Quantitative susceptibility mapping‐based cerebral metabolic rate of oxygen mapping with minimum local variance

et al. 2019

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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.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…CMRO 2 (µmol/100 g/min) and OEF (%) can be expressed as

{C M R O}_{2} = C B F \cdot O E F \cdot {[H]}_{a}

O E F = 1 - \frac{Y}{Y_{a}}

where CBF is the cerebral blood flow (mL/100 g/min),

{[H]}_{a}

is the oxygenated heme molar concentration in the arteriole (7.377 μmol/mL) estimated from

{[H]}_{a} = [H] \cdot Y_{a}

, where

[H] = 7.53

μmol/mL is the heme molar concentration in tissue blood assuming a hematocrit of

Hct = 0.357

Ya and

Y

are the arterial (assumed to be 0.98) and venous oxygenation.…”

Section: Theorymentioning

confidence: 99%

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

Kee

Spincemaille

et al. 2018

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126

“…The mean OEF in GM and WM in both cases is at the higher end of the range of 30% to 45% found in literature . However, the ANN produces a lower intersubject variability compared to least‐squares approaches . Taking into account the negative linear trend displayed in the Bland‐Altman plot and the mean difference that is compatible with 0, the ANN implemented here also seems to have a regularizing effect on the intersubject mean OEF.…”

Section: Discussionmentioning

confidence: 48%

Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD

Hubertus

Thomas

et al. 2019

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Purpose To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi‐Newton (QN) method for numerical optimization. Methods Random combinations of OEF, deoxygenated blood volume (ν), R2, and nonblood magnetic susceptibility (χnb) with each parameter following a Gaussian distribution that represented physiological gray matter and white matter values were used to simulate quantitative BOLD signals and QSM values. An ANN was trained with the simulated data with added Gaussian noise. The ANN was applied to multigradient echo brain data of 7 healthy subjects, and the reconstructed parameters and maps were compared to QN results using Student t test and Bland‐Altman analysis. Results Intersubject means and SDs of gray matter were OEF =43.5±0.8%, R2=13.5±0.3 Hz, ν=3.4±0.1%, χnb=-25±5 ppb for ANN; and OEF = 43.8±5.2%, R2=12.2±0.8 Hz, ν=4.2±0.6%, χnb=-39±7 ppb for QN, with a significant difference (P<0.05) for R2, ν, and χnb. For white matter, they were OEF = 47.5±1.1%, R2=17.1±0.4 Hz, ν=2.5±0.2%, χnb=-38±5 ppb for ANN; and OEF =42.3±5.6%, R2=16.7±0.7 Hz, ν=2.9±0.3%, χnb=-45±9 ppb for QN, with a significant difference (P<0.05) for OEF and ν. ANN revealed more gray–white matter contrast but less intersubject variation in OEF than QN. In contrast to QN, the ANN reconstruction did not need an additional sequence for parameter initialization and took approximately 1 s rather than roughly 1 h. Conclusion ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.