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Alzaidi AA, Panek R, Blockley NP. Quantitative BOLD (qBOLD) imaging of oxygen metabolism and blood oxygenation in the human body: A scoping review. Magn Reson Med 2024; 92:1822-1837. [PMID: 39072791 DOI: 10.1002/mrm.30165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 05/06/2024] [Accepted: 05/08/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE There are many approaches to the quantitative BOLD (qBOLD) technique described in the literature, differing in pulse sequences, MRI parameters and data processing. Thus, in this review, we summarized the acquisition methods, approaches used for oxygenation quantification and clinical populations investigated. METHODS Three databases were systematically searched (Medline, Embase, and Web of Science) for published research that used qBOLD methods for quantification of oxygen metabolism. Data extraction and synthesis were performed by one author and reviewed by a second author. RESULTS A total of 93 relevant papers were identified. Acquisition strategies were summarized, and oxygenation parameters were found to have been investigated in many pathologies such as steno-occlusive diseases, stroke, glioma, and multiple sclerosis disease. CONCLUSION A summary of qBOLD approaches for oxygenation measurements and applications could help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.
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Affiliation(s)
- Ahlam A Alzaidi
- David Greenfield Human Physiology Unit, School of Life Sciences, University of Nottingham, Nottingham, UK
- Radiology Department, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Rafal Panek
- Medical Physics and Clinical Engineering, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Nicholas P Blockley
- David Greenfield Human Physiology Unit, School of Life Sciences, University of Nottingham, Nottingham, UK
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Lin Z, Jiang D, Hong Y, Zhang Y, Hsu YC, Lu H, Wu D. Vessel-specific quantification of cerebral venous oxygenation with velocity-encoding preparation and rapid acquisition. Magn Reson Med 2024; 92:782-791. [PMID: 38523598 DOI: 10.1002/mrm.30092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/03/2024] [Accepted: 03/07/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE Non-invasive measurement of cerebral venous oxygenation (Yv) is of critical importance in brain diseases. The present work proposed a fast method to quantify regional Yv map for both large and small veins. METHODS A new sequence was developed, referred to as TRU-VERA (T2 relaxation under velocity encoding and rapid acquisition, which isolates blood spins from static tissue with velocity-encoding preparation, modulates the T2 weighting of venous signal with T2-preparation and utilizes a bSSFP readout to achieve fast acquisition with high resolution. The sequence was first optimized to achieve best sensitivity for both large and small veins, and then validated with TRUST (T2 relaxation under spin tagging), TRUPC (T2 relaxation under phase contrast), and accelerated TRUPC MRI. Regional difference of Yv was evaluated, and test-retest reproducibility was examined. RESULTS Optimal Venc was determined to be 3 cm/s, while recovery time and balanced SSFP flip angle within reasonable range had minimal effect on SNR efficiency. Venous T2 measured with TRU-VERA was highly correlated with T2 from TRUST (R2 = 0.90), and a conversion equation was established for further calibration to Yv. TRU-VERA sequences showed consistent Yv estimation with TRUPC (R2 = 0.64) and accelerated TRUPC (R2 = 0.79). Coefficient of variation was 0.84% for large veins and 2.49% for small veins, suggesting an excellent test-retest reproducibility. CONCLUSION The proposed TRU-VERA sequence is a promising method for vessel-specific oxygenation assessment.
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Affiliation(s)
- Zixuan Lin
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yiwen Hong
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthineers Ltd, Shanghai, China
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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Wehrli FW. Recent Advances in MR Imaging-based Quantification of Brain Oxygen Metabolism. Magn Reson Med Sci 2024; 23:377-403. [PMID: 38866481 PMCID: PMC11234951 DOI: 10.2463/mrms.rev.2024-0028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
The metabolic rate of oxygen (MRO2) is fundamental to tissue metabolism. Determination of MRO2 demands knowledge of the arterio-venous difference in hemoglobin-bound oxygen concentration, typically expressed as oxygen extraction fraction (OEF), and blood flow rate (BFR). MRI is uniquely suited for measurement of both these quantities, yielding MRO2 in absolute physiologic units of µmol O2 min-1/100 g tissue. Two approaches are discussed, both relying on hemoglobin magnetism. Emphasis will be on cerebral oxygen metabolism expressed in terms of the cerebral MRO2 (CMRO2), but translation of the relevant technologies to other organs, including kidney and placenta will be touched upon as well. The first class of methods exploits the blood's bulk magnetic susceptibility, which can be derived from field maps. The second is based on measurement of blood water T2, which is modulated by diffusion and exchange in the local-induced fields within and surrounding erythrocytes. Some whole-organ methods achieve temporal resolution adequate to permit time-series studies of brain energetics, for instance, during sleep in the scanner with concurrent electroencephalogram (EEG) sleep stage monitoring. Conversely, trading temporal for spatial resolution has led to techniques for spatially resolved approaches based on quantitative blood oxygen level dependent (BOLD) or calibrated BOLD models, allowing regional assessment of vascular-metabolic parameters, both also exploiting deoxyhemoglobin paramagnetism like their whole-organ counterparts.
