Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging

Validation of a new 3D quantitative BOLD based cerebral oxygen extraction mapping

Quantitative BOLD (qBOLD) MRI allows evaluation of oxidative metabolism of the brain based purely on an endogenous contrast mechanism. The method quantifies deoxygenated blood volume (DBV) and hemoglobin oxygen saturation level of venous blood (Yv), yielding oxygen extraction fraction (OEF), and along with a separate measurement of cerebral blood flow, cerebral metabolic rate of oxygen (CMRO2) maps. Here, we evaluated our recently reported 3D qBOLD method that rectifies a number of deficiencies in prior qBOLD approaches in terms of repeat reproducibility and sensitivity to hypercapnia on the metabolic parameters, and in comparison to dual-gas calibrated BOLD (cBOLD) MRI for determining resting-state oxygen metabolism. Results suggested no significant difference between test-retest qBOLD scans in either DBV and OEF. Exposure to hypercapnia yielded group averages of 38 and 28% for OEF and 151 and 146 µmol/min/100 g for CMRO2 in gray matter at baseline and hypercapnia, respectively. The decrease of OEF during hypercapnia was significant (p ≪ 0.01), whereas CMRO2 did not change significantly (p = 0.25). Finally, baseline OEF (37 vs. 39%) and CMRO2 (153 vs. 145 µmol/min/100 g) in gray matter using qBOLD and dual-gas cBOLD were found to be in good agreement with literature values, and were not significantly different from each other (p > 0.1).

Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a simulated-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms. The PnP OA-LFE module's versatility was further demonstrated by its successful application to xQSM, a distinct DL-QSM network for dipole inversion. In conclusion, this work introduces a new DL paradigm, allowing researchers to develop innovative QSM methods without requiring a complete overhaul of their existing architectures.

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32 % accuracy improvement than supervised deep learning methods. It is also 33 % more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 min.

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.

Cerebral oxygen metabolism from MRI susceptibility

This article provides an overview of MRI methods exploiting magnetic susceptibility properties of blood to assess cerebral oxygen metabolism, including the tissue oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2). The first section is devoted to describing blood magnetic susceptibility and its effect on the MRI signal. Blood circulating in the vasculature can have diamagnetic (oxyhemoglobin) or paramagnetic properties (deoxyhemoglobin). The overall balance between oxygenated and deoxygenated hemoglobin determines the induced magnetic field which, in turn, modulates the transverse relaxation decay of the MRI signal via additional phase accumulation. The following sections of this review then illustrate the principles underpinning susceptibility-based techniques for quantifying OEF and CMRO2. Here, it is detailed whether these techniques provide global (OxFlow) or local (Quantitative Susceptibility Mapping - QSM, calibrated BOLD - cBOLD, quantitative BOLD - qBOLD, QSM+qBOLD) measurements of OEF or CMRO2, and what signal components (magnitude or phase) and tissue pools they consider (intravascular or extravascular). Validations studies and potential limitations of each method are also described. The latter include (but are not limited to) challenges in the experimental setup, the accuracy of signal modeling, and assumptions on the measured signal. The last section outlines the clinical uses of these techniques in healthy aging and neurodegenerative diseases and contextualizes these reports relative to results from gold-standard PET.

Quantitative Evaluation of Oxygen Extraction Fraction Changes in the Monkey Brain during Acute Stroke by Using Quantitative Susceptibility Mapping

BACKGROUND

The oxygen extraction fraction (OEF) indicates the brain's oxygen consumption and can be estimated by using the quantitative susceptibility mapping (QSM) MRI technique. Recent studies have suggested that OEF alteration following stroke is associated with the viability of at-risk tissue. In the present study, the temporal evolution of OEF in the monkey brain during acute stroke was investigated using QSM.

METHODS

Ischemic stroke was induced in adult rhesus monkeys (n = 8) with permanent middle cerebral artery occlusion (pMCAO) by using an interventional approach. Diffusion-, T2-, and T2*-weighted images were conducted on day 0, day 2, and day 4 post-stroke using a clinical 3T scanner. Progressive changes in magnetic susceptibility and OEF, along with their correlations with the transverse relaxation rates and diffusion indices, were examined.

