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

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).

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

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

CLINICAL RELEVANCE STATEMENT

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.

KEY POINTS

• 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.

The changes of oxygen extraction fraction in different types of lesions in relapsing-remitting multiple sclerosis: A cross-sectional and longitudinal study

OBJECTIVES

To explore the oxygen metabolism level of different types of lesions in relapsing-remitting multiple sclerosis (RRMS) patients by oxygen extraction fraction (OEF) both cross-sectionally and longitudinally.

METHODS

Forty-six RRMS patients and forty-one healthy controls (HC) went MRI examination. The quantitative susceptibility mapping (QSM) and OEF map were reconstructed from a 3D multi-echo gradient echo sequence. MS lesions in white matter were classified as contrast-enhancing lesions (CELs) on post-gadolinium T1-weighted sequence, paramagnetic rim lesions (PRLs), hyperintense lesions and non-hyperintense lesions on QSM, respectively. The susceptibility and OEF of different types of lesions were compared. The susceptibility and OEF values were measured and compared among different types of lesions. Among these RRMS patients, seventeen had follow-up MRI and 232 lesions, and baseline to follow-up longitudinal changes in susceptibility and OEF were measured.

RESULTS

PRLs had higher susceptibility and lower OEF than CELs, hyperintense lesions, and non-hyperintense lesions. The hyperintense lesions had higher susceptibility and lower OEF than non-hyperintense lesions. In longitudinal changes, PRLs had susceptibility increased (P < 0.001) and OEF decreased (P < 0.001). The hyperintense lesions showed significant decreases in susceptibility (P = 0.020), and non-hyperintense lesions showed significant increases in OEF during follow-up (P = 0.005). Notably, hyperintense lesions may convert to PRLs or non-hyperintense lesions as time progresses, accompanied by changes of OEF and susceptibility in the lesions.

CONCLUSION

This study revealed tissue damage and oxygen metabolism level in different types of MS lesions. The OEF may contribute to further understanding the evolution of MS lesions.

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.

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD

OBJECTIVE

The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition.

METHODS

Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed.

RESULTS

Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score.

CONCLUSION

CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.

Microstructure-Informed Myelin Mapping (MIMM) from Gradient Echo MRI using Stochastic Matching Pursuit

Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed.

Highlights

novel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling.utilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron.matter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts.acquired myelin and iron maps may be utilized to monitor longitudinal disease progress.

Evaluating Physiological MRI Parameters in Patients with Brain Metastases Undergoing Stereotactic Radiosurgery-A Preliminary Analysis and Case Report

Brain metastases occur in ten to thirty percent of the adult cancer population. Treatment consists of different (palliative) options, including stereotactic radiosurgery (SRS). Sensitive MRI biomarkers are needed to better understand radiotherapy-related effects on cerebral physiology and the subsequent effects on neurocognitive functioning. In the current study, we used physiological imaging techniques to assess cerebral blood flow (CBF), oxygen extraction fraction (OEF), cerebral metabolic rate of oxygen (CMRO2) and cerebrovascular reactivity (CVR) before and three months after SRS in nine patients with brain metastases. The results showed improvement in OEF, CBF and CMRO2 within brain tissue that recovered from edema (all p ≤ 0.04), while CVR remained impacted. We observed a global post-radiotherapy increase in CBF in healthy-appearing brain tissue (p = 0.02). A repeated measures correlation analysis showed larger reductions within regions exposed to higher radiotherapy doses in CBF (rrm = -0.286, p < 0.001), CMRO2 (rrm = -0.254, p < 0.001), and CVR (rrm = -0.346, p < 0.001), but not in OEF (rrm = -0.004, p = 0.954). Case analyses illustrated the impact of brain metastases progression on the post-radiotherapy changes in both physiological MRI measures and cognitive performance. Our preliminary findings suggest no radiotherapy effects on physiological parameters occurred in healthy-appearing brain tissue within 3-months post-radiotherapy. Nevertheless, as radiotherapy can have late side effects, larger patient samples allowing meaningful grouping of patients and longer follow-ups are needed.

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.

