4862

Non-Invasive Perfusion MR Imaging of the Human Brain via Breath-Holding

Jacob Benjamin Schulman¹, Sriranga Kashyap², Seong-Gi Kim³, and Kamil Uludag¹
¹Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, ²Krembil Brain Institute, University Health Network, Toronto, ON, Canada, ³Center for Neuroscience Imaging Research, Suwon, Korea, Republic of

Synopsis

Keywords: Perfusion, Perfusion

Motivation: DSC is the leading methodology for MR-based perfusion imaging. However, the technique's reliance on invasive gadolinium injections poses a major limitation.

Goal(s): Can breath-holding induce perfusion contrast that is exploitable using DSC MRI?

Approach: Ten healthy subjects underwent MRI at both 3T and 7T, while performing eight 16 s breath-holds. Breath-hold-induced signal changes were fed into a DSC MRI analysis pipeline, and perfusion was quantified.

Results: Calculated cerebral perfusion values were within the physiological range of literature values; the breath-hold task yielded significantly higher contrast-to-noise and GM-to-WM contrast with higher field strength and increased scan time, although this plateaued at roughly 6 min.

Impact: For the first time, we show that DSC-MRI using breath-holding allows for the quantification of perfusion parameters. This may have broad implications for neurovascular disease, either circumventing the need for invasive gadolinium injections or shedding additional light into pathology.

Introduction

Cerebral perfusion imaging, which allows clinicians to investigate diseases characterized by vascular abnormality, is often performed using dynamic susceptibility contrast (DSC) MRI.^1,2 Here, traditionally, a bolus of paramagnetic gadolinium (Gd) is injected and tracked as it passes through the cerebral vasculature, revealing the underlying perfusion properties of the tissue.² However, Gd-DSC has limitations—it is invasive, expensive, and exhibits toxicity in certain patient populations.^3,4,5 Like Gd, deoxyhemoglobin (dOHb) is paramagnetic, but it is an endogenous substance whose concentration can be adjusted via either a gas-control system or voluntary changes in respiration, such as breath-holding.^6,7,8 There is no study that has developed and/or applied a DSC analysis pipeline to obtain perfusion information from signal changes induced by breath-holding.

Methods

We acquired T₂*-weighted gradient-echo 2D-EPI (GRE-EPI) MRI data from the same ten healthy subjects (age: 30.4±9.4 years, 3 female) at both 3T and 7T, who performed eight breath-holds (16 s each separated by 44 s of rest). The GRE-EPI data had 2.0 mm isotropic spatial resolution (64 interleaved axial slices; GRAPPA = 2; Ref lines PE = 46; SMS = 2; FoVread = 192 mm; phase-encoding = P-A; ɑ = 70°; TR/TE = 2000/30 ms; bandwidth = 2312 Hz/px; echo-spacing = 0.53 ms; EPI factor = 94; 260 TRs, TA = 8:53 min). First, we temporally averaged the eight breath-hold-induced signal boluses and converted signal to relaxation (∆R₂*). We then selected an arterial input function (AIF) from the middle cerebral artery and scaled this by the venous output function (VOF) integral from the superior sagittal sinus.⁹ Finally, we used a standard DSC MRI framework to quantify perfusion.¹⁰

Results

Whereas the tissue time-courses were characterized by a negative relaxation change (Figure 1), we found that the AIF during a breath-hold was characterized by a positive relaxation change, likely representing vasodilation, as supported by both simulations and the literature (Figure 2).¹¹ Breath-hold DSC (bhDSC)-calculated CBV (CBV_3T = 6.0 mL/100g; CBV_7T = 5.2 mL/100g) and CBF (CBF_3T = 59.3 mL/100g/min; CBF_7T = 44.3 mL/100g/min) were generally within the physiological range of values reported in the literature using Gd-DSC MRI (Figure 3).^12,13,14 At 7T, the breath-hold task yielded significantly higher contrast-to-noise (CNR) (p = 0.0012) and GM-to-WM contrast (p = 0.00075) relative to 3T. Although CNR increased with scan time, we found CNR gains roughly plateaued following approximately 6 min of scan time (after averaging 6 breath-holds) at both 3T and 7T (Figure 4).

Discussion

Unlike gas-control-system-induced hypoxia, a breath-hold primarily results in hypercapnia.⁷ Through vasodilation, hypercapnia results in tissue hyperoxia, and thus, signal gain in correspondence with ΔCO₂.⁷ The initially counter-intuitive finding of AIF relaxation change being anti-correlated with the negative relaxation change in tissue is supported by the fact that breath-hold-induced vasodilation, particularly at 7T, leads to an increase in intravascular space and a reduction in CSF, resulting in signal loss in voxels containing substantial CSF and ΔCBV (Figure 2C). Although generally within the literature range, calculated CBV and CBF differed between field-strengths—this is not unexpected given the many known DSC quantification dependencies.¹⁵In this case, while signal change in tissue scales approximately linearly as field strength increases, this is not the case in voxels containing large intravascular contributions (i.e., VOF)—this results in an observed CBV and CBF decrease as field strength increases from 3T to 7T.

Conclusion

For the first time, we used breath-hold-induced T₂*-MRI signal changes to quantify perfusion parameters using a DSC framework, at both 3T and 7T. We found that CNR and GM-to-WM contrast increased with increasing field strength and found perfusion values to be within the physiological range of expected values. We expect that the methods and results presented in this study can help pave the way toward contrast-free perfusion imaging, both clinically and in research.

Acknowledgements

We would like to thank Boohee Choi and Suji Jeong at the Institute for Basic Science for helping us acquire the data.

References

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Figures

Figure 1. Breath-Hold-Induced Relaxation Rate Time Course Dynamics. Time courses were averaged across subjects for gray matter (GM), white matter (WM), vein (VOF), and artery (AIF) at 3T and 7T.

Figure 2. The AIF. A. ∆R₂* AUC maps were calculated subject-wise, transformed to MNI152 2 mm anatomical space, and averaged at both 3T and 7T. Pink arrows indicate an example of the AIF’s location. AUC maps are overlaid onto the anatomical template. B. The associated AIF ∆R₂* time courses are shown at 3T and 7T, with subject-wise time courses in gray and subject-averaged time courses in pink. C. Illustration of vasodilation mechanism which results in arterial signal loss during breath-holding.

Figure 3. bhDSC Perfusion Quantification. A. CBV (mL/100g) and CBF (mL/100g/min) maps were calculated subject-wise, transformed to MNI152 2 mm anatomical space, and averaged at both 3T and 7T. B. Box and whisker plots show the calculated CBV and CBF (for GM and WM) at 3T and 7T.

Figure 4. Breath-hold CNR Properties. Top. CNR maps were transformed to MNI152 2 mm anatomical space and averaged across subjects for both 3T and 7T. For illustration purposes, CNR maps are shown when there is no bolus averaging or 2, 4, 6, or 8 boluses averaged at 7T. Bottom. Plot of subject-averaged CNR_GM at 3T (dashed) and 7T (solid) as a function of the number of breath-holds averaged (error bars represent the standard error).

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)

4862

DOI: https://doi.org/10.58530/2024/4862