Elsevier

NeuroImage

Volume 125, 15 January 2016, Pages 920-931
NeuroImage

Baseline oxygenation in the brain: Correlation between respiratory-calibration and susceptibility methods

https://doi.org/10.1016/j.neuroimage.2015.11.007Get rights and content

Highlights

  • We compared two MRI methods to image oxygen extraction fraction (OEF): QUO2 and quantitative susceptibility mapping (QSM).

  • OEF values from QUO2 with three gas challenges correlated with OEF values from QSM in cerebral veins.

  • QUO2 measurements of OEF depend on accurate measurement of the hyperoxic BOLD signal.

  • Blood flow changes during visual stimulation correlated with baseline OEF by both methods.

Abstract

New MRI methods for noninvasive imaging of baseline oxygen extraction fraction (OEF) in the brain show great promise. Quantitative O2 imaging (QUO2) applies a biophysical model to measure OEF in tissue from BOLD, cerebral blood flow (CBF), and end-tidal O2 (ETO2) signals acquired during two or more gas manipulations. Alternatively, quantitative susceptibility mapping (QSM) maps baseline OEF along cerebral vessels based on the deoxyhemoblogin (dHb) susceptibility shift between veins and water. However, these approaches have not been carefully compared to each other or to known physiological signals. The aims of this study were to compare OEF values by QUO2 and QSM; and to see if baseline OEF relates to BOLD and CBF changes during a visual task.

Simultaneous BOLD and arterial spin labeling (ASL) scans were acquired at 7 T in 11 healthy subjects continuously during hypercapnia (5% CO2, 21% O2), hyperoxia (100% O2), and carbogen (5% CO2, 95% O2) for QUO2 analysis. Separate BOLD-ASL scans were acquired during a checkerboard stimulus to identify functional changes in the visual cortex. Gradient echo phase images were also collected at rest for QSM reconstruction of OEF along cerebral veins draining the visual cortex.

Mean baseline OEF was (43.5 ± 14)% for QUO2 with two gases, (42.3 ± 17)% for QUO2 with three gases, and (29.4 ± 3)% for QSM across volunteers. Three-gas QUO2 values of OEF correlated with QSM values of OEF (P = 0.03). However, Bland–Altman analysis revealed that QUO2 tended to measure higher baseline OEF with respect to QSM, which likely results from underestimation of the hyperoxic BOLD signal and low signal-to-noise ratio of the ASL acquisitions. Across subjects, the percent CBF change during the visual task correlated with OEF measured by 3-gas QUO2 (P < 0.04); and by QSM (P = 0.035), providing evidence that the new methods measure true variations in brain physiology across subjects.

Introduction

Adequate oxygen supply is critical to the health of cerebral tissues, and cerebral oxygen extraction fraction (OEF) is thought to be highly conserved across the brain (Hatazawa et al., 1995, Ishii et al., 1996b). While OEF is preserved across a wide range of physiological states in normal brain function, it is also known to be altered by cerebrovascular (Derdeyn et al., 1998, Yamauchi et al., 1999) and neurodegenerative disorders (Ishii et al., 1996a). The ability to noninvasively image OEF would thus provide important clinical information in patients where the brain oxygen supply may be disrupted. This information may also improve our understanding of the baseline physiology that underlies vascular and metabolic blood-oxygen-level-dependent (BOLD) signal changes. Although [15O] positron emission tomography (PET) is the accepted reference method to quantify OEF maps in the brain (Ito et al., 2005), it is rarely used in the clinic due to the need for a cyclotron on site to produce short half-life [15O]-tracers, experimental complexity, and use of ionizing radiation. Recognition of the importance of sophisticated OEF mapping and the current limitations in its measurement has prompted recent efforts to develop magnetic resonance imaging (MRI) alternatives. A variety of MRI methods have been proposed to measure global and local OEF from T2 relaxation (Bolar et al., 2011, Guo and Wong, 2012, Lu and Ge, 2008), respiratory calibration (Bulte et al., 2012, Gauthier and Hoge, 2012, Wise et al., 2013), and magnetic susceptibility contrasts (Fan et al., 2012, Haacke et al., 1997, Jain et al., 2010).

