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Cerebral oxygen extraction fraction mapping: comparison of dual-gas challenge calibrated BOLD and challenge-free gradient echo QSM+qBOLD
Junghun Cho1, Yuhan Ma2, Pascal Spincemaille3, Bruce Pike2,4, and Yi Wang1,3
1Biomedical Engineering, Cornell University, New York, NY, United States, 2McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada, 3Radiology, Weill Cornell Medical College, New York, NY, United States, 4Radiology, University of Calgary, Calgary, AB, Canada

Synopsis

In this study, we compare cerebral oxygen extraction fraction (OEF) and metabolic rate of oxygen consumption (CMRO2) maps obtained using dual-gas challenge calibrated-BOLD (DGC) and challenge-free gradient echo quantitative susceptibility mapping plus quantitative blood oxygen level-dependent modeling (QSM+qBOLD or QQ) to explore the important clinical advantage of challenging-free data acquisition. In n=11 healthy subjects, cortical gray matter average OEF was not significantly different (36.4±1.9% and 38.0±9.1%, P=0.63) as was CMRO2 (151.4±17.6 and 168.2±54.1 μmolO2/min/100g, P=0.26), for QQ and DGC, respectively. QQ can measure OEF and CMRO2 at both baseline and hypercapnia independently, showing a 14% CMRO2 decrease in hypercapnia (P=0.039).

Introduction

Cerebral metabolic rate of oxygen consumption (CMRO2) and oxygen extraction fraction (OEF) are crucial for studying various diseases (1-4)(5)(6). Dual-gas challenge calibrated BOLD (DGC) (7) has been developed to map OEF and CMRO2 using hypercapnia and hyperoxia (8,9), but is difficult to administrate in clinical practice. Recently, a combined QSM modeling of phase signal and qBOLD modeling of phase signal (QSM+qBOLD or QQ) has been introduced to map OEF and CMRO2 without challenge (10,11), showing great potential for clinical use. The purpose here is to compare QQ with DGC and investigate changes in CMRO2 during hypercapnia, which is assumed to be constant in calibrated BOLD (7).

Theory and Methods

OEF and CMRO2 can be expressed: $$$CMRO2 = CBF\cdot Y_{a}\cdot [H]\cdot OEF$$$ with $$$CBF$$$ as cerebral blood flow (m/100g/min), $$$Y_{a}$$$ arterial oxygenation, $$$[H]$$$ heme concentration.
QQ
Oxygenation $$$Y$$$ and other parameters are estimated from multiecho gradient echo (mGRE) data through a combined QSM (phase) and qBOLD (magnitude) model (10,11): $$Y^{*},v^{*},R_{2}^{*},S_0^*,\chi_{nb}^{*}=\begin{array}{c}argmin\\Y,v,R_{2}, S_0, \chi_{nb}\end{array}\left\{ \begin{array}{c}\begin{array}{c}w||F_{QSM}\left(Y,v,\chi_{nb}\right)-\chi||_{2}^{2}\\+||S(t)-S_{qBOLD}\left(S_{0,},Y,v,R_{2,}\chi_{nb},t\right)||_{2}^{2}+\lambda\left(\overline{OEF(Y)}-OEF_{wb}\right)^{2}\end{array}\end{array}\right\} $$ Here $$$w$$$ is a weighting factor; $$F_{QSM}(Y,v,\chi_{nb})=\left[\frac{\chi_{ba}}{\alpha}+\psi_{Hb}\cdot\Delta\chi_{Hb}\cdot\left(-Y+\frac{1-\left(1-\alpha\right)\cdot Y_{a}}{\alpha}\right)\right]\cdot v + \left(1-\frac{v}{\alpha}\right)\cdot \chi_{nb}$$ where $$$v$$$ venous blood volume, $$$\chi_{nb}$$$ non-blood tissue susceptibility, $$$\chi_{ba}=-108.3$$$ppb (12), $$$\alpha$$$=0.77 (13), $$$\psi_{Hb}$$$=0.0909 for Hct=0.357 (14-17), $$$\Delta \chi_{Hb}$$$ = 12522 ppb (18,19);$$S_{qBOLD}\left(t\right)=S_0\cdot e^{-R_2\cdot t}\cdot F_{BOLD}\left(Y,v,\chi_{nb},t\right)\cdot G(t)$$ where $$$G(t)$$$ a macroscopic field effect (10), $$$F_{BOLD}\left(Y,v,\chi_{nb},t\right)=exp\left(-v\cdot f_{s}\left(\delta\omega\cdot t\right)\right)$$$ (20) with $$$f_s$$$ a signal decay by the blood vessel network (21), $$\delta \omega\left(Y,\chi_{nb}\right)=\frac{1}{3}\cdot \gamma \cdot B_{0}\cdot \left[Hct\cdot \Delta \chi_{0}\cdot \left(1-Y\right) + \chi_{ba}-\chi_{nb}\right]$$ where $$$\gamma$$$=267.513MHz/T, $$$B_0$$$=3T, $$$\Delta \chi_{0}=4\pi\times0.27 ppm$$$ (22). The third term is the physiological constraint that the whole brain average OEF, $$$\overline{OEF(Y)}$$$, should be similar to $$$OEF_{wb}$$$, the OEF value estimated from the straight sinus (11). We used cluster analysis of time evolution (CAT) to improve the robustness against noise (11).
DGC
BOLD signal difference by R2* change between rest and respiratory challenges (hypercapnia and hyperoxia) (7) was modeled for baseline OEF calculation (9). First, the calibration parameter M was calculated based on the Davis model (23,24) from the BOLD and CBF changes during a hypercapnic condition, assuming that CMRO2 is not altered. $$\frac{\Delta BOLD}{BOLD_{0}}=M\left(1-\left(\frac{CBF}{CBF_0}\right)^{\eta-\beta}\left(\frac{CMRO_2}{CMRO_{2,0}}\right)^{\beta}\right)$$ where $$M\equiv TE\cdot A\cdot CBV_{0}\cdot \left[dHb\right]^{\beta}_{v_0}$$ with $$$\eta$$$=0.18 (25). $$$\beta$$$ =1.3 at 3T (26). The hyperoxic condition induces BOLD signal changes by decreasing [dHb] in venous and capillary blood (27,28):$$\frac{\Delta BOLD}{\Delta BOLD_{0}}=M\left(1-\left(\frac{CBF}{CBF_{0}}\right)^{\eta}\left(\frac{[dHb]_{v}}{[dHb]_{v_{0}}}+\frac{CBF_{0}}{CBF}-1\right)^{\beta}\right)$$ M is obtained from hypercapnia with Eq. 5. OEF then can be calculated from the [dHb] change with an assumed CBF decrease of -3.11% (9).
Validation
QQ was compared with DGC in 10 healthy subjects at 3T: 1) For QQ, 3D mGRE data (1 mm3 voxel size, TE1/ΔTE/TE7 = 4.6/4.06/29.0 ms, TR= 32 ms) was acquired at baseline and during a hypercapnic condition. 2) For DGC BOLD, dual-echo pCASL (TR/TE1/TE2 = 4000/10/30 ms, label duration/post-label delay = 1665/900 ms; 3.9 mm3 voxel size) was acquired under a baseline condition, a hypercapnic challenge condition (increased PetCO2 of 7 mmHg above baseline), and two hyperoxic conditions (increased PetO2 of 150 mmHg and 300 mmHg above baseline). For ROI analysis, the cortical gray matter (CGM) mask was automatically segmented (29), and retaining the voxels with significant BOLD response to hyperoxia z > 2.3 and realistic OEF values from 0 to 1. The obtained CGM mask was resampled to the QSM resolution for ROI analysis of the QQ result.

