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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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T AR, K K, Paul JS. Unveiling metabolic patterns in dementia: Insights from high-resolution quantitative blood-oxygenation-level-dependent MRI. Med Phys 2024. [PMID: 38888202 DOI: 10.1002/mp.17173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/12/2024] [Accepted: 05/08/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Oxygen extraction fraction (OEF) and deoxyhemoglobin (DoHb) levels reflect variations in cerebral oxygen metabolism in demented patients. PURPOSE Delineating the metabolic profiles evident throughout different phases of dementia necessitates an integrated analysis of OEF and DoHb levels. This is enabled by leveraging high-resolution quantitative blood oxygenation level dependent (qBOLD) analysis of magnitude images obtained from a multi-echo gradient-echo MRI (mGRE) scan performed on a 3.0 Tesla scanner. METHODS Achieving superior spatial resolution in qBOLD necessitates the utilization of an mGRE scan with only four echoes, which in turn limits the number of measurements compared to the parameters within the qBOLD model. Consequently, it becomes imperative to discard non-essential parameters to facilitate further analysis. This process entails transforming the qBOLD model into a format suitable for fitting the log-magnitude difference (L-MDif) profiles of the four echo magnitudes present in each brain voxel. In order to bolster spatial specificity, the log-difference qBOLD model undergoes refinement into a representative form, termed as r-qBOLD, particularly when applied to class-averaged L-MDif signals derived through k-means clustering of L-MDif signals from all brain voxels into a predetermined number of clusters. The agreement between parameters estimated using r-qBOLD for different cluster sizes is validated using Bland-Altman analysis, and the model's goodness-of-fit is evaluated using aχ 2 ${\chi ^2}$ -test. Retrospective MRI data of Alzheimer's disease (AD), mild cognitive impairment (MCI), and non-demented patients without neuropathological disorders, pacemakers, other implants, or psychiatric disorders, who completed a minimum of three visits prior to MRI enrolment, are utilized for the study. RESULTS Utilizing a cohort comprising 30 demented patients aged 65-83 years in stages 4-6 representing mild, moderate, and severe stages according to the clinical dementia rating (CDR), matched with an age-matched non-demented control group of 18 individuals, we conducted joint observations of OEF and DoHb levels estimated using r-qBOLD. The observations elucidate metabolic signatures in dementia based on OEF and DoHb levels in each voxel. Our principal findings highlight the significance of spatial patterns of metabolic profiles (metabolic patterns) within two distinct regimes: OEF levels exceeding the normal range (S1-regime), and OEF levels below the normal range (S2-regime). The S1-regime, accompanied by low DoHb levels, predominantly manifests in fronto-parietal and perivascular regions with increase in dementia severity. Conversely, the S2-regime, accompanied by low DoHb levels, is observed in medial temporal (MTL) regions. Other regions with abnormal metabolic patterns included the orbitofrontal cortex (OFC), medial-orbital prefrontal cortex (MOPFC), hypothalamus, ventro-medial prefrontal cortex (VMPFC), and retrosplenial cortex (RSP). Dysfunction in the OFC and MOPFC indicated cognitive and emotional impairment, while hypothalamic involvement potentially indicated preclinical dementia. Reduced metabolic activity in the RSP suggested early-stage AD related functional abnormalities. CONCLUSIONS Integrated analysis of OEF and DoHb levels using r-qBOLD reveals distinct metabolic signatures across dementia phases, highlighting regions susceptible to neuronal loss, vascular involvement, and preclinical indicators.