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Affiliation(s)
- Felix W Wehrli
- Laboratory for Structural, Physiologic and Functional Imaging (LSPFI), Department of Radiology, Perelman School of Medicine, University Pennsylvania, Philadelphia, Pennsylvania, USA
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Lee H, Xu J, Fernandez-Seara MA, Wehrli FW. Validation of a new 3D quantitative BOLD based cerebral oxygen extraction mapping. J Cereb Blood Flow Metab 2024; 44:1184-1198. [PMID: 38289876 PMCID: PMC11179617 DOI: 10.1177/0271678x231220332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 02/01/2024]
Abstract
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).
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Affiliation(s)
- Hyunyeol Lee
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jing Xu
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maria A Fernandez-Seara
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Clinica Universidad de Navarra, Pamplona, Spain
| | - Felix W Wehrli
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Xu J, Wiemken A, Langham MC, Rao H, Nabbout M, Caporale AS, Schwab RJ, Detre JA, Wehrli FW. Sleep-stage-dependent alterations in cerebral oxygen metabolism quantified by magnetic resonance. J Neurosci Res 2024; 102:e25313. [PMID: 38415989 DOI: 10.1002/jnr.25313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 01/25/2024] [Accepted: 02/09/2024] [Indexed: 02/29/2024]
Abstract
A key function of sleep is to provide a regular period of reduced brain metabolism, which is critical for maintenance of healthy brain function. The purpose of this work was to quantify the sleep-stage-dependent changes in brain energetics in terms of cerebral metabolic rate of oxygen (CMRO2 ) as a function of sleep stage using quantitative magnetic resonance imaging (MRI) with concurrent electroencephalography (EEG) during sleep in the scanner. Twenty-two young and older subjects with regular sleep hygiene and Pittsburgh Sleep Quality Index (PSQI) in the normal range were recruited for the study. Cerebral blood flow (CBF) and venous oxygen saturation (SvO2 ) were obtained simultaneously at 3 Tesla field strength and 2.7-s temporal resolution during an 80-min time series using OxFlow, an in-house developed imaging sequence. The method yields whole-brain CMRO2 in absolute physiologic units via Fick's Principle. Nineteen subjects yielded evaluable data free of subject motion artifacts. Among these subjects, 10 achieved slow-wave (N3) sleep, 16 achieved N2 sleep, and 19 achieved N1 sleep while undergoing the MRI protocol during scanning. Mean CMRO2 was 98 ± 7(μmol min-1 )/100 g awake, declining progressively toward deepest sleep stage: 94 ± 10.8 (N1), 91 ± 11.4 (N2), and 76 ± 9.0 μmol min-1 /100 g (N3), with each level differing significantly from the wake state. The technology described is able to quantify cerebral oxygen metabolism in absolute physiologic units along with non-REM sleep stage, indicating brain oxygen consumption to be closely associated with depth of sleep, with deeper sleep stages exhibiting progressively lower CMRO2 levels.
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Affiliation(s)
- Jing Xu
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew Wiemken
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael C Langham
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hengyi Rao
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marianne Nabbout
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alessandra S Caporale
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurosciences, Imaging and Clinical Sciences, 'G. d'Annunzio University' of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), 'G. d'Annunzio University' of Chieti-Pescara, Chieti, Italy
| | - Richard J Schwab
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John A Detre
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Felix W Wehrli
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Caporale AS, Barclay AM, Xu J, Rao H, Lee H, Langham MC, Detre JA, Wehrli FW. Superior sagittal sinus flow as a proxy for tracking global cerebral blood flow dynamics during wakefulness and sleep. J Cereb Blood Flow Metab 2023; 43:1340-1350. [PMID: 36927172 PMCID: PMC10369151 DOI: 10.1177/0271678x231164423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 01/18/2023] [Accepted: 02/10/2023] [Indexed: 03/18/2023]
Abstract
Sleep, a state of reduced consciousness, affects brain oxygen metabolism and lowers cerebral metabolic rate of oxygen (CMRO2). Previously, we quantified CMRO2 during sleep via Fick's Principle, with a single-band MRI sequence measuring both hemoglobin O2 saturation (SvO2) and superior sagittal sinus (SSS) blood flow, which was upscaled to obtain total cerebral blood flow (tCBF). The procedure involves a brief initial calibration scan to determine the upscaling factor (fc), assumed state-invariant. Here, we used a dual-band sequence to simultaneously provide SvO2 in SSS and tCBF in the neck every 16 seconds, allowing quantification of fc dynamically. Ten healthy subjects were scanned by MRI with simultaneous EEG for 80 minutes, yielding 300 temporal image frames per subject. Four volunteers achieved slow-wave sleep (SWS), as evidenced by increased δ-wave activity (per American Academy of Sleep Medicine criteria). SWS was maintained for 13.5 ± 7.0 minutes, with CMRO2 28.6 ± 5.5% lower than pre-sleep wakefulness. Importantly, there was negligible bias between tCBF obtained by upscaling SSS-blood flow, and tCBF measured directly in the inflowing arteries of the neck (intra-class correlation 0.95 ± 0.04, averaged across all subjects), showing that the single-band approach is a valid substitute for quantifying tCBF, simplifying image data collection and analysis without sacrificing accuracy.
