Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (FMRI) is most commonly used in a semi-quantitative manner to infer changes in brain activity. Despite the basis of the image contrast lying in the cerebral venous blood oxygenation level, quantification of absolute cerebral metabolic rate of oxygen consumption (CMRO 2 ) has only recently been demonstrated. Here we examine two approaches to the calibration of FMRI signal to measure absolute CMRO 2 using hypercapnic and hyperoxic respiratory challenges. The first approach is to apply hypercapnia and hyperoxia separately but interleaved in time and the second is a combined approach in which we apply hyperoxic challenges simultaneously with different levels of hypercapnia. Eleven healthy volunteers were studied at 3T using a dual gradient-echo spiral readout pulsed arterial spin labelling (ASL) imaging sequence. Respiratory challenges were conducted using an automated system of dynamic end-tidal forcing. A generalised BOLD signal model was applied, within a Bayesian estimation framework, that aims to explain the effects of modulation of CBF and arterial oxygen content to estimate venous deoxyhaemoglobin concentration ([dHb] 0 ). Using CBF measurements combined with the estimated oxygen extraction fraction (OEF), absolute CMRO 2 was calculated. The interleaved approach to hypercapnia and hyperoxia, as well as yielding estimates of CMRO 2 and OEF demonstrated a significant increase in regional CBF, venous oxygen saturation (SvO 2 ) (a decrease in OEF) and absolute CMRO 2 in visual cortex in response to a continuous (20 minute) visual task, demonstrating the potential for the method in measuring long term changes in CMRO 2 . The combined approach to oxygen and carbon dioxide modulation, as well as taking less time to acquire data, yielded whole brain grey matter estimates of CMRO 2 and OEF of 184±45 μmol/100g/min and 0.42±0.12 respectively, along with additional estimates of the vascular parameters α = 0.33±0.06, the exponent relating relative increases in CBF to CBV, and β = 1.35±0.13, the exponent relating deoxyhaemoglobin concentration to the relaxation rate R 2 *. Maps of cerebrovascular and cerebral metabolic parameters were also calculated. We show that combined modulation of oxygen and carbon dioxide can offer an experimentally more efficient approach to estimating OEF and absolute CMRO 2 along with the additional vascular parameters that form an important part of the commonly used calibrated FMRI signal model.

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, R'. 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 R' caused by partial volumes of CSF were shown to be reduced with the application of CSF nulling. Significant decreases in R' (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.

This study investigates diffusion tensor imaging (DTI) for providing microstructural insight into changes in arterial tissue by exploring how cell, collagen and elastin content effect fractional anisotropy (FA), mean diffusivity (MD) and tractography. Five ex vivo porcine carotid artery models (n = 6 each) were compared—native, fixed native, collagen degraded, elastin degraded and decellularised. Vessels were imaged at 7 T using a DTI protocol with b = 0 and 800 s/mm2 and 10 isotopically distributed directions. FA and MD were evaluated in the vessel media and compared across models. FA values measured in native (p < 0.0001), fixed native (p < 0.0001) and collagen degraded (p = 0.0018, p = 0.0016, respectively) were significantly higher than those in elastin degraded and decellularised arteries. Native and fixed native had significantly lower MD values than elastin degraded (p < 0.0001) and decellularised tissue (p = 0.0032, p = 0.0003, respectively). Significantly lower MD was measured in collagen degraded compared with the elastin degraded model (p = 0.0001). Tractography yielded helically arranged tracts for native and collagen degraded vessels only. FA, MD and tractography were found to be highly sensitive to changes in the microstructural composition of arterial tissue, specifically pointing to cell, not collagen, content as the dominant source of the measured anisotropy in the vessel wall.

Recently a new class of calibrated blood oxygen level dependent (BOLD) functional magnetic resonance imaging (MRI) methods were introduced to quantitatively measure the baseline oxygen extraction fraction (OEF). These methods rely on two respiratory challenges and a mathematical model of the resultant changes in the BOLD functional MRI signal to estimate the OEF. However, this mathematical model does not include all of the effects that contribute to the BOLD signal, it relies on several physiological assumptions and it may be affected by intersubject physiological variability. The aim of this study was to investigate these sources of systematic error and their effect on estimating the OEF. This was achieved through simulation using a detailed model of the BOLD signal. Large ranges for intersubject variability in baseline physiological parameters such as haematocrit and cerebral blood volume were considered. Despite this the uncertainty in the relationship between the measured BOLD signals and the OEF was relatively low. Investigations of the physiological assumptions that underlie the mathematical model revealed that OEF measurements are likely to be overestimated if oxygen metabolism changes during hypercapnia or cerebral blood flow changes under hyperoxia. Hypoxic hypoxia was predicted to result in an underestimation of the OEF, whilst anaemic hypoxia was found to have only a minimal effect.