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Affiliation(s)
- Arun Raj T
- Division of Medical Informatics, School of Informatics, Kerala University of Digital Sciences Innovation & Technology (DUK), Trivandrum, Kerala, India
| | - Karthik K
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Joseph Suresh Paul
- Division of Medical Informatics, School of Informatics, Kerala University of Digital Sciences Innovation & Technology (DUK), Trivandrum, Kerala, India
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Le LNN, Wheeler GJ, Holy EN, Donnay CA, Blockley NP, Yee AH, Ng KL, Fan AP. Cortical oxygen extraction fraction using quantitative BOLD MRI and cerebral blood flow during vasodilation. Front Physiol 2023; 14:1231793. [PMID: 37869717 PMCID: PMC10588655 DOI: 10.3389/fphys.2023.1231793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/25/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction: We aimed to demonstrate non-invasive measurements of regional oxygen extraction fraction (OEF) from quantitative BOLD MRI modeling at baseline and after pharmacological vasodilation. We hypothesized that OEF decreases in response to vasodilation with acetazolamide (ACZ) in healthy conditions, reflecting compensation in regions with increased cerebral blood flow (CBF), while cerebral metabolic rate of oxygen (CMRO2) remained unchanged. We also aimed to assess the relationship between OEF and perfusion in the default mode network (DMN) regions that have shown associations with vascular risk factors and cerebrovascular reactivity in different neurological conditions. Material and methods: Eight healthy subjects (47 ± 13 years, 6 female) were scanned on a 3 T scanner with a 32-channel head coil before and after administration of 15 mg/kg ACZ as a pharmacological vasodilator. The MR imaging acquisition protocols included: 1) A Gradient Echo Slice Excitation Profile Imaging Asymmetric Spin Echo scan to quantify OEF, deoxygenated blood volume, and reversible transverse relaxation rate (R2 ') and 2) a multi-post labeling delay arterial spin labeling scan to measure CBF. To assess changes in each parameter due to vasodilation, two-way t-tests were performed for all pairs (baseline versus vasodilation) in the DMN brain regions with Bonferroni correction for multiple comparisons. The relationships between CBF versus OEF and CBF versus R2' were analyzed and compared across DMN regions using linear, mixed-effect models. Results: During vasodilation, CBF significantly increased in the medial frontal cortex (P = 0.004 ), posterior cingulate gyrus (pCG) (P = 0.004 ), precuneus cortex (PCun) (P = 0.004 ), and occipital pole (P = 0.001 ). Concurrently, a significant decrease in OEF was observed only in the pCG (8.8%, P = 0.003 ) and PCun (8.7 % , P = 0.001 ). CMRO2 showed a trend of increased values after vasodilation, but these differences were not significant after correction for multiple comparisons. Although R2' showed a slightly decreasing trend, no statistically significant changes were found in any regions in response to ACZ. The CBF response to ACZ exhibited a stronger negative correlation with OEF (β = - 0.104 ± 0.027 ; t = - 3.852 , P < 0.001 ), than with R2' (β = - 0.016 ± 0.006 ; t = - 2.692 , P = 0.008 ). Conclusion: Quantitative BOLD modeling can reliably measure OEF across multiple physiological conditions and captures vascular changes with higher sensitivity than R2' values. The inverse correlation between OEF and CBF across regions in DMN, suggests that these two measurements, in response to ACZ vasodilation, are reliable indicators of tissue health in this healthy cohort.
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Affiliation(s)
- Linh N. N. Le
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States
| | - Gregory J. Wheeler
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States
| | - Emily N. Holy
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Corinne A. Donnay
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Nicholas P. Blockley
- School of Medicine and Health Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Alan H. Yee
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Kwan L. Ng
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Audrey P. Fan
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States
- Department of Neurology, University of California, Davis, Davis, CA, United States
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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: 11] [Impact Index Per Article: 11.0] [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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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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Jang J, Kang J, Nam Y. [Brain Iron Imaging in Aging and Cognitive Disorders: MRI Approaches]. TAEHAN YONGSANG UIHAKHOE CHI 2022; 83:527-537. [PMID: 36238502 PMCID: PMC9514519 DOI: 10.3348/jksr.2022.0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022]
Abstract
Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer's disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future.
