Cerebral metabolic rate of oxygen (CMRO<sub>2</sub>) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level‐dependent imaging (qBOLD)

Cho, Junghun; Kee, Youngwook; Spincemaille, Pascal; Nguyen, Thanh D.; Zhang, Jingwei; Gupta, Ajay; Zhang, Shun; Wang, Yi

doi:10.1002/mrm.27135

Cited by 68 publications

(149 citation statements)

References 57 publications

(181 reference statements)

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“…Although the hypercapnia challenge was successful, the approach does require a nontrivial experimental setup, including the use of a gas delivery system. Recent proposals of QSM‐OEF without a challenge by minimizing local variance and QSM‐OEF in combination with qBOLD may overcome this difficulty by only acquiring data at baseline …”

Section: Discussionmentioning

confidence: 99%

“…By altering the brain‐susceptibility distributions using caffeine and hyperventilation as stimuli, microvascular OEF and CMRO 2 was mapped in the whole brain . Moreover, microvascular OEF can also be measured from QSM without applying a stimulus by minimizing local variance in small brain regions or by combining it with the quantitative BOLD method …”

Section: Introductionmentioning

confidence: 99%

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

Sun

Cho

et al. 2019

Magnetic Resonance in Med

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Purpose To compare regional oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2) quantified from the microvascular quantitative susceptibility mapping (QSM) using a hypercapnic gas challenge with those measured by the dual‐gas calibrated BOLD imaging (DGC‐BOLD) in healthy subjects. Methods Ten healthy subjects were scanned using a 3T MR system. The QSM data were acquired with a multi‐echo gradient‐echo sequence at baseline and hypercapnia. Cerebral blood flow data were acquired using the pseudo‐continuous arterial spin labeling technique. Baseline OEF and CMRO2 were calculated using QSM and cerebral blood flow measurements. The DGC‐BOLD data were also collected under a hypercapnic and a hyperoxic condition to yield baseline OEF and CMRO2. The QSM‐OEF and CMRO2 maps were compared with DGC‐BOLD OEF and CMRO2 maps using region of interest (vascular territories) analysis and Bland‐Altman plots. Results Hypercapnia is a robust stimulus for mapping OEF in combination with QSM. Average OEF in 16 vascular territory regions of interest across 10 subjects was 0.40 ± 0.04 by QSM‐OEF and 0.38 ± 0.09 by DGC‐BOLD. The average CMRO2 was 176 ± 35 and 167 ± 53 μmol O2/min/100g by QSM‐OEF and DGC‐BOLD, respectively. A Bland‐Altman plot of regional OEF and CMRO2 in regions of interest revealed a statistically significant but small difference (OEF difference = 0.02, CMRO2 difference = 9 μmol O2/min/100g, p < .05) between the 2 methods for the 10 healthy subjects. Conclusion Hypercapnic challenge–assisted QSM‐OEF is a feasible approach to quantify regional brain OEF and CMRO2. Compared with DGC‐BOLD, hypercapnia QSM‐OEF results in smaller intersubject variability and requires only 1 gas challenge.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sun

Cho

et al. 2019

Magnetic Resonance in Med

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“…OEF in % and CMRO2 in µmol/100 g/min were reconstructed from the GESSE data following the approach of Cho et al

OEF = 1 - \frac{Y}{Y_{a}},

CMRO 2 = CBF \cdot OEF \cdot {(][, H)}_{a} .

…”

Section: Methodsmentioning

confidence: 99%

“…Additional unknowns are the deoxygenated blood volume ν, the transverse relaxation rate R 2 , the signal at the SE of the GESSE S 0 and the non‐blood susceptibility χ nb . QSM and qBOLD data were combined into 1 minimization term culminating in the cost function

Y^{*}, ν^{*}, R_{2}^{*}, S_{0}^{*}, {normalχ}_{nb}^{*} = {argmin}_{Y, ν, R_{2}, S_{0}, {normalχ}_{nb}} \{\underset{TE}{false\sum} {(∥∥, | S_{GESSE} (TE) | - F_{qBOLD} ()(, Y, ν, R_{2}, S_{0}, χ_{nb}, TE))}_{2}^{2} + w {(∥∥, QSM - F_{QSM} ()(, Y, ν, χ_{nb}))}_{2}^{2}\},

= {argmin}_{Y, ν, R_{2}, S_{0}, {normalχ}_{nb}} \{E_{qBOLD} + w \cdot E_{QSM}\} .