One new class of MRI methods utilizes respiratory calibration with multiple gas challenges to quantify tissue OEF in the brain. Traditional calibrated BOLD techniques have relied on a single isometabolic gas challenge, such as hypercapnia or hyperoxia, to measure relative changes in oxygen metabolism during a functional task (Chiarelli et al., 2007, Davis et al., 1998). These approaches typically assume a baseline OEF value and estimate the M parameter, the maximum achievable BOLD signal at rest due to deoxyhemoglobin (dHb), as an intermediate step in the processing. On the other hand, the recently proposed generalized calibration model (GCM) enables biophysical modeling of BOLD MRI, perfusion MRI, and end-tidal O2 (ETO2) responses to arbitrary combinations of hyperoxia and hypercapnia (Gauthier and Hoge, 2013). Through use of multiple gas manipulations and the GCM, local baseline values of M and OEF are available per tissue voxel. Several variants of this respiratory calibration approach have been implemented with pure hyperoxia and hypercapnia (Bulte et al., 2012, Germuska and Bulte, 2014), or multiple combinations of gases with different combinations of O2 and CO2 concentrations (Wise et al., 2013). In this work, we focus on a specific variant known as QUantitative O2 (QUO2) MRI (Gauthier et al., 2012), which provides a graphical interpretation of the GCM and was originally proposed with use of three gases.

On the other hand, magnetic susceptibility represents a distinct MRI contrast mechanism that reflects the magnetizability of a tissue and is thus sensitive to brain oxygenation. The OEF level in cerebral veins directly relates to the concentration of paramagnetic dHb molecules in the vessels (Weisskoff and Kiihne, 1992). The presence of dHb molecules changes the magnetic susceptibility in venous blood relative to reference tissue, such as the cerebrospinal fluid, which can be measured using gradient echo phase images. These magnetic field perturbations are non-local and depend on the geometry of the object and its orientation with respect to the main magnetic field, B0. Based on the observed MRI field maps, quantitative susceptibility mapping (QSM) methods (Bilgic et al., 2014, de Rochefort et al., 2008, Liu et al., 2009, Liu et al., 2012) have been proposed to invert the dipole imaging kernel and reconstruct the underlying susceptibility distribution. These QSM reconstructions typically rely on prior information about the spatial “smoothness” of the desired susceptibility, and allow measurement of susceptibility, and thus OEF, along brain vessels of arbitrary orientation and geometry. Given sufficient resolution to measure susceptibility within the veins, quantitative oxygenation venograms that measure baseline OEF along the venous vasculature of the brain are then available (Fan et al., 2014, Haacke et al., 2010, Xu et al., 2014).

While these new MRI approaches to assess absolute OEF are promising, they have not been carefully compared to each other or to known physiological signals, such as BOLD contrast and cerebral blood flow (CBF). Only a few studies have investigated the reproducibility of MRI-based oxygenation mapping across different sites (Liu et al., 2015), begun to compare global measurements of baseline OEF in healthy volunteers (Barhoum et al., 2015, Rodgers et al., 2015), or estimated relative changes of OEF compared to corresponding changes in BOLD and CBF (Donahue et al., 2009, Lu and van Zijl, 2005). Our aims were thus to compare two variants of QUO2 (with two and three gases, respectively) against independent OEF values by QSM analysis; as well as to see if baseline OEF measurements by these methods relate to BOLD and perfusion signals elicited during a visual functional task. These experiments were performed at 7 Tesla (7 T) to achieve high spatial resolution and localize OEF values to the visual cortex for comparison.

Section snippets

MRI acquisitions

Eleven healthy volunteers (9 female, ages 22–32 years, free of vascular abnormalities) were scanned on a MAGNETOM 7 T scanner (Siemens Healthcare, Erlangen, Germany) with a 24-channel head coil (6 volunteers) or a 32-channel head coil (all remaining participants). All procedures were approved by the Ethics Committee of the University of Leipzig and informed written consent was given by all volunteers. Manual shimming was performed to optimize field homogeneity over the imaging and labeling

Results

Table 1 lists the baseline OEF (%) measured in each volunteer from the visual ROI. OEF values are shown for QUO2 with data from hypercapnia and hyperoxia (2-gas), QUO2 with data from all three gas challenges (3-gas), and QSM in vessels. Individual plots of M (%) versus OEF (%) for each gas are shown as an intermediate step of the QUO2 technique in Fig. 2 for each subject and for group analysis. Group mean OEF values were (43.5 ± 14)% for 2-gas QUO2, (42.3 ± 17)% for 3-gas QUO2, and (29.4 ± 3)% for

Discussion

The present study compared two advanced MRI techniques to measure absolute, resting brain oxygenation in the visual cortex of healthy volunteers. We compared 2-gas and 3-gas versions of QUO2 against QSM for OEF assessment, and found that the 3-gas QUO2 values more strongly related to QSM values. Although susceptibility and respiratory calibration methods provided OEF estimates that were correlated, a non-constant overestimation bias in OEF was found with QUO2 relative to QSM. Further analysis

Acknowledgments

We thank Dr. Robert Trampel, Domenica Wilfling, and Elisabeth Wladimirow for their indispensible technical support during MRI experiments. This work was supported by the Alexander von Humboldt Foundation (C.J.G.), the Fonds de Recherche Santé Québec (C.J.G.), the Natural Sciences and Engineering Research Council of Canada (RGPIN-2015-04665) and the MIT-Germany Program (A.P.F.).