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Affiliation(s)
- Alessandra S Caporale
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neuroscience, Imaging and Clinical Sciences, ‘G. d’Annunzio University’ of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), ‘G. d’Annunzio University’ of Chieti-Pescara, Chieti, Italy
| | - Alexander M Barclay
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jing Xu
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hengyi Rao
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Division of Sleep and Chronobiology, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hyunyeol Lee
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- School of Electronics Engineering, Kyungpook National University, Daegu, South Korea
| | - Michael C Langham
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John A Detre
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Felix W Wehrli
- Laboratory for Structural, Physiological, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Biondetti E, Cho J, Lee H. Cerebral oxygen metabolism from MRI susceptibility. Neuroimage 2023; 276:120189. [PMID: 37230206 PMCID: PMC10335841 DOI: 10.1016/j.neuroimage.2023.120189] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 05/27/2023] Open
Abstract
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.
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Affiliation(s)
- Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy
| | - Junghun Cho
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, New York, USA
| | - Hyunyeol Lee
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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Li H, Wang C, Yu X, Luo Y, Wang H. Measurement of Cerebral Oxygen Extraction Fraction Using Quantitative BOLD Approach: A Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:101-118. [PMID: 36939794 PMCID: PMC9883382 DOI: 10.1007/s43657-022-00081-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/29/2022] [Accepted: 10/11/2022] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
- Hongwei Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433 China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
| | - Xuchen Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433 China
| | - Yu Luo
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200434 China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, 200433 China
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Deshpande RS, Langham MC, Cheng CC, Wehrli FW. Metabolism of oxygen via T 2 and interleaved velocity encoding: A rapid method to quantify whole-brain cerebral metabolic rate of oxygen. Magn Reson Med 2022; 88:1229-1243. [PMID: 35699155 PMCID: PMC9247043 DOI: 10.1002/mrm.29299] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/31/2022] [Accepted: 04/20/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE Cerebral metabolic rate of oxygen (CMRO2 ) is an important biomarker of brain function. Key physiological parameters required to quantify CMRO2 include blood flow rate in the feeding arteries and venous oxygen saturation (SvO2 ) in the draining vein. Here, a pulse sequence, metabolism of oxygen via T2 and interleaved velocity encoding (MOTIVE), was developed to measure both parameters simultaneously and enable CMRO2 quantification in a single pass. METHODS The MOTIVE sequence interleaves a phase-contrast module between a nonselective saturation and a background-suppressed T2 -prepared EPI readout (BGS-EPI) to measure T2 of blood water protons and cerebral blood flow in 20 s or less. The MOTIVE and standalone BGS-EPI sequences were compared against TRUST ("T2 relaxation under spin tagging") in the brain in healthy subjects (N = 24). Variants of MOTIVE to enhance resolution or shorten scan time were explored. Intrasession and intersession reproducibility studies were performed. RESULTS MOTIVE experiments yielded an average SvO2 of 61 ± 6% in the superior sagittal sinus of the brain and an average cerebral blood flow of 56 ± 10 ml/min/100 g. The bias in SvO2 of MOTIVE and BGS-EPI to TRUST was +2 ± 4% and +1 ± 3%, respectively. The bias in cerebral blood flow of MOTIVE to Cartesian phase-contrast reference was +1 ± 6 ml/min/100 g. CONCLUSIONS The MOTIVE sequence is an advance over existing T2 -based oximetric methods. It does not require a control image and simultaneously measures SvO2 and flow velocity. The measurements agree well with TRUST and reference phase-contrast sequences. This noninvasive technique enables CMRO2 quantification in under 20 s and is reproducible for in vivo applications.
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Affiliation(s)
- Rajiv S. Deshpande
- Laboratory for Structural Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael C. Langham
- Laboratory for Structural Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng-Chieh Cheng
- Laboratory for Structural Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Present affiliation: Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Felix W. Wehrli
- Laboratory for Structural Physiologic and Functional Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Jiang D, Lu H. Cerebral oxygen extraction fraction MRI: Techniques and applications. Magn Reson Med 2022; 88:575-600. [PMID: 35510696 PMCID: PMC9233013 DOI: 10.1002/mrm.29272] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/20/2022] [Accepted: 03/29/2022] [Indexed: 12/20/2022]
Abstract
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.
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Affiliation(s)
- Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
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