Oxygen Extraction Fraction Maps Research Articles

Oxygen Extraction Fraction Mapping on Admission Magnetic Resonance Imaging May Predict Recovery of Hyperacute Ischemic Brain Lesions After Successful Thrombectomy: A Retrospective Observational Study.

In acute stroke, diffusion-weighted imaging (DWI) is used to assess the ischemic core. Dynamic-susceptibility contrast perfusion magnetic resonance imaging allows an estimation of the oxygen extraction fraction (OEF), but the outcome of DWI lesions with increased OEF postrecanalization is unclear. This study investigated the impact of OEF on the fate of DWI lesions in patients achieving recanalization after thrombectomy. This was a retrospective analysis of the HIBISCUS-STROKE cohort (Cohort of Patients to Identify Biological and Imaging Markers of Cardiovascular Outcomes in Stroke; NCT: 03149705), a single-center observational study that prospectively enrolled patients who underwent magnetic resonance imaging triage for thrombectomy and a day-6 T2-fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging. Automated postprocessing of admission dynamic-susceptibility contrast perfusion magnetic resonance imaging generated OEF maps. At visual analysis, the OEF status within DWI lesions was assessed in comparison to the contralateral side and correlated with volume changes (difference of ischemic lesion between admission DWI and registered day-6 T2-FLAIR). At voxel-based analysis, recovered DWI regions (lesions present on the admission DWI but absent on the registered day-6 T2-FLAIR) and nonrecovered regions were segmented to extract semiquantitative OEF values. Of the participants enrolled from 2016 to 2022, 134 of 321 (41.7%) were included (median age, 71.0 years; 58.2% male; median baseline National Institutes of Health Scale score, 15.0). At visual analysis, 46 of 134 (34.3%) patients had increased OEF within DWI lesions. These patients were more likely to show a reduction in ischemic lesion volumes compared with those without increased OEF (median change, -4.0 versus 4.8 mL; P<0.0001). Multivariable analysis indicated that increased OEF within DWI lesions was associated with a reduction in ischemic lesion volumes from admission DWI to day-6 T2-FLAIR (odds ratio, 0.68 [95% CI, 0.49-0.87]; P=0.008). At voxel-based analysis, recovered DWI regions had increased OEF, while nonrecovered regions had decreased OEF (median, 126.9% versus -27.0%; P<0.0001). Increased OEF within hyperacute DWI lesions was associated with ischemic lesion recovery between admission DWI and day-6 T2-FLAIR in patients achieving recanalization after thrombectomy. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03149705.

Oxygen extraction fraction mapping based combining quantitative susceptibility mapping and quantitative blood oxygenation level-dependent imaging model using multi-delay PCASL

Background and purposeThe oxygen extraction fraction is an essential biomarker for the assessment of brain metabolism. A recently proposed method combined with quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude enables noninvasive mapping of the oxygen extraction fraction. Our study investigated the oxygen extraction fraction mapping variations of single-delay and multi-delay arterial spin-labeling. Materials and methodsA total of twenty healthy participants were enrolled. The multi-echo spoiled gradient-echo, multi-delay arterial spin-labeling, and magnetization-prepared rapid gradient echo sequences were acquired at 3.0 T. The mean oxygen extraction fraction was generated under a single delay time of 1780 ms, multi-delay arterial spin-labeling of transit-corrected cerebral blood flow, and multi-delay arterial spin-labeling of arterial cerebral blood volume. The results were compared via paired t tests and the Wilcoxon test. Linear regression analyses were used to investigate the relationships among the oxygen extraction fraction, cerebral blood flow, and venous cerebral blood volume. ResultsThe oxygen extraction fraction estimate with multi-delay arterial spin-labeling yielded a significantly lower value than that with single-delay arterial spin-labeling. The average values for the whole brain under single-delay arterial spin-labeling, multi-delay arterial spin-labeling of transit-corrected cerebral blood flow, and multi-delay arterial spin-labeling of arterial cerebral blood volume were 41.5 ± 1.7 % (P < 0.05), 41.3 ± 1.9 % (P < 0.001), and 40.9 ± 1.9 % (N = 20), respectively. The oxygen extraction fraction also showed a significant inverse correlation with the venous cerebral blood volume under steady-state conditions when multi-delay arterial spin-labeling was used (r = 0.5834, p = 0.0069). ConclusionThese findings suggest that the oxygen extraction fraction is significantly impacted by the arterial spin-labeling methods used in the quantitative susceptibility mapping plus the quantitative blood oxygen level-dependent model, indicating that the differences should be accounted for when employing oxygen extraction fraction mapping based on this model in diseases.