Cerebral oxygen extraction fraction declines with ventricular enlargement in patients with normal pressure hydrocephalus

OBJECTIVE

Normal pressure hydrocephalus (NPH) is a neurodegenerative disease that is potentially reversible by shunt surgery in approximately 60% of patients. Imaging may provide a means to investigate brain tissue viability and oxygen metabolism in NPH patients.

METHODS

Oxygen extraction fraction (OEF) mapping was generated from 3D multi-echo gradient echo MRI (mGRE) data using QQ-CCTV algorithm and cerebral blood flow (CBF) using 3D arterial spin labeling (ASL) MRI data, thereby calculating the cerebral metabolic rate of oxygen (CMRO2 = CBF × OEF × [H]a) in 16 NPH patients. Regression analyses using cortical gray matter and deep gray matter regions were conducted with age, gender, CSF stroke volume and normalized ventricular volume as independent variables.

RESULTS

OEF showed significant negative correlations with normalized brain ventricular volumes in the whole brain (p = 0.004, q = 0.01), cortical gray matter (p = 0.004, q = 0.01), caudate (p = 0.02, q = 0.04), and pallidum (p = 0.03, q = 0.04), but no significant correlation with CSF stroke volume (q > 0.05). There was no significant finding with CBF or CMRO2.

CONCLUSION

In NPH patients, low OEF in several regions was significantly correlated with large ventricular volumes, indicating decreased tissue oxygen metabolism with increased NPH severity. OEF mapping may provide a functional understanding of neurodegeneration in NPH and may improve monitoring of disease course and treatment outcomes.

Measurement of Cerebral Oxygen Extraction Fraction Using Quantitative BOLD Approach: A Review

Quantification of brain oxygenation and metabolism, both of which are indicators of the level of brain activity, plays a vital role in understanding the cerebral perfusion and the pathophysiology of brain disorders. Magnetic resonance imaging (MRI), a widely used clinical imaging technique, which is very sensitive to magnetic susceptibility, has the possibility of substituting positron emission tomography (PET) in measuring oxygen metabolism. This review mainly focuses on the quantitative blood oxygenation level-dependent (qBOLD) method for the evaluation of oxygen extraction fraction (OEF) in the brain. Here, we review the theoretic basis of qBOLD, as well as existing acquisition and quantification methods. Some published clinical studies are also presented, and the pros and cons of qBOLD method are discussed as well.

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.

Brain oxygen extraction and neural tissue susceptibility are associated with cognitive impairment in older individuals

BACKGROUND AND PURPOSE

We investigated the effects of aging, white matter hyperintensities (WMH), and cognitive impairment on brain iron levels and cerebral oxygen metabolism, known to be altered in Alzheimer's disease (AD), using quantitative susceptibility mapping and MR-based cerebral oxygen extraction fraction (OEF).

METHODS

In 100 individuals over the age of 50 (68/32 cognitively impaired/intact), OEF and neural tissue susceptibility (χ_n ) were computed retrospectively from MRI multi-echo gradient echo data, obtained on a 3 Tesla MRI scanner. The effects of age and WMH on OEF and χ_n were assessed within groups, and OEF and χ_n were assessed between groups, using multivariate regression analyses.

RESULTS

Cognitively impaired subjects were found to have 19% higher OEF and 34% higher χ_n than cognitively intact subjects in the cortical gray matter and several frontal, temporal, and parietal regions (p < .05). Increased WMH burden was significantly associated with decreased OEF in the cognitively impaired, but not in the cognitively intact. Older age had a stronger association with decreased OEF in the cognitively intact group. Both older age and increased WMH burden were significantly associated with increased χ_n in temporoparietal regions in the cognitively impaired.

CONCLUSIONS

Higher brain OEF and χ_n in cognitively impaired older individuals may reflect altered oxygen metabolism and iron in areas with underlying AD pathology. Both age and WMH have associations with OEF and χ_n but are modified by the presence of cognitive impairment.

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 (χn) 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 χn within the rim and core were compared using linear mixed-effect model analysis. The rim showed higher OEF and χn 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 χn contrast = 33.9 ± 20.3 ppb vs. 25.7 ± 20.5 ppb (p = 0.017). Between MS and HC, OEF and χn 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 χn, -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.

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

PURPOSE

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).

METHODS

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.

RESULTS

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.

CONCLUSION

QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.