References (75)

  • K.E. Hammond et al.

    Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases

    NeuroImage

    (2008)
  • H.V. Hare et al.

    Investigating the field-dependence of the Davis model: calibrated fMRI at 1.5, 3 and 7 T

    NeuroImage

    (2015)
  • S. Iscoe et al.

    Hyperoxia-induced hypocapnia: an underappreciated risk

    Chest

    (2005)
  • C. Kolbitsch et al.

    The influence of hyperoxia on regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV) and cerebral blood flow velocity in the middle cerebral artery (CBFVMCA) in human volunteers

    Magn. Reson. Imaging

    (2002)
  • J. Liau et al.

    Inter-subject variability in hypercapnic normalization of the BOLD fMRI response

    NeuroImage

    (2009)
  • T.T. Liu et al.

    A signal processing model for arterial spin labeling functional MRI

    NeuroImage

    (2005)
  • J. Liu et al.

    Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map

    NeuroImage

    (2012)
  • Z.B. Rodgers et al.

    Rapid T2- and susceptometry-based CMRO2 quantification with interleaved TRUST (iTRUST)

    NeuroImage

    (2015)
  • F. Schweser et al.

    Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism?

    NeuroImage

    (2011)
  • R.G. Wise et al.

    Measurement of OEF and absolute CMRO2: MRI-based methods using interleaved and combined hypercapnia and hyperoxia

    NeuroImage

    (2013)
  • S. Barhoum et al.

    Comparison of MRI methods for measuring whole-brain venous oxygen saturation

    Magn. Reson. Med.

    (2015)
  • P.L. Bazin et al.

    Automated vessel segmentation from quantitative susceptibility maps at 7 Tesla

    Proc. Int. Soc. Magn. Reson. Med.

    (2015)
  • B. Bilgic et al.

    Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection

    Magn. Reson. Med.

    (2014)
  • N.P. Blockley et al.

    A review of calibrated blood oxygenation level-dependent (BOLD) methods for the measurement of task-induced changes in brain oxygen metabolism

    NMR Biomed.

    (2013)
  • D.S. Bolar et al.

    QUantitative Imaging of eXtraction of oxygen and TIssue consumption (QUIXOTIC) using venular-targeted velocity-selective spin labeling

    Magn. Reson. Med.

    (2011)
  • J.L. Boxerman et al.

    The intravascular contribution to fMRI signal change: Monte Carlo modeling and diffusion-weighted studies in vivo

    Magn. Reson. Med.

    (1995)
  • D.P. Bulte et al.

    Cerebral perfusion response to hyperoxia

    J. Cereb. Blood Flow Metab.

    (2007)
  • T.L. Davis et al.

    Calibrated functional MRI: mapping the dynamics of oxidative metabolism

    Proc. Natl. Acad. Sci. U. S. A.

    (1998)
  • L. de Rochefort et al.

    Quantitative MR susceptibility mapping using piece-wise constant regularized inversion of the magnetic field

    Magn. Reson. Med.

    (2008)
  • C.P. Derdeyn et al.

    Increased oxygen extraction fraction is associated with prior ischemic events in patients with carotid occlusion

    Stroke

    (1998)
  • M.J. Donahue et al.

    Cerebral blood flow, blood volume, and oxygen metabolism dynamics in human visual and motor cortex as measured by whole-brain multi-modal magnetic resonance imaging

    J. Cereb. Blood Flow Metab.

    (2009)
  • A.P. Fan et al.

    Phase-based regional oxygen metabolism (PROM) using MRI

    Magn. Reson. Med.

    (2012)
  • A.P. Fan et al.

    Quantitative oxygenation venography from MRI phase

    Magn. Reson. Med.

    (2014)
  • C.J. Gauthier et al.

    A generalized procedure for calibrated MRI incorporating hyperoxia and hypercapnia

    Hum. Brain Mapp.

    (2013)
  • J. Guo et al.

    Venous oxygenation mapping using velocity-selective excitation and arterial nulling

    Magn. Reson. Med.

    (2012)
  • E.M. Haacke et al.

    In vivo measurement of blood oxygen saturation using magnetic resonance imaging: a direct validation of the blood oxygen level-dependent concept in functional brain imaging

    Hum. Brain Mapp.

    (1997)
  • E.M. Haacke et al.

    Susceptibility mapping as a means to visualize veins and quantify oxygen saturation

    J. Magn. Reson. Imaging

    (2010)
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    Present address: Lucas Center for Imaging, 1201 Welch Road, Stanford CA, 94043, USA.

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