Abstract WMP63: Oxygen Metabolic Stress Attributes Intrinsic Vulnerability to Watershed Regions in Healthy Individuals

Introduction: Physiological imaging studies in patients with severe steno-occlusive carotid disease (SSCD) reveal hemodynamic compromise as a mechanism underlying watershed infarcts (WI). However, it is unclear if the watershed region (WR) is intrinsically vulnerable in healthy individuals. We measured cerebral blood flow (CBF) and oxygen extraction fraction (OEF) in healthy individuals to determine if the WR was under metabolic stress, and therefore an intrinsically vulnerable region. Methods: To define a common WR, we created an “average WI density map” using MRIs from patients with diffusion restriction ipsilateral to SSCD from a single academic center (7/10 to 8/20). WI density maps were created using 5%, 10% and 15% incidence thresholds. Maps were overlaid onto coregistered CBF and OEF maps generated from pseudo-continuous arterial spin labeling (pCASL) and asymmetric spin echo sequences, respectively, performed on healthy controls aged &lt;55 with no vascular risk factors. OEF was normalized to whole brain values. White matter OEF and CBF was compared within vs. outside the WR using the Kruskal Wallis test. Results: 47 MRIs (mean age 62 years, 55 % females) were used to create average WI density maps using 3 thresholds (Fig A). Average CBF and OEF maps were created from 29 healthy volunteers (mean age 39 years, 59 % females). WI maps were superimposed on the CBF and OEF maps from healthy controls. OEF was higher (p&lt;0.001), and CBF lower (p&lt;0.001) within compared to outside the watershed regions, regardless of incidence threshold used (10% threshold shown in Fig. B-C). Conclusion: Increased OEF and decreased CBF suggest oxygen metabolic stress in the WR, suggesting that the deep white matter may be intrinsically vulnerable even in healthy individuals. Indeed, several brain pathologies (WI, white matter hyperintensities of presumed vascular origin, and multiple sclerosis lesions) are known to fall within this region.

Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra-tissue with low perfusion yet intact cellular integrity-making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders.

Spatiotemporal patterns of brain iron-oxygen metabolism in patients with Parkinson's disease.

Iron deposition and mitochondrial dysfunction are closely associated with the genesis and progression of Parkinson's disease (PD). This study aims to extract susceptibility and oxygen extraction fraction (OEF) values of deep grey matter (DGM) to explore spatiotemporal progression patterns of brain iron-oxygen metabolism in PD. Ninety-five PD patients and forty healthy controls (HCs) were included. Quantitative susceptibility mapping (QSM) and OEF maps were computed from MRI multi-echo gradient echo data. Analysis of covariance (ANCOVA) was used to compare mean susceptibility and OEF values in DGM between early-stage PD (ESP), advanced-stage PD (ASP) patients and HCs. Then Granger causality analysis on the pseudo-time-series of MRI data was applied to assess the causal effect of early altered nuclei on iron content and oxygen extraction in other DGM nuclei. The susceptibility values in substantia nigra (SN), red nucleus, and globus pallidus (GP) significantly increased in PD patients compared with HCs, while the iron content in GP did not elevate obviously until the late stage. The mean OEF values for the caudate nucleus, putamen, and dentate nucleus were higher in ESP patients than in ASP patients or/and HCs. We also found that iron accumulation progressively expands from the midbrain to the striatum. These alterations were correlated with clinical features and improved AUC for early PD diagnosis to 0.824. Abnormal cerebral iron deposition and tissue oxygen utilization in PD measured by QSM and OEF maps could reflect pathological alterations in neurodegenerative processes and provide valuable indicators for disease identification and management. Noninvasive assessment of cerebral iron-oxygen metabolism may serve as clinical evidence of pathological changes in PD and improve the validity of diagnosis and disease monitoring. • Quantitative susceptibility mapping and oxygen extraction fraction maps indicated the cerebral pathology of abnormal iron accumulation and oxygen metabolism in Parkinson's disease. • Iron deposition is mainly in the midbrain, while altered oxygen metabolism is concentrated in the striatum and cerebellum. • The susceptibility and oxygen extraction fraction values in subcortical nuclei were associated with clinical severity.

