Quantitative Perfusion, Oxygenation, and CMRO2 Imaging in Post-Acetazolamide Moyamoya Disease Patients

Wendy Ni^1,2, Thomas Christen², and Greg Zaharchuk²

¹Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ²Department of Radiology, Stanford University, Stanford, CA, United States

Synopsis

The acetazolamide challenge can be used to assess cerebrovascular reserve and oxygenation in patients with steno-occlusive diseases such Moyamoya, thus enabling evaluation of the quantitative blood oxygen-level dependent (qBOLD) approach of modeling tissue oxygenation. In this study, we mapped post-acetazolamide oxygenation (with transverse relaxation rate R₂’), CBF with arterial spin labeling (ASL), and CBV with dynamic susceptibility contrast (DSC). We found that angiographically abnormal tissues are relatively hypoperfused and hypoxic. Finally, we investigated a qBOLD biophysical model for quantitative tissue oxygenation which suggested no difference in the cerebral metabolic rate of oxygen consumption (CMRO₂) between normal and affected regions.

Purpose

Acetazolamide (ACZ) injection is a robust clinical perfusion challenge for assessing cerebrovascular reserve¹. For steno-occlusive pathologies such as Moyamoya disease, oxygenation mapping may provide additional dialogistic information². In this study, we perform R₂’ relaxometry for oxygenation mapping, multi-delay ASL for measuring cerebral blood flow (CBF), and dynamic susceptibility contrast (DSC) for measuring cerebral blood volume (CBV) after ACZ injection in a cohort of pre-operative Moyamoya disease patients. We also compare our findings with a quantitative BOLD (qBOLD) oxygenation model³ to estimate the cerebral metabolic rate of oxygen consumption (CMRO₂) in both affected and non-affected tissues.

Methods

19 Moyamoya disease patients (ages 39±12 y, 14 F) were scanned with informed consent and IRB approval, at 3.0T (MR750W, GE-Healthcare) with ACZ injection (1g IV). Post-ACZ R2’ mapping was performed using GESFIDE⁴ (TESE/TR 100/2000ms, TE 5-130ms, 40 echoes, resolution 1.9×1.9×1.5mm³,14 slices). Post-ACZ perfusion was mapped with two sequences: a) multi-delay pseudo-continuous ASL⁵ (TR/TE 6518/25.1ms, label time 2000ms, 5 equally spaced PLDs 700-3000ms, resolution 3.4×3.4×4mm³, 36 axial slices), offering improved quantitation in areas of slow collateral flow; b) DSC imaging (TR/TE 1800/40ms, resolution 1.9×1.9×5.0mm³ with 20 slices) using 0.1mmol/kg of gadolinium contrast (Multihance, Bracco). Whole-brain T1-weighted 3D IR-FSPGR and TOF MRA of the Circle of Willis were performed.

GESFIDE images were analyzed in native space, with R₂’ maps calculated using mono-exponential fitting⁴. CBF maps were calculated from ASL images⁶, while DSC images were processed⁷ using commercial software (RAPID, iSchemaView) before combined ASL-DSC (CAD) correction⁸ was applied for absolute CBV quantitation. Using a multiparametric qBOLD approach⁹ with arterial/venous blood volume ratio 30/70 ¹⁰, we created tissue oxygen saturation (StO₂) and CMRO₂ maps. Data was analyzed using 3cm annular, mixed-cortical ROIs approximating major arterial territories, with 6 radial segments at two standard 14mm-thick levels. Each ROI was classified “normal” or “abnormal” by blinded inspection of TOF MRA images and axial projection by an experienced neuroradiologist. Comparisons were performed using the 2-tailed t-test with α=0.05.

Results

The results from group-level analysis (N=228) are shown in Table 1. Post-ACZ R₂’ and CBF significantly differed between normal and abnormal ROIs (respectively, 16±31% higher and 13±45% lower in abnormal ROIs), while CBV did not (p=0.18).

Evaluating all regions, R2’ significantly decreased with increased perfusion metrics, consistent with better oxygenation in regions with better perfusion (Fig. 2-3): (normal) R₂’=-0.027*CBV +3.23, R²=0.06, p<0.001; and (abnormal) R₂’=-0.015*CBF +3.94, R²=0.19, p<0.001. The slopes of regression lines were not significantly different between normal and abnormal data, while the intercepts were.