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Whole-brain 3D mapping of oxygen metabolism using constrained quantitative BOLD. Neuroimage 2022; 250:118952. [PMID: 35093519 PMCID: PMC9007034 DOI: 10.1016/j.neuroimage.2022.118952] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/24/2021] [Accepted: 01/25/2022] [Indexed: 12/02/2022] Open
Abstract
Quantitative BOLD (qBOLD) MRI permits noninvasive evaluation of hemodynamic and metabolic states of the brain by quantifying parametric maps of deoxygenated blood volume (DBV) and hemoglobin oxygen saturation level of venous blood (Yv), and along with a measurement of cerebral blood flow (CBF), the cerebral metabolic rate of oxygen (CMRO2). The method, thus should have potential to provide important information on many neurological disorders as well as normal cerebral physiology. One major challenge in qBOLD is to separate de-oxyhemoglobin’s contribution to R2′ from other sources modulating the voxel signal, for instance, R2, R2′ from non-heme iron (R′2,nh), and macroscopic magnetic field variations. Further, even with successful separation of the several confounders, it is still challenging to extract DBV and Yv from the heme-originated R2′ because of limited sensitivity of the qBOLD model. These issues, which have not been fully addressed in currently practiced qBOLD methods, have so far precluded 3D whole-brain implementation of qBOLD. Thus, the purpose of this work was to develop a new 3D MRI oximetry technique that enables robust qBOLD parameter mapping across the entire brain. To achieve this goal, we employed a rapid, R2′-sensitive, steady-state 3D pulse sequence (termed ‘AUSFIDE’) for data acquisition, and implemented a prior-constrained qBOLD processing pipeline that exploits a plurality of preliminary parameters obtained via AUSFIDE, along with additionally measured cerebral venous blood volume. Numerical simulations and in vivo studies at 3 T were performed to evaluate the performance of the proposed, constrained qBOLD mapping in comparison to the parent qBOLD method. Measured parameters (Yv, DBV, R′2,nh, nonblood magnetic susceptibility) in ten healthy subjects demonstrate the expected contrast across brain territories, while yielding group-averages of 64.0 ± 2.3 % and 62.2 ± 3.1 % for Yv and 2.8 ± 0.5 % and 1.8 ± 0.4 % for DBV in cortical gray and white matter, respectively. Given the Yv measurements, additionally quantified CBF in seven of the ten study subjects enabled whole-brain 3D CMRO2 mapping, yielding group averages of 134.2 ± 21.1 and 79.4 ± 12.6 µmol/100 g/min for cortical gray and white matter, in good agreement with literature values. The results suggest feasibility of the proposed method as a practical and reliable means for measuring neurometabolic parameters over an extended brain coverage.
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Cho J, Zhang J, Spincemaille P, Zhang H, Hubertus S, Wen Y, Jafari R, Zhang S, Nguyen TD, Dimov AV, Gupta A, Wang Y. 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. Magn Reson Med 2021; 87:1583-1594. [PMID: 34719059 DOI: 10.1002/mrm.29057] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 09/01/2021] [Accepted: 10/07/2021] [Indexed: 01/17/2023]
Abstract
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.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Hang Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Simon Hubertus
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yan Wen
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Ramin Jafari
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Shun Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Alexey V Dimov
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Cho J, Spincemaille P, Nguyen TD, Gupta A, Wang Y. Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD. Magn Reson Med 2021; 86:2635-2646. [PMID: 34110656 DOI: 10.1002/mrm.28875] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/02/2021] [Accepted: 05/10/2021] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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13
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He Y, Wang M, Yu X. High spatiotemporal vessel-specific hemodynamic mapping with multi-echo single-vessel fMRI. J Cereb Blood Flow Metab 2020; 40:2098-2114. [PMID: 31696765 PMCID: PMC7786852 DOI: 10.1177/0271678x19886240] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
High-resolution fMRI enables noninvasive mapping of the hemodynamic responses from individual penetrating vessels in animal brains. Here, a 2D multi-echo single-vessel fMRI (MESV-fMRI) method has been developed to map the fMRI signal from arterioles and venules with a 100 ms sampling rate at multiple echo times (TE, 3-30 ms) and short acquisition windows (<1 ms). The T2*-weighted signal shows the increased extravascular effect on venule voxels as a function of TE. In contrast, the arteriole voxels show an increased fMRI signal with earlier onset than venules voxels at the short TE (3 ms) with increased blood inflow and volume effects. MESV-fMRI enables vessel-specific T2* mapping and presents T2*-based fMRI time courses with higher contrast-to-noise ratios (CNRs) than the T2*-weighted fMRI signal at a given TE. The vessel-specific T2* mapping also allows semi-quantitative estimation of the oxygen saturation levels (Y) and their changes (ΔY) at a given blood volume fraction upon neuronal activation. The MESV-fMRI method enables vessel-specific T2* measurements with high spatiotemporal resolution for better modeling of the fMRI signal based on the hemodynamic parameters.