Assessing Cerebral Oxygen Metabolism Changes in Patients With Preeclampsia Using Voxel-Based Morphometry of Oxygen Extraction Fraction Maps in Magnetic Resonance Imaging.

The objective of this study was to analyze the different brain oxygen metabolism statuses in preeclampsia using magnetic resonance imaging and investigate the factors that affect cerebral oxygen metabolism in preeclampsia. Forty-nine women with preeclampsia (mean age 32.4 years; range, 18-44 years), 22 pregnant healthy controls (PHCs) (mean age 30.7 years; range, 23-40 years), and 40 non-pregnant healthy controls (NPHCs) (mean age 32.5 years; range, 20-42 years) were included in this study. Brain oxygen extraction fraction (OEF) values were computed using quantitative susceptibility mapping (QSM) plus quantitative blood oxygen level-dependent magnitude-based OEF mapping (QSM + quantitative blood oxygen level-dependent imaging or QQ) obtained with a 1.5-T scanner. Voxel-based morphometry (VBM) was used to investigate the differences in OEF values in the brain regions among the groups. Among the three groups, the average OEF values were significantly different in multiple brain areas, including the parahippocampus, multiple gyri of the frontal lobe, calcarine, cuneus, and precuneus (all P-values were less than 0.05, after correcting for multiple comparisons). The average OEF values of the preeclampsia group were higher than those of the PHC and NPHC groups. The bilateral superior frontal gyrus/bilateral medial superior frontal gyrus had the largest size of the aforementioned brain regions, and the OEF values in this area were 24.2 ± 4.6, 21.3 ± 2.4, and 20.6 ± 2.8 in the preeclampsia, PHC, and NPHC groups, respectively. In addition, the OEF values showed no significant differences between NPHC and PHC. Correlation analysis revealed that the OEF values of some brain regions (mainly involving the frontal, occipital, and temporal gyrus) were positively correlated with age, gestational week, body mass index, and mean blood pressure in the preeclampsia group (r = 0.361-0.812). Using whole-brain VBM analysis, we found that patients with preeclampsia had higher OEF values than controls.

Non-Invasive measurement of the cerebral metabolic rate of oxygen using MRI in rodents.

Malfunctions of oxygen metabolism are suspected to play a key role in a number of neurological and psychiatric disorders, but this hypothesis cannot be properly investigated without an in-vivo non-invasive measurement of brain oxygen consumption. We present a new way to measure the Cerebral Metabolic Rate of Oxygen (CMRO 2) by combining two existing magnetic resonance imaging techniques, namely arterial spin-labelling and oxygen extraction fraction mapping. This method was validated by imaging rats under different anaesthetic regimes and was strongly correlated to glucose consumption measured by autoradiography.

Non-Invasive measurement of the cerebral metabolic rate of oxygen using MRI in rodents

Malfunctions of oxygen metabolism are suspected to play a key role in a number of neurological and psychiatric disorders, but this hypothesis cannot be properly investigated without an in-vivo non-invasive measurement of brain oxygen consumption. We present a new way to measure the Cerebral Metabolic Rate of Oxygen (CMRO2) by combining two existing magnetic resonance imaging techniques, namely arterial spin-labelling and oxygen extraction fraction mapping. This method was validated by imaging rats under different anaesthetic regimes and was strongly correlated to glucose consumption measured by autoradiography.