…”

Section: Methodsmentioning

confidence: 99%

“…Cho et al used a multi gradient echo (GRE) sequence for their combined approach and applied the lately developed qBOLD model of Ulrich and Yablonskiy for reconstruction. Despite the good performance of the model in gray matter (GM), it was biased in white matter (WM) when sampling with short echo times owing to the fast T 2 component associated with myelin .…”

Section: Introductionmentioning

confidence: 99%

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Comparison of gradient echo and gradient echo sampling of spin echo sequence for the quantification of the oxygen extraction fraction from a combined quantitative susceptibility mapping and quantitative BOLD (QSM+qBOLD) approach

Hubertus

Thomas

Cho

et al. 2019

Magnetic Resonance in Med

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Purpose To compare gradient echo (GRE) and gradient echo sampling of spin echo (GESSE) sequences for the quantification of the oxygen extraction fraction (OEF) from combined quantitative BOLD and quantitative susceptibility mapping (QSM) with regard to accuracy, precision and parameter initialization. Methods GRE and GESSE data were acquired from 7 healthy volunteers. QSM was applied to the GRE data and used as a regularization for the single‐compartment quantitative BOLD fit to the GESSE and GRE data, respectively, to quantify OEF, deoxygenated blood volume (ν), R2, and non‐blood susceptibility (χnb). Intersubject means within gray and white matter, respectively, were compared between GESSE and GRE (Student's t) and gray–white matter contrast was determined for each sequence separately. A single‐ and multi‐compartment simulation was used to compare reconstruction accuracy. Results Intersubject means and SDs for gray and white matter were OEF = 32.4 ± 1.6%, ν = 2.9 ± 0.1%, R2 = 14.2 ± 0.5 Hz, χnb = −43 ± 5 ppb for GESSE and OEF = 43.0 ± 5.4%, ν = 3.5 ± 0.4%, R2 = 14.4 ± 0.7 Hz, χnb = −43 ± 8 ppb for GRE with a significant difference (P < 0.05) for OEF and ν. Gray–white matter contrast was significant (P < 0.05) in all parameters for GESSE but only in ν and R2 for GRE. All parameters reconstructed from GESSE had higher accuracy than from GRE in the single‐ but not multi‐compartment simulation. Conclusion GESSE yields higher parameter accuracy in simulated gray matter but produces unphysiological gray–white matter contrast in OEF in vivo. GRE produces uniform OEF maps in vivo and is more efficient, which could facilitate a clinical implementation, but revealed biases in simulation. The appropriate sequence should be chosen depending on application.

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Sleep‐stage‐dependent alterations in cerebral oxygen metabolism quantified by magnetic resonance

Xu,

Wiemken,

Langham

et al. 2024

J of Neuroscience Research

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

Abstract: Quantitative CMRO mapping that fits the entire complex GRE data is feasible by combining QSM analysis of phase and qBOLD analysis of magnitude.

Cited by 68 publications

References 57 publications

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

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

Comparison of gradient echo and gradient echo sampling of spin echo sequence for the quantification of the oxygen extraction fraction from a combined quantitative susceptibility mapping and quantitative BOLD (QSM+qBOLD) approach

Sleep‐stage‐dependent alterations in cerebral oxygen metabolism quantified by magnetic resonance

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

Abstract: Quantitative CMRO mapping that fits the entire complex GRE data is feasible by combining QSM analysis of phase and qBOLD analysis of magnitude.

Cited by 68 publications

References 57 publications

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

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

Comparison of gradient echo and gradient echo sampling of spin echo sequence for the quantification of the oxygen extraction fraction from a combined quantitative susceptibility mapping and quantitative BOLD (QSM+qBOLD) approach

Sleep‐stage‐dependent alterations in cerebral oxygen metabolism quantified by magnetic resonance

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