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Affiliation(s)
- Yi He
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Maosen Wang
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Xin Yu
- Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
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14
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Lee H, Wehrli FW. Venous cerebral blood volume mapping in the whole brain using venous-spin-labeled 3D turbo spin echo. Magn Reson Med 2020; 84:1991-2003. [PMID: 32243708 DOI: 10.1002/mrm.28262] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 02/27/2020] [Accepted: 02/29/2020] [Indexed: 11/12/2022]
Abstract
PURPOSE Venous cerebral blood volume (CBVv ) is a major contributor to BOLD contrast, and therefore is an important parameter for understanding the underlying mechanism. Here, we propose a velocity-selective venous spin labeling (VS-VSL)-prepared 3D turbo spin echo pulse sequence for whole-brain baseline CBVv mapping. METHODS Unlike previous CBVv measurement techniques that exploit the interrelationship between BOLD signals and CBVv , in the proposed VS-VSL technique both arterial blood and cerebrospinal fluid (CSF) signals were suppressed before the VS pulse train for exclusive labeling of venous blood, while a single-slab 3D turbo spin echo readout was used because of its relative immunity to magnetic field variations. Furthermore, two approximations were made to the VS-VSL signal model for simplified derivation of CBVv . In vivo studies were performed at 3T field strength in 8 healthy subjects. The performance of the proposed VS-VSL method in baseline CBVv estimation was first evaluated in comparison to the existing, hyperoxia-based method. Then, data were also acquired using VS-VSL under hypercapnic and hyperoxic gas breathing challenges for further validation of the technique. RESULTS The proposed technique yielded physiologically plausible baseline CBVv values, and when compared with the hyperoxia-based method, showed no statistical difference. Furthermore, data acquired using VS-VSL yielded average CBVv of 2.89%/1.78%, 3.71%/2.29%, and 2.88%/1.76% for baseline, hypercapnia, and hyperoxia, respectively, in gray/white matter regions. As expected, hyperoxia had negligible effect (P > .8), whereas hypercapnia demonstrated vasodilation (P << .01). CONCLUSION Upon further validation of the quantification model, the method is expected to have merit for 3D CBVv measurements across the entire brain.
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Affiliation(s)
- Hyunyeol Lee
- Laboratory for Structural, Physiologic, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Felix W Wehrli
- Laboratory for Structural, Physiologic, and Functional Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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15
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Englund EK, Fernández-Seara MA, Rodríguez-Soto AE, Lee H, Rodgers ZB, Vidorreta M, Detre JA, Wehrli FW. Calibrated fMRI for dynamic mapping of CMRO 2 responses using MR-based measurements of whole-brain venous oxygen saturation. J Cereb Blood Flow Metab 2020; 40:1501-1516. [PMID: 31394960 PMCID: PMC7308517 DOI: 10.1177/0271678x19867276] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Functional MRI (fMRI) can identify active foci in response to stimuli through BOLD signal fluctuations, which represent a complex interplay between blood flow and cerebral metabolic rate of oxygen (CMRO2) changes. Calibrated fMRI can disentangle the underlying contributions, allowing quantification of the CMRO2 response. Here, whole-brain venous oxygen saturation (Yv) was computed alongside ASL-measured CBF and BOLD-weighted data to derive the calibration constant, M, using the proposed Yv-based calibration. Data were collected from 10 subjects at 3T with a three-part interleaved sequence comprising background-suppressed 3D-pCASL, 2D BOLD-weighted, and single-slice dual-echo GRE (to measure Yv via susceptometry-based oximetry) acquisitions while subjects breathed normocapnic/normoxic, hyperoxic, and hypercapnic gases, and during a motor task. M was computed via Yv-based calibration from both hypercapnia and hyperoxia stimulus data, and results were compared to conventional hypercapnia or hyperoxia calibration methods. Mean M in gray matter did not significantly differ between calibration methods, ranging from 8.5 ± 2.8% (conventional hyperoxia calibration) to 11.7 ± 4.5% (Yv-based calibration in response to hyperoxia), with hypercapnia-based M values between (p = 0.56). Relative CMRO2 changes from finger tapping were computed from each M map. CMRO2 increased by ∼20% in the motor cortex, and good agreement was observed between the conventional and proposed calibration methods.