Oxygen extraction fraction (OEF) assesses cerebral oxygen metabolism of deep gray matter in patients with pre-eclampsia.

The objective of this study was to compare oxygen extraction fraction (OEF) values in the deep gray matter (GM) of pre-eclampsia (PE) patients, pregnant healthy controls (PHCs), and non-pregnant healthy controls (NPHCs) to explore their brain oxygen metabolism differences in GM. Forty-seven PE patients, forty NPHCs, and twenty-one PHCs were included. Brain OEF values were computed from quantitative susceptibility mapping (QSM) plus quantitative blood oxygen level-dependent magnitude (QSM + qBOLD = QQ)-based mapping. One-way ANOVA was used to compare mean OEF values in the three groups. The area under the curve of the mean OEF value in each region of interest was estimated using a receiver operating characteristic curve analysis. We found that the mean OEF values in the thalamus, putamen, caudate nucleus, pallidum, and substantia nigra were significantly different in these three groups (F = 5.867, p = 0.004; F = 5.142, p = 0007; F = 6.158, p = 0.003; F = 6.319, p = 0.003; F = 5.491, p = 0.005). The mean OEF values for these 5 regions were higher in PE patients than in NPHCs and in PHCs (p < 0.05). The AUC of these ROIs ranged from 0.673 to 0.692 (p < 0.01) and cutoff values varied from 35.1 to 36.6%, indicating that the OEF values could discriminate patients with and without PE. Stepwise multivariate analysis revealed that the OEF values correlated with hematocrit in pregnant women (r = 0.353, p = 0.003). OEF values in the brains of pregnant women can be measured in clinical practice using QQ-based OEF mapping for noninvasive assessment of hypertensive disorders. • Pre-eclampsia is a hypertensive disorder associated with abnormalities in brain oxygen extraction. • Oxygen extraction fraction (OEF) is an indicator of brain tissue viability and function. QQ-based mapping of OEF is a new MRI technique that can noninvasively quantify brain oxygen metabolism. • OEF values in the brains of pregnant women can be measured for noninvasive assessment of hypertensive disorders in clinical practice.

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.

To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET). The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test. In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps. QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.

Application of Cluster Analysis of Time Evolution for Magnetic Resonance Imaging -Derived Oxygen Extraction Fraction Mapping: A Promising Strategy for the Genetic Profile Prediction and Grading of Glioma.

Background: The intratumoral heterogeneity of oxygen metabolism and angiogenesis are core hallmarks of glioma, unveiling that genetic aberrations associated with magnetic resonance imaging (MRI) phenotypes may aid in the diagnosis and treatment of glioma.Objective: To explore the predictability of MRI-based oxygen extraction fraction (OEF) mapping using cluster analysis of time evolution (CAT) for genetic profiling and glioma grading.Methods: Ninety-one patients with histopathologically confirmed glioma were examined with CAT for quantitative susceptibility mapping and quantitative blood oxygen level–dependent magnitude-based OEF mapping and dynamic contrast-enhanced (DCE) MRI. Imaging biomarkers, including oxygen metabolism (OEF) and angiogenesis [volume transfer constant, cerebral blood volume (CBV), and cerebral blood flow], were investigated to predict IDH mutation, O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status, receptor tyrosine kinase (RTK) subgroup, and differentiation of glioblastoma (GBM) vs. lower-grade glioma (LGG). The corresponding DNA sequencing was also obtained. Results were compared with DCE-MRI using receiver operating characteristic (ROC) analysis.Results: IDH1-mutated LGGs exhibited significantly lower OEF and hypoperfusion than IDH wild-type tumors (all p < 0.01). OEF and perfusion metrics showed a tendency toward higher values in MGMT unmethylated GBM, but only OEF retained significance (p = 0.01). Relative prevalence of RTK alterations was associated with increased OEF (p = 0.003) and perfusion values (p < 0.05). ROC analysis suggested OEF achieved best performance for IDH mutation detection [area under the curve (AUC) = 0.828]. None of the investigated parameters enabled prediction of MGMT status except OEF with a moderate AUC of 0.784. Predictive value for RTK subgroup was acceptable by using OEF (AUC = 0.764) and CBV (AUC = 0.754). OEF and perfusion metrics demonstrated excellent performance in glioma grading. Moreover, mutational landscape revealed hypoxia or angiogenesis-relevant gene signatures were associated with specific imaging phenotypes.Conclusion: CAT for MRI-based OEF mapping is a promising technology for oxygen measurement and along with perfusion MRI can predict genetic profiles and tumor grade in a non-invasive and clinically relevant manner.Clinical Impact: Physiological imaging provides an in vivo portrait of genetic alterations in glioma and offers a potential strategy for non-invasively selecting patients for individualized therapies.

