gray and white matter measurements were found to be: 61.6 ± 13.7 and 24.9 ± 4.0 ml 100 g-1 min-1 for CBF; 1.83 ± 0.32 and 1.10 ± 0.19 ml 100 g-1 for venous CBV; and 2.94 ± 0.52 and 3.73 ± 0.60 s for MTT, respectively. We conclude that it is possible to derive CBF, CBV and MTT metrics within expected physiological ranges via analysis of dynamic BOLD fMRI acquired during a period of hyperoxia.

Publication Type: Journal Article
Source of Publication: NeuroImage, v.178, p. 461-474
Publisher: Elsevier BV
Place of Publication: The Netherlands
ISSN: 1095-9572
1053-8119
Fields of Research (FoR) 2020: 3209 Neurosciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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