RESULTS

The magnetic susceptibility and OEF in injured gray matter of the brain significantly increased during the hyperacute phase, and then decreased significantly on day 2 and day 4. Moreover, the temporal changes of OEF in gray matter were moderately correlated with mean diffusivity (MD) (r = 0.52; p = 0.046) from day 0 to day 4. Magnetic susceptibility in white matter progressively increased (from negative values to near zero) during acute stroke, and significant increases were seen on day 2 (p = 0.08) and day 4 (p = 0.003) when white matter was significantly degenerated. However, significant reduction of OEF in white matter was not seen until day 4 post-stroke.

CONCLUSION

The preliminary results demonstrate that QSM-derived OEF is a robust approach to examine the progressive changes of gray matter in the ischemic brain from the hyperacute phase to the subacute phase of stroke. The changes of OEF in gray matter were more prominent than those in white matter following stroke insult. The findings suggest that QSM-derived OEF may provide complementary information for understanding the neuropathology of the brain tissue following stroke and predicting stroke outcomes.

Cerebral oxygen extraction fraction MRI: Techniques and applications

The human brain constitutes 2% of the body's total mass but uses 20% of the oxygen. The rate of the brain's oxygen utilization can be derived from a knowledge of cerebral blood flow and the oxygen extraction fraction (OEF). Therefore, OEF is a key physiological parameter of the brain's function and metabolism. OEF has been suggested to be a useful biomarker in a number of brain diseases. With recent advances in MRI techniques, several MRI-based methods have been developed to measure OEF in the human brain. These MRI OEF techniques are based on the T₂ of blood, the blood signal phase, the magnetic susceptibility of blood-containing voxels, the effect of deoxyhemoglobin on signal behavior in extravascular tissue, and the calibration of the BOLD signal using gas inhalation. Compared to ¹⁵ O PET, which is considered the "gold standard" for OEF measurement, MRI-based techniques are non-invasive, radiation-free, and are more widely available. This article provides a review of these emerging MRI-based OEF techniques. We first briefly introduce the role of OEF in brain oxygen homeostasis. We then review the methodological aspects of different categories of MRI OEF techniques, including their signal mechanisms, acquisition methods, and data analyses. The strengths and limitations of the techniques are discussed. Finally, we review key applications of these techniques in physiological and pathological conditions.

Reduced magnetic resonance angiography signal intensity in the middle cerebral artery ipsilateral to severe carotid stenosis may be a practical index of high oxygen extraction fraction

Objectives

Angiographic “slow flow” in the middle cerebral artery (MCA), caused by carotid stenosis, may be associated with high oxygen extraction fraction (OEF). If the MCA slow flow is associated with a reduced relative signal intensity (rSI) of the MCA on MR angiography, the reduced rSI may be associated with a high OEF. We investigated whether the MCA slow flow ipsilateral to carotid stenosis was associated with a high OEF and aimed to create a practical index to estimate the high OEF.

Methods

We included patients who underwent digital subtraction angiography (DSA) and MRA between 2015 and 2019 to evaluate carotid stenosis. MCA slow flow by image count using DSA, MCA rSI, minimal luminal diameter (MLD) of the carotid artery, carotid artery stenosis rate (CASr), and whole-brain OEF (wb-OEF) was evaluated. When MCA slow flow was associated with a high wb-OEF, the determinants of MCA slow flow were identified, and their association with high wb-OEF was evaluated.

Results

One hundred and twenty-seven patients met our inclusion criteria. Angiographic MCA slow flow was associated with high wb-OEF. We identified MCA rSI and MLD as determinants of angiographic MCA slow flow. The upper limits of MCA rSI and MLD for angiographic MCA slow flow were 0.89 and 1.06 mm, respectively. The wb-OEF was higher in patients with an MCA rSI ≤ 0.89 and ipsilateral MLD ≤ 1.06 mm than patients without this combination.

Conclusions

The combination of reduced MCA rSI and ipsilateral narrow MLD is a straightforward index of high wb-OEF.

Key Points

• The whole-brain OEF in patients with angiographic slow flow in the MCA ipsilateral to high-grade carotid stenosis was higher than in patients without it.

• Independent determinants of MCA slow flow were MCA relative signal intensity (rSI) on MRA or minimal luminal diameter (MLD) of the carotid stenosis.

• The wb-OEF was higher in patients with an MCA rSI ≤ 0.89 and ipsilateral MLD ≤ 1.06 mm than patients without this combination.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08272-3.