Brain oxygen extraction fraction mapping in patients with multiple sclerosis

We aimed to demonstrate the feasibility of whole brain oxygen extraction fraction (OEF) mapping for measuring lesion specific and regional OEF abnormalities in multiple sclerosis (MS) patients. In 22 MS patients and 11 healthy controls (HC), OEF and neural tissue susceptibility () maps were computed from MRI multi-echo gradient echo data. In MS patients, 80 chronic active lesions with hyperintense rim on quantitative susceptibility mapping were identified, and the mean OEF and within the rim and core were compared using linear mixed-effect model analysis. The rim showed higher OEF and than the core: relative to their adjacent normal appearing white matter, OEF contrast = −6.6 ± 7.0% vs. −9.8 ± 7.8% (p < 0.001) and contrast = 33.9 ± 20.3 ppb vs. 25.7 ± 20.5 ppb (p = 0.017). Between MS and HC, OEF and were compared using a linear regression model in subject-based regions of interest. In the whole brain, compared to HC, MS had lower OEF, 30.4 ± 3.3% vs. 21.4 ± 4.4% (p < 0.001), and higher , −23.7 ± 7.0 ppb vs. −11.3 ± 7.7 ppb (p = 0.018). Our feasibility study suggests that OEF may serve as a useful quantitative marker of tissue oxygen utilization in MS.

Non-Invasive measurement of the cerebral metabolic rate of oxygen using MRI in rodents

Malfunctions of oxygen metabolism are suspected to play a key role in a number of neurological and psychiatric disorders, but this hypothesis cannot be properly investigated without an in-vivo non-invasive measurement of brain oxygen consumption. We present a new way to measure the Cerebral Metabolic Rate of Oxygen (CMRO2) by combining two existing magnetic resonance imaging techniques, namely arterial spin-labelling and oxygen extraction fraction mapping. This method was validated by imaging rats under different anaesthetic regimes and was strongly correlated to glucose consumption measured by autoradiography.

Non-Invasive measurement of the cerebral metabolic rate of oxygen using MRI in rodents

Malfunctions of oxygen metabolism are suspected to play a key role in a number of neurological and psychiatric disorders, but this hypothesis cannot be properly investigated without an in-vivo non-invasive measurement of brain oxygen consumption. We present a new way to measure the Cerebral Metabolic Rate of Oxygen (CMRO2) by combining two existing magnetic resonance imaging techniques, namely arterial spin-labelling and oxygen extraction fraction mapping. This method was validated by imaging rats under different anaesthetic regimes and was strongly correlated to glucose consumption measured by autoradiography.

Initial Experience of Challenge-Free MRI-Based Oxygen Extraction Fraction Mapping of Ischemic Stroke at Various Stages: Comparison With Perfusion and Diffusion Mapping.