Result

QQ showed a small and statistically non-significant difference in OEF values compared to DGC (n=10): 36.4 ± 1.9 and 38.0 ± 9.1 % (P=0.63), with corresponding CMRO2 of 151.4 ± 17.6 and 168.2 ± 54.1 μmol O2/min/100g (P=0.26) for QQ and DGC, respectively (Fig. 1) Also, QQ showed more uniform OEF map than DGC (Fig. 1). In hypercapnia (n=10), QQ showed lower OEF (36.4 ± 1.9 vs. 22.0 ± 3.6 %, P<0.01), higher CBF (57.0 ± 9.9 vs. 80.2 ± 11.6 %, P<0.01), slightly lower CMRO2 maps (151.4 ± 17.6 vs. 130.2 ± 28.6 μmol O2/min/100g, P=0.039) compared to those at baseline (Fig. 2).

Discussion

OEF values from both QQ and DGC agree with literature values based on PET-OEF: 35 ± 7 % (30) and 40 ± 9 % (31). A high inter-subject variation in DGC may be attributed to low SNR in both BOLD and CBF signals, e.g. only 2.3 ± 0.5 % hypercapnia induced BOLD signal increase. The small OEF variation in QQ may have benefited from the usage of cluster analysis of time evolution (CAT) which improves effective SNR(11). Uniform OEF maps from QQ (Figure 1) is consistent with PET studies (31-33), which is difficult to investigate in DGC due to inaccuracies in white matter OEF caused by low BOLD and CBF SNR. QQ showed a 14% CMRO2 reduction in CGM due to hypercapnia. This agrees well with a recent study suggesting 10~15 % reduction with the similar hypercapnic level (34).

Conclusion

This study shows that a recently proposed QQ provided similar OEF values in CGM, compared to a well-established DGC. Based on this validation, QQ can be readily applied in various diseases, such as stroke (35), tumor (36), multiple sclerosis (37), since it does not require a gas challenge. Furthermore, QQ revealed a mild decrease in CMRO2 with hypercapnia, which shall be considered in future calibrated BOLD studies.

Acknowledgements

No acknowledgement found.

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Figures

Baseline OEF, CBF, CMRO2 maps between DGC and QQ in two healthy subjects. QQ CBF map is obtained by registering DGC CBF map to QSM resolution. QQ showed less inter-subject variation in OEF.

OEF, CBF, CMRO2 from QQ between baseline (BL) and hypercapnia (HC) in two healthy subjects. Compared to BL, HC shows lower OEF, higher CBF, slightly lower CRMO2.

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)
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