The Spatiotemporal Evolution of MRI-Derived Oxygen Extraction Fraction and Perfusion in Ischemic Stroke

Purpose

This study aimed to assess the spatiotemporal evolution of oxygen extraction fraction (OEF) in ischemic stroke with a newly developed cluster analysis of time evolution (CAT) for a combined quantitative susceptibility mapping and quantitative blood oxygen level-dependent model (QSM + qBOLD, QQ).

Method

One hundred and fifteen patients in different ischemic stroke phases were retrospectively collected for measurement of OEF of the infarcted area defined on diffusion-weighted imaging (DWI). Clinical severity was assessed using the National Institutes of Health Stroke Scale (NIHSS). Of the 115 patients, 11 underwent two longitudinal MRI scans, namely, three-dimensional (3D) multi-echo gradient recalled echo (mGRE) and 3D pseudo-continuous arterial spin labeling (pCASL), to evaluate the reversal region (RR) of the initial diffusion lesion (IDL) that did not overlap with the final infarct (FI). The temporal evolution of OEF and the cerebral blood flow (CBF) in the IDL, the RR, and the FI were assessed.

Results

Compared to the contralateral mirror area, the OEF of the infarcted region was decreased regardless of stroke phases (p < 0.05) and showed a declining tendency from the acute to the chronic phase (p = 0.022). Five of the 11 patients with longitudinal scans showed reversal of the IDL. Relative oxygen extraction fraction (rOEF, compared to the contralateral mirror area) of the RR increased from the first to the second MRI (p = 0.044). CBF was about 1.5-fold higher in the IDL than in the contralateral mirror area in the first MRI. Two patients showed penumbra according to the enlarged FI volume. The rOEF of the penumbra fluctuated around 1.0 at earlier scan times and then decreased, while the CBF decreased continuously.

Conclusion

The spatiotemporal evolution of OEF and perfusion in ischemic lesions is heterogeneous, and the CAT-based QQ method is feasible to capture cerebral oxygen metabolic information.

Brain metabolism in tau and amyloid mouse models of Alzheimer's disease: An MRI study

Alzheimer's disease (AD) is the leading cause of cognitive impairment and dementia in elderly individuals. According to the current biomarker framework for "unbiased descriptive classification", biomarkers of neurodegeneration, "N", constitute a critical component in the tri-category "A/T/N" system. Current biomarkers of neurodegeneration suffer from potential drawbacks such as requiring invasive lumbar puncture, involving ionizing radiation, or representing a late, irreversible marker. Recent human studies have suggested that reduced brain oxygen metabolism may be a new functional marker of neurodegeneration in AD, but the heterogeneity and the presence of mixed pathology in human patients did not allow a full understanding of the role of oxygen extraction and metabolism in AD. In this report, global brain oxygen metabolism and related physiological parameters were studied in two AD mouse models with relatively pure pathology, using advanced MRI techniques including T2 -relaxation-under-spin-tagging (TRUST) and phase contrast (PC) MRI. Additionally, regional cerebral blood flow (CBF) was determined with pseudocontinuous arterial spin labeling. Reduced global oxygen extraction fraction (by -18.7%, p = 0.008), unit-mass cerebral metabolic rate of oxygen (CMRO2 ) (by -17.4%, p = 0.04) and total CMRO2 (by -30.8%, p < 0.001) were observed in Tau4RΔK mice-referred to as the tau AD model-which manifested pronounced neurodegeneration, as measured by diminished brain volume (by -15.2%, p < 0.001). Global and regional CBF in these mice were not different from those of wild-type mice (p > 0.05), suggesting normal vascular function. By contrast, in B6;SJL-Tg [APPSWE]2576Kha (APP) mice-referred to as the amyloid AD model-no brain volume reduction, as well as relatively intact brain oxygen extraction and metabolism, were found (p > 0.05). Consistent with the imaging data, behavioral measures of walking distance were impaired in Tau4RΔK mice (p = 0.004), but not in APP mice (p = 0.88). Collectively, these findings support the hypothesis that noninvasive MRI measurement of brain oxygen metabolism may be a promising biomarker of neurodegeneration in AD.