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Affiliation(s)
- Erin K Englund
- Laboratory for Structural, Physiologic and Functional Imaging (LSPFI), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Ana E Rodríguez-Soto
- Laboratory for Structural, Physiologic and Functional Imaging (LSPFI), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hyunyeol Lee
- Laboratory for Structural, Physiologic and Functional Imaging (LSPFI), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Zachary B Rodgers
- Laboratory for Structural, Physiologic and Functional Imaging (LSPFI), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marta Vidorreta
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.,Siemens Healthineers, Madrid, Spain
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Felix W Wehrli
- Laboratory for Structural, Physiologic and Functional Imaging (LSPFI), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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16
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Cherukara MT, Stone AJ, Chappell MA, Blockley NP. Model-based Bayesian inference of brain oxygenation using quantitative BOLD. Neuroimage 2019; 202:116106. [PMID: 31430532 PMCID: PMC7334042 DOI: 10.1016/j.neuroimage.2019.116106] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 07/30/2019] [Accepted: 08/16/2019] [Indexed: 11/17/2022] Open
Abstract
Streamlined Quantitative BOLD (sqBOLD) is an MR technique that can non-invasively measure physiological parameters including Oxygen Extraction Fraction (OEF) and deoxygenated blood volume (DBV) in the brain. Current sqBOLD methodology rely on fitting a linear model to log-transformed data acquired using an Asymmetric Spin Echo (ASE) pulse sequence. In this paper, a non-linear model implemented in a Bayesian framework was used to fit physiological parameters to ASE data. This model makes use of the full range of available ASE data, and incorporates the signal contribution from venous blood, which was ignored in previous analyses. Simulated data are used to demonstrate the intrinsic difficulty in estimating OEF and DBV simultaneously, and the benefits of the proposed non-linear model are shown. In vivo data are used to show that this model improves parameter estimation when compared with literature values. The model and analysis framework can be extended in a number of ways, and can incorporate prior information from external sources, so it has the potential to further improve OEF estimation using sqBOLD.
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Affiliation(s)
- Matthew T Cherukara
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Alan J Stone
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Michael A Chappell
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Nicholas P Blockley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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17
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Cho J, Zhang S, Kee Y, Spincemaille P, Nguyen TD, Hubertus S, Gupta A, Wang Y. Cluster analysis of time evolution (CAT) for quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent magnitude (qBOLD)-based oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO 2 ) mapping. Magn Reson Med 2019; 83:844-857. [PMID: 31502723 DOI: 10.1002/mrm.27967] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/07/2019] [Accepted: 08/04/2019] [Indexed: 01/01/2023]
Abstract
PURPOSE To improve the accuracy of QSM plus quantitative blood oxygen level-dependent magnitude (QSM + qBOLD or QQ)-based mapping of the oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using cluster analysis of time evolution (CAT). METHODS 3D multi-echo gradient echo and arterial spin labeling images were acquired in 11 healthy subjects and 5 ischemic stroke patients. DWI was also carried out on patients. CAT was developed for analyzing signal evolution over TE. QQ-based OEF and CMRO2 were reconstructed with and without CAT, and results were compared using region of interest analysis and a paired t-test. RESULTS Simulations demonstrated that CAT substantially reduced noise error in QQ-based OEF. In healthy subjects, QQ-based OEF appeared less noisy and more uniform with CAT than without CAT; average OEF with and without CAT in cortical gray matter was 32.7 ± 4.0% and 37.9 ± 4.5%, with corresponding CMRO2 of 148.4 ± 23.8 and 171.4 ± 22.4 μmol/100 g/min, respectively. In patients, regions of low OEF were confined within the ischemic lesions defined on DWI when using CAT, which was not observed without CAT. CONCLUSION The cluster analysis of time evolution (CAT) significantly improves the robustness of QQ-based OEF against noise.
