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A flow-diffusion model of oxygen transport for quantitative mapping of cerebral metabolic rate of oxygen (CMRO2) with single gas calibrated fMRI

Authors :
Antonio M Chiarelli
Michael Germuska
Hannah Chandler
Rachael Stickland
Eleonora Patitucci
Emma Biondetti
Daniele Mascali
Neeraj Saxena
Sharmila Khot
Jessica Steventon
Catherine Foster
Ana E Rodríguez-Soto
Erin Englund
Kevin Murphy
Valentina Tomassini
Felix W Wehrli
Richard G Wise
Publication Year :
2022
Publisher :
SAGE Publications, 2022.

Abstract

One promising approach for mapping CMRO2 is dual-calibrated functional MRI (dc-fMRI). This method exploits the Fick Principle to combine estimates of CBF from ASL, and OEF derived from BOLD-ASL measurements during arterial O2 and CO2 modulations. Multiple gas modulations are required to decouple OEF and deoxyhemoglobin-sensitive blood volume. We propose an alternative single gas calibrated fMRI framework, integrating a model of oxygen transport, that links blood volume and CBF to OEF and creates a mapping between the maximum BOLD signal, CBF and OEF (and CMRO2). Simulations demonstrated the method’s viability within physiological ranges of mitochondrial oxygen pressure, PmO2, and mean capillary transit time. A dc-fMRI experiment, performed on 20 healthy subjects using O2 and CO2 challenges, was used to validate the approach. The validation conveyed expected estimates of model parameters (e.g., low PmO2), with spatially uniform OEF maps (grey matter, GM, OEF spatial standard deviation ≈ 0.13). GM OEF estimates obtained with hypercapnia calibrated fMRI correlated with dc-fMRI (r = 0.65, p = 2·10−3). For 12 subjects, OEF measured with dc-fMRI and the single gas calibration method were correlated with whole-brain OEF derived from phase measures in the superior sagittal sinus (r = 0.58, p = 0.048; r = 0.64, p = 0.025 respectively). Simplified calibrated fMRI using hypercapnia holds promise for clinical application.

Details

Language :
English
ISSN :
0271678X
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....c9efb137e9899339e8c351189c094b2d