Cerebral oxygen extraction fraction (OEF): Comparison of challenge-free gradient echo QSM+qBOLD (QQ) with 15O PET in healthy adults

We aimed to validate oxygen extraction fraction (OEF) estimations by quantitative susceptibility mapping plus quantitative blood oxygen-level dependence (QSM+qBOLD, or QQ) using ¹⁵O-PET. In ten healthy adult brains, PET and MRI were acquired simultaneously on a PET/MR scanner. PET was acquired using C[¹⁵O], O[¹⁵O], and H₂[¹⁵O]. Image-derived arterial input functions and standard models of oxygen metabolism provided quantification of PET. MRI included T1-weighted imaging, time-of-flight angiography, and multi-echo gradient-echo imaging that was processed for QQ. Region of interest (ROI) analyses compared PET OEF and QQ OEF. In ROI analyses, the averaged OEF differences between PET and QQ were generally small and statistically insignificant. For whole brains, the average and standard deviation of OEF was 32.8 ± 6.7% for PET; OEF was 34.2 ± 2.6% for QQ. Bland-Altman plots quantified agreement between PET OEF and QQ OEF. The interval between the 95% limits of agreement was 16.9 ± 4.0% for whole brains. Our validation study suggests that respiratory challenge-free QQ-OEF mapping may be useful for non-invasive clinical assessment of regional OEF impairment.

Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD

PURPOSE

To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO₂ ) using temporal clustering, tissue composition, and total variation (CCTV).

METHODS

Three-dimensional multi-echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion-weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue-type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ-based OEF and CMRO₂ were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region-of-interest analysis, Kruskal-Wallis test, and post hoc Wilcoxson rank sum test.

RESULTS

In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ-based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast-to-noise ratio between DWI-defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 (P = .01) versus 0.7 ± 0.5 (P < .001).

CONCLUSION

The CCTV mapping significantly improves the robustness of QQ-based OEF against noise.

Susceptibility-diffusion mismatch correlated with leptomeningeal collateralization in large vessel occlusion stroke

Objective

To investigate the relationship between asymmetric prominent hypointense vessels (prominent vessel sign, PVS) on susceptibility-weighted imaging (SWI) and leptomeningeal collateralization in patients with acute ischemic stroke due to large vessel occlusion.

Methods

We retrospectively enrolled patients with M1 segment occlusion of the middle cerebral artery who underwent emergency magnetic resonance imaging and digital subtraction angiography within 24 hours from stroke onset. The extent of PVS on SWI was assessed using the Alberta Stroke Program Early CT Score (ASPECTS). Leptomeningeal collateralization on digital subtraction angiography images was assessed using the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) scale. Spearman’s rank correlation test was performed to explore the correlation of ASITN/SIR scores with SWI-ASPECTS and SWI-diffusion-weighted imaging (DWI) mismatch scores.

Results

Thirty-five patients were enrolled. There was no significant correlation between SWI-ASPECTS and ASITN/SIR scores. However, SWI-DWI mismatch scores were positively correlated with ASITN/SIR scores.

Conclusion

The range of PVS on SWI did not closely reflect the collateral status, while the range of SWI-DWI mismatch was significantly correlated with the leptomeningeal collateralization. In patients with acute anterior circulation stroke due to large vessel occlusion, larger SWI-DWI mismatch was associated with better leptomeningeal collaterals.

Factors associated with prominent vessel sign on susceptibility-weighted imaging in acute ischemic stroke

The prominent vessel sign (PVS) on susceptibility-weighted imaging (SWI) is not displayed in all cases of acute ischemia. We aimed to investigate the factors associated with the presence of PVS in stroke patients. Consecutive ischemic stroke patients admitted within 24 h from symptom onset underwent emergency multimodal MRI at admission. Associated factors for the presence of PVS were analyzed using univariate analyses and multivariable logistic regression analyses. A total of 218 patients were enrolled. The occurrence rate of PVS was 55.5%. Univariate analyses showed significant differences between PVS-positive group and PVS-negative group in age, history of coronary heart disease, baseline NIHSS scores, total cholesterol, hemoglobin, anterior circulation infarct, large vessel occlusion, and cardioembolism. Multivariable logistic regression analyses revealed that the independent factors associated with PVS were anterior circulation infarct (odds ratio [OR] 13.7; 95% confidence interval [CI] 3.5–53.3), large vessel occlusion (OR 123.3; 95% CI 33.7–451.5), and cardioembolism (OR 5.6; 95% CI 2.1–15.3). Anterior circulation infarct, large vessel occlusion, and cardioembolism are independently associated with the presence of PVS on SWI.

Cerebral oxygen extraction fraction: Comparison of dual-gas challenge calibrated BOLD with CBF and challenge-free gradient echo QSM+qBOLD

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.