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Affiliation(s)
- Junghun Cho
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Shun Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York
- Department of Radiology, Tongji Hospital, Wuhan, China
| | - Youngwook Kee
- Department of Radiology, Weill Cornell Medical College, New York, New York
| | | | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York
| | - Simon Hubertus
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medical College, New York, New York
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York
- Department of Radiology, Weill Cornell Medical College, New York, New York
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18
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Simulations of the effect of diffusion on asymmetric spin echo based quantitative BOLD: An investigation of the origin of deoxygenated blood volume overestimation. Neuroimage 2019; 201:116035. [PMID: 31326570 PMCID: PMC6996000 DOI: 10.1016/j.neuroimage.2019.116035] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 07/10/2019] [Accepted: 07/17/2019] [Indexed: 11/21/2022] Open
Abstract
Quantitative BOLD (qBOLD) is a technique for mapping oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) in the human brain. Recent measurements using an asymmetric spin echo (ASE) based qBOLD approach produced estimates of DBV which were systematically higher than measurements from other techniques. In this study, we investigate two hypotheses for the origin of this DBV overestimation using simulations and consider the implications for experimental measurements. Investigations were performed by combining Monte Carlo simulations of extravascular signal with an analytical model of the intravascular signal. HYPOTHESIS 1: DBV overestimation is due to the presence of intravascular signal which is not accounted for in the analysis model. Intravascular signal was found to have a weak effect on qBOLD parameter estimates. HYPOTHESIS 2: DBV overestimation is due to the effects of diffusion which are not accounted for in the analysis model. The effect of diffusion on the extravascular signal was found to result in a vessel radius dependent variation in qBOLD parameter estimates. In particular, DBV overestimation peaks for vessels with radii from 20 to 30 μm and is OEF dependent. This results in the systematic underestimation of OEF. IMPLICATIONS: The impact on experimental qBOLD measurements was investigated by simulating a more physiologically realistic distribution of vessel sizes with a small number of discrete radii. Overestimation of DBV consistent with previous experiments was observed, which was also found to be OEF dependent. This results in the progressive underestimation of the measured OEF. Furthermore, the relationship between the measured OEF and the true OEF was found to be dependent on echo time and spin echo displacement time. The results of this study demonstrate the limitations of current ASE based qBOLD measurements and provide a foundation for the optimisation of future acquisition approaches.
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19
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Jiang D, Lu H, Parkinson C, Su P, Wei Z, Pan L, Tekes A, Huisman TAGM, Golden WC, Liu P. Vessel-specific quantification of neonatal cerebral venous oxygenation. Magn Reson Med 2019; 82:1129-1139. [PMID: 31066104 DOI: 10.1002/mrm.27788] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 03/25/2019] [Accepted: 04/08/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE Noninvasive measurement of cerebral venous oxygenation (Yv ) in neonates is important in the assessment of brain oxygen extraction and consumption, and may be useful in characterizing brain development and neonatal brain diseases. This study aims to develop a rapid method for vessel-specific measurement of Yv in neonates. METHODS We developed a pulse sequence, named accelerated T2 -relaxation-under-phase-contrast (aTRUPC), which consists of velocity-encoding phase-contrast module to isolate pure blood signal, flow-insensitive T2 -preparation to quantify blood T2 , and turbo-field-echo (TFE) scheme for rapid image acquisition, which is critical for neonatal MRI. A series of studies were conducted. First, the pulse sequence was optimized in terms of TFE factor, velocity encoding (VENC), and slice thickness for best sensitivity. Second, to account for the influence of TFE acquisition on T2 quantification, simulation and experiments were conducted to establish the relationship between TFE-T2 and standard T2 . Finally, the complete aTRUPC sequence was applied on a group of healthy neonates and normative Yv values were determined. RESULTS Optimal parameters of aTRUPC in neonates were found to be a TFE factor of 15, VENC of 5 cm/s, and slice thickness of 10 mm. The TFE-T2 was on average 3.9% lower than standard T2 . These two measures were strongly correlated (R2 = 0.86); thus their difference can be accounted for by a correction equation, T2,standard = 1.2002 × T2,TFE - 10.6276. Neonatal Yv values in veins draining cortical brain and those draining central brain were 64.8 ± 2.9% and 70.2 ± 3.3%, respectively, with a significant difference (P =.02). CONCLUSION The aTRUPC MRI has the potential to provide vessel-specific quantification of cerebral Yv in neonates.
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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.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland
| | - Charlamaine Parkinson
- Neurosciences Intensive Care Nursery, Johns Hopkins School of Medicine, Baltimore, Maryland.,Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Pan Su
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Zhiliang Wei
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland
| | - Li Pan
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Siemens Healthineers, Baltimore, Maryland
| | - Aylin Tekes
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Neurosciences Intensive Care Nursery, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Thierry A G M Huisman
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Neurosciences Intensive Care Nursery, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - W Christopher Golden
- Neurosciences Intensive Care Nursery, Johns Hopkins School of Medicine, Baltimore, Maryland.,Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Peiying Liu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
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