Purpose
To compare regional oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2) quantified from the microvascular quantitative susceptibility mapping (QSM) using a hypercapnic gas challenge with those measured by the dual‐gas calibrated BOLD imaging (DGC‐BOLD) in healthy subjects.
Methods
Ten healthy subjects were scanned using a 3T MR system. The QSM data were acquired with a multi‐echo gradient‐echo sequence at baseline and hypercapnia. Cerebral blood flow data were acquired using the pseudo‐continuous arterial spin labeling technique. Baseline OEF and CMRO2 were calculated using QSM and cerebral blood flow measurements. The DGC‐BOLD data were also collected under a hypercapnic and a hyperoxic condition to yield baseline OEF and CMRO2. The QSM‐OEF and CMRO2 maps were compared with DGC‐BOLD OEF and CMRO2 maps using region of interest (vascular territories) analysis and Bland‐Altman plots.
Results
Hypercapnia is a robust stimulus for mapping OEF in combination with QSM. Average OEF in 16 vascular territory regions of interest across 10 subjects was 0.40 ± 0.04 by QSM‐OEF and 0.38 ± 0.09 by DGC‐BOLD. The average CMRO2 was 176 ± 35 and 167 ± 53 μmol O2/min/100g by QSM‐OEF and DGC‐BOLD, respectively. A Bland‐Altman plot of regional OEF and CMRO2 in regions of interest revealed a statistically significant but small difference (OEF difference = 0.02, CMRO2 difference = 9 μmol O2/min/100g, p < .05) between the 2 methods for the 10 healthy subjects.
Conclusion
Hypercapnic challenge–assisted QSM‐OEF is a feasible approach to quantify regional brain OEF and CMRO2. Compared with DGC‐BOLD, hypercapnia QSM‐OEF results in smaller intersubject variability and requires only 1 gas challenge.
Purpose
To use hyperoxia in combination with QSM to quantify microvascular oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) in healthy subjects and to cross‐validate results with those from hypercapnia QSM‐OEF.
Methods
Ten healthy subjects were scanned on a 3T MRI scanner. At baseline normoxia and during hyperoxia (PetO2 = +300 mmHg), QSM data were acquired using a multi‐echo gradient‐echo (GRE) sequence, and cerebral blood flow data were acquired using a pseudocontinuous arterial spin labeling sequence. The OEF and CMRO2 maps were computed and compared with those from hypercapnia QSM‐OEF, acquired in the same subjects, using correlation and Bland‐Altman analysis in 16 vascular territories.
Results
Hyperoxia QSM‐OEF produced physiologically reasonable OEF and CMRO2 values in all subjects (gray‐matter region of interest average OEF = 0.42 ± 0.04, average CMRO2 = 181 ± 34 μmol O2/min/100 g). When compared with hypercapnia QSM‐OEF, Bland‐Altman plots revealed small deviations (mean OEF difference = 0.015, mean CMRO2 difference = 4.9 μmol O2/min/100 g, P < .05). Good and excellent correlations of regional OEF and CMRO2 were found for the two methods. In addition, hyperoxia had minimal impact on cerebral blood flow (average gray‐matter cerebral blood flow was reduced by 7.5 ± 5.4%).
Conclusions
Hyperoxia in combination with QSM is a robust approach to measure OEF. Compared with hypercapnia, hyperoxia is more comfortable and has minimal impact on cerebral blood flow.
SummaryBlood oxygen level‐dependent (BOLD) functional magnetic resonance imaging (fMRI) studies of patients with cerebrovascular disease have largely ignored the confounds associated with abnormal cerebral blood flow, vascular reactivity and neurovascular coupling. We studied BOLD fMRI activation and cerebrovascular reactivity in moyamoya disease. To characterize the impact of remote vascular demands on BOLD fMRI measurements, we varied the vascular territories engaged by manipulating the experimental task performed by the participants. Vascular territories affected by disease were identified using BOLD cerebrovascular reactivity. Preliminary evidence from two patients pre‐ and postrevascularization surgery and four controls indicates that neurovascular coupling in affected brain regions can be modulated by the task‐related vascular demands in unaffected regions. In one patient studied, we observed that brain regions with improved cerebrovascular reactivity after surgery demonstrated normalized neurovascular coupling, that is the degree to which neurovascular coupling was modulated by task‐related vascular demands was decreased. We propose that variations in task‐dependent neurovascular coupling in patients with moyamoya disease are likely related to vascular steal. While preliminary, our findings are a proof of concept of the limitations of BOLD fMRI in cerebrovascular disease and suggest a role for assessment of cerebrovascular reactivity to improve interpretation of task‐related BOLD fMRI activation.
Purpose
To compare cortical gray matter oxygen extraction fraction (OEF) estimated from 2 MRI methods: (1) the quantitative susceptibility mapping (QSM) plus quantitative blood oxygen level dependent imaging (qBOLD) (QSM+qBOLD or QQ), and (2) the dual‐gas calibrated‐BOLD (DGCB) in healthy subjects; and to investigate the validity of iso‐cerebral metabolic rate of oxygen consumption assumption during hypercapnia using QQ.
Methods
In 10 healthy subjects, 3 tesla MRI including a multi‐echo gradient echo sequence at baseline and hypercapnia for QQ, as well as an EPI dual‐echo pseudo‐continuous arterial spin labeling for DGCB, were performed under a hypercapnic and a hyperoxic condition. OEFs from QQ and DGCB were compared using region of interest analysis and paired t test. For QQ, cerebral metabolic rate of oxygen consumption = cerebral blood flow*OEF*arterial oxygen content was generated for both baseline and hypercapnia, which were compared.
Results
Average OEF in cortical gray matter across 10 subjects from QQ versus DGCB was 35.5 ± 6.7% versus 38.0 ± 9.1% (P = .49) at baseline and 20.7 ± 4.4% versus 28.4 ± 7.6% (P = .02) in hypercapnia: OEF in cortical gray matter was significantly reduced as measured in QQ (P < .01) and in DGCB (P < .01). Cerebral metabolic rate of oxygen consumption (in μmol O2/min/100 g) was 168.2 ± 54.1 at baseline from DGCB and was 153.1 ± 33.8 at baseline and 126.4 ± 34.2 (P < .01) in hypercapnia from QQ.
Conclusion
The differences in OEF obtained from QQ and DGCB are small and nonsignificant at baseline but are statistically significant during hypercapnia. In addition, QQ shows a cerebral metabolic rate of oxygen consumption decrease (17.4%) during hypercapnia.
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