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, 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.

Prospects for investigating brain oxygenation in acute stroke: Experience with a non‐contrast quantitative BOLD based approach

“…However, further work is required to establish the accuracy and repeatability of this technique. From Figure it can be seen that the error in DBV can be quite high and from previous work it has been noted that the accuracy of this measurement requires improvement (Stone & Blockley, ).…”

“…A FLAIR‐GASE acquisition was used to measure baseline brain oxygenation using the sqBOLD approach (Stone & Blockley, ) (96 × 96 matrix, field of view = 220 mm 2 , nine 5 mm slabs consisting of four 1.25 mm sub‐slices, 100% partition oversampling, 1 mm slice gap, TR/TE = 3,000/82 ms, TI FLAIR = 1,210 ms, ASE‐sampling scheme τ start /τ finish /Δτ = −16/64/8 ms, scan duration 4 min 30 s). FLAIR‐GASE consists of three separate components, nulling of CSF partial volumes using FLuid Attenuated Inversion Recovery (FLAIR; Hajnal et al, ), direct measurement of R 2 ′ using an asymmetric spin echo (ASE; Wismer et al, ) and minimisation of MFI using gradient echo slice excitation profile imaging (GESEPI; Yang, Williams, Demeure, Mosher, & Smith, ).…”

“…The model fit was inversely weighted for τ , with data being acquired at higher values of τ receiving less weighting in the fit, due to the increasing contribution of noise with increasing signal decay. This helps to account for the lower SNR in data acquired at longer τ ‐values (Stone & Blockley, ). Parameter maps of [dHb] were calculated using Equation , where DBV and R 2 ′ were estimated as above and other parameters are known or assumed constants (Δχ 0 = 0.264 × 10 −6 (He & Yablonskiy, ), κ = 0.03 (McPhee & Hammer, )). …”

“…The Kruskal–Wallis test is a version of the classical one‐way ANOVA that does not require the assumption of normally distributed data. This non‐parametric test was chosen as the distribution of [dHb] values in healthy grey matter have previously been found to be nonnormally distributed (Stone & Blockley, ). On rejecting the null hypothesis, post hoc pairwise comparisons between the different tissue outcome ROIs were performed using the Tukey–Kramer method (honest significant difference test) to consider which pairs were significantly different.…”

“…Recently we proposed a refinement of the qBOLD method targeted at minimising confounding effects during data acquisition, rather than by postprocessing, which we term streamlined‐qBOLD (sqBOLD; Stone & Blockley, ). By minimising the influence of nuisance signals (macroscopic field inhomogeneities, MFIs; cerebral spinal fluid, CSF; and R 2 ‐weighting) during image acquisition the application of the qBOLD model is simplified, improving the robustness of the resultant oxygenation maps.…”