MRI-based oxygen extraction fraction imaging has a great potential benefit in the selection of clinical strategies for ischemic stroke patients. This study aimed to evaluate the performance of a challenge-free oxygen extraction fraction (OEF) mapping in a cohort of acute and subacute ischemic stroke patients. Consecutive ischemic stroke patients (a total of 30 with 5 in the acute stage, 19 in the early subacute stage, and 6 in the late subacute stage) were recruited. All subjects underwent MRI including multi-echo gradient echo (mGRE), diffusion weighted imaging (DWI), and 3D-arterial spin labeling (ASL). OEF maps were generated from mGRE phase + magnitude data, which were processed using quantitative susceptibility mapping (QSM) + quantitative blood oxygen level-dependent (qBOLD) imaging with cluster analysis of time evolution. Cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) maps were reconstructed from 3D-ASL and DWI, respectively. Further, cerebral metabolic rate of oxygen (CMRO2) was calculated as the product of CBF and OEF. OEF, CMRO2, CBF, and ADC values in the ischemic cores (absolute values) and their contrasts to the contralateral regions (relative values) were evaluated. One-way analysis of variance (ANOVA) was used to compare OEF, CMRO2, CBF, and ADC values and their relative values among different stroke stages. The OEF value of infarct core showed a trend of decrease from acute, to early subacute, and to late subacute stages of ischemic stroke. Significant differences among the three stroke stages were only observed in the absolute OEF (F = 6.046, p = 0.005) and relative OEF (F = 5.699, p = 0.009) values of the ischemic core, but not in other measurements (absolute and relative CMRO2, CBF, ADC values, all values of p > 0.05). In conclusion, the challenge-free QSM + qBOLD-generated OEF mapping can be performed on stroke patients. It can provide more information on tissue viability that was not available with CBF and ADC and, thus, may help to better manage ischemic stroke patients.

Quantification of brain oxygen extraction fraction using QSM and a hyperoxic challenge.

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. 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. 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/100g). When compared with hypercapnia QSM-OEF, Bland-Altman plots revealed small deviations (mean OEF difference = 0.015, mean CMRO2 difference = 4.9μmol O2 /min/100g, 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%). 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.

Oxygen extraction fraction mapping with multi-parametric quantitative BOLD MRI: Reduced transverse relaxation bias using 3D-GraSE imaging

Magnetic resonance imaging (MRI)-based quantification of the blood-oxygenation-level-dependent (BOLD) effect allows oxygen extraction fraction (OEF) mapping. The multi-parametric quantitative BOLD (mq-BOLD) technique facilitates relative OEF (rOEF) measurements with whole brain coverage in clinically applicable scan times. Mq-BOLD requires three separate scans of cerebral blood volume and transverse relaxation rates measured by gradient-echo (1/T2∗) and spin-echo (1/T2). Although the current method is of clinical merit in patients with stroke, glioma and internal carotid artery stenosis (ICAS), there are relaxation measurement artefacts that impede the sensitivity of mq-BOLD and artificially elevate reported rOEF values.We posited that T2-related biases caused by slice refocusing imperfections during rapid 2D-GraSE (Gradient and Spin Echo) imaging can be reduced by applying 3D-GraSE imaging sequences, because the latter requires no slice selective pulses. The removal of T2-related biases would decrease overestimated rOEF values measured by mq-BOLD. We characterized effects of T2-related bias in mq-BOLD by comparing the initially employed 2D-GraSE and two proposed 3D-GraSE sequences to multiple single spin-echo reference measurements, both in vitro and in vivo. A phantom and 25 participants, including young and elderly healthy controls as well as ICAS-patients, were scanned. We additionally proposed a procedure to reliably identify and exclude artefact affected voxels. In the phantom, 3D-GraSE derived T2 values had 57% lower deviation from the reference. For in vivo scans, the formerly overestimated rOEF was reduced by −27% (p ​< ​0.001). We obtained rOEF ​= ​0.51, which is much closer to literature values from positron emission tomography (PET) measurements. Furthermore, increased sensitivity to a focal rOEF elevation in an ICAS-patient was demonstrated.In summary, the application of 3D-GraSE improves the mq-BOLD-based rOEF quantification while maintaining clinically feasible scan times. Thus, mq-BOLD with non-slice selective T2 imaging is highly promising to improve clinical diagnostics of cerebrovascular diseases such as ICAS.

Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD.