Finally, we calculated post-ACZ StO₂ to be mostly over 99% and significantly (p<0.05) lower in abnormal ROIs, though only by a small amount. Post-ACZ CMRO₂ did not differ between normal and abnormal regions.

Discussion

Our study found that post-ACZ CBF, measured via multi-delay ASL, discriminated between angiographically normal and abnormal regions. Since diseased tissues experience chronic vasodilation at baseline and have limited ability to further vasodilate, it is reasonable that no differences in post-ACZ CBV were observed.

R₂’ imaging showed that the amount of deoxyhemoglobin also differed in normal and abnormal tissues. According to the qBOLD biophysical model presented by Yablonskiy and Haacke³: $$$R_2 \propto Hct\cdot DBV \cdot (1-StO_2)$$$, where Hct is capillary hematocrit, DBV is deoxygenated blood volume and StO₂ is tissue oxygen saturation, we conclude that StO₂ is lower in abnormal regions, consistent with our expectation that some of them experience chronic under-oxygenation. However, the weak correlation (Fig. 2) suggests that Hct and/or post-ACZ StO₂ were not sufficiently constant across data points, and/or CBV was not proportional to DBV. The significant offset indicates sources of R₂’ other than oxygenation, some of which have previously been discussed in literature⁴.

Finally, the StO₂ values calculated from the multiparametric qBOLD approach were significantly higher compared to literature¹¹, though StO₂ was lower in affected regions, some of which are expected to be under-oxygenated. The source of the high StO₂ values appears to be the very high CBV values measured (Table 1). CMRO₂ in angiographically normal regions was comparable to measurements obtained via positron emission tomography¹². The lack of significant difference between normal and abnormal regions may indicate that most patients in our study are not at late stage of Moyamoya disease where tissue metabolism is reduced.

Conclusion

In this study, we imaged post-ACZ perfusion in Moyamoya disease patients using multi-delay ASL and DSC. We also imaged R₂’ for oxygenation quantitation with qBOLD methods. Our data agreed with our expectation that angiographically abnormal tissues are under-oxygenated and less well-perfused during a perfusion challenge, but quantitation of StO₂ requires needs further evaluation.

Acknowledgements

NIH R01NS066506, R01NS047607, R21NS087491, NCRR 5P41RR09784. GE Healthcare.

References

1. AS Vagal et al., Am J Neuroradiol, 2009.

2. W Powers, Ann Neurol, 1991.

3. DA Yablonskiy & EM Haacke, Mag Reson Med, 1994.

4. W Ni et al., Mag Reson Med, 2015.

5. R Wang et al., Eur Radiol, 2014.

6. W Dai et al., Mag Reson Med, 2012.

7. M Straka et al., J Mag Reson Imag, 2010.

8. G Zaharchuk et al., Mag Reson Med, 2010.

9. T Christen et al., Mag Reson Med, 2012.

10. D Bulte et al., Proc ISMRM, 2015.

11. V Faoro et al., J Appl Physiol, 2007.

12. H Okazawa et al., JCBFM, 2001.

Figures

Table 1: Post-ACZ measurements showed significant differences in R₂’, CBF, Tmax and StO₂ between angiographically normal and abnormal ROIs, while CBV and CMRO₂ was the same in both regions.

Fig. 1: Example of (a) axial maximum intensity projection (MIP) of TOF MRA indicating an occluded MCA and a stenosed ACA; and (b-c) two levels of analysis with overlaid regions of interest (ROIs) in which the color indicates its angiographic status.

Fig. 2: Linear regression of R₂’ vs. CBV found a very weak negative correlation. Separate fitting of normal and abnormal data found significantly different (p<0.05) intercepts but identical slopes: y_normal=-0.024x+2.98 and y_abnormal=-0.024x+3.40, potentially indicating a non-perfusion, non-oxygenation source of R₂’ affected by disease, or a non-linear relationship in general.

Fig. 3: Linear regression of R₂’ vs. CBF also found a slightly stronger negative correlation. Separate fitting found significantly different intercepts and slopes: y_normal=-0.007x+3.23 and y_abnormal=-0.017x+4.17.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

1432