To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-Newton (QN) method for numerical optimization. Random combinations of OEF, deoxygenated blood volume ( ), R2 , and nonblood magnetic susceptibility ( ) with each parameter following a Gaussian distribution that represented physiological gray matter and white matter values were used to simulate quantitative BOLD signals and QSM values. An ANN was trained with the simulated data with added Gaussian noise. The ANN was applied to multigradient echo brain data of 7 healthy subjects, and the reconstructed parameters and maps were compared to QN results using Student t test and Bland-Altman analysis. Intersubject means and SDs of gray matter were OEF %, Hz, %, ppb for ANN; and OEF = %, Hz, %, ppb for QN, with a significant difference ( ) for , , and . For white matter, they were OEF = %, Hz, %, ppb for ANN; and OEF %, Hz, %, ppb for QN, with a significant difference ( ) for OEF and . ANN revealed more gray-white matter contrast but less intersubject variation in OEF than QN. In contrast to QN, the ANN reconstruction did not need an additional sequence for parameter initialization and took approximately 1 s rather than roughly 1 h. ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.

Detecting resting-state brain activity using OEF-weighted imaging

Traditional resting-state functional magnetic resonance imaging (fMRI) is mainly based on the blood oxygenation level-dependent (BOLD) contrast. The oxygen extraction fraction (OEF) represents an important parameter of brain metabolism and is a key biomarker of tissue viability, detecting the ratio of oxygen utilization to oxygen delivery. Investigating spontaneous fluctuations in the OEF-weighted signal is crucial for understanding the underlying mechanism of brain activity because of the immense energy budget during the resting state. However, due to the poor temporal resolution of OEF mapping, no studies have reported using OEF contrast to assess resting-state brain activity. In this fMRI study, we recorded brain OEF-weighted fluctuations for 10 min in healthy volunteers across two scanning visits, using our recently developed pulse sequence that can acquire whole-brain voxel-wise OEF-weighted signals with a temporal resolution of 3 s. Using both group-independent component analysis and seed-based functional connectivity analysis, we robustly identified intrinsic brain networks, including the medial visual, lateral visual, auditory, default mode and bilateral executive control networks, using OEF contrast. Furthermore, we investigated the resting-state local characteristics of brain activity based on OEF-weighted signals using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). We demonstrated that the gray matter regions of the brain, especially those in the default mode network, showed higher ReHo and fALFF values with the OEF contrast. Moreover, voxel-wise test-retest reliability comparisons across the whole brain demonstrated that the reliability of resting-state brain activity based on the OEF contrast was moderate for the network indices and high for the local activity indices, especially for ReHo. Although the reliabilities of the OEF-based indices were generally lower than those based on BOLD, the reliability of OEF-ReHo was slightly higher than that of BOLD-ReHo, with a small effect size, which indicated that OEF-ReHo could be used as a reliable index for characterizing resting-state local brain activity as a complement to BOLD. In conclusion, OEF can be used as an effective contrast to study resting-state brain activity with a medium to high test-retest reliability.

Cerebral metabolic rate of oxygen (CMRO2 ) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level-dependent imaging (qBOLD).

To map the cerebral metabolic rate of oxygen (CMRO2 ) by estimating the oxygen extraction fraction (OEF) from gradient echo imaging (GRE) using phase and magnitude of the GRE data. 3D multi-echo gradient echo imaging and perfusion imaging with arterial spin labeling were performed in 11 healthy subjects. CMRO2 and OEF maps were reconstructed by joint quantitative susceptibility mapping (QSM) to process GRE phases and quantitative blood oxygen level-dependent (qBOLD) modeling to process GRE magnitudes. Comparisons with QSM and qBOLD alone were performed using ROI analysis, paired t-tests, and Bland-Altman plot. The average CMRO2 value in cortical gray matter across subjects were 140.4 ± 14.9, 134.1 ± 12.5, and 184.6 ± 17.9 μmol/100 g/min, with corresponding OEFs of 30.9 ± 3.4%, 30.0 ± 1.8%, and 40.9 ± 2.4% for methods based on QSM, qBOLD, and QSM+qBOLD, respectively. QSM+qBOLD provided the highest CMRO2 contrast between gray and white matter, more uniform OEF than QSM, and less noisy OEF than qBOLD. Quantitative CMRO2 mapping that fits the entire complex GRE data is feasible by combining QSM analysis of phase and qBOLD analysis of magnitude.