Synopsis

Intracranial vascular compliance (IVC) is an important factor in regulating the cerebral perfusion pressure and is believed to be linked to multiple neurological disorders. In this study, a retrospectively-gated multi-PLD pCASL technique was used to estimate arterial cerebral blood volume (aCBV) and compliance. Our results showed that this technique can quantify cardiac-induced variations of aCBV as well as IVC distribution maps.

Introduction

Methods

Theory: The arterial and tissue compartments of the pCASL signal in each voxel ($$$S_a$$$ and $$$S_t$$$, respectively) can be expressed using the general kinetic model^6,7 as follows:

$$ S_a(t)=2αM_{0b}.aCBV.(c(t)*(r_a (t).m_a (t))) \tag{1}$$

$$aCVB=f.τ_2 \tag{2}$$

$$ c(t)=exp(-τ_1/T_{1b} ), τ_1<t<τ_1+w \tag{3}$$

$$ S_t(t)=(1/τ_2) (S_a (t)*(r_t (t).m_t (t))) \tag{4}$$

where $$$α$$$ is the labeling efficiency, $$$M_{0b}$$$ is the equilibrium magnetization of the blood, $$$f$$$ is the cerebral blood flow, $$$c(t)$$$ is the normalized arterial input function, $$$T_{1b}$$$ is the T1 relaxation of blood in arteries, $$$w$$$ is the tagging duration, and $$$τ_1$$$ and $$$τ_2$$$ are arterial and tissue transit times, respectively. The residue and magnetization relaxation functions are defined as:

$$r_a(t)=exp(-t/τ_2 ) \tag{5}$$

$$m_a (t)=exp(-t/T_{1b} ) \tag{6}$$

$$r_t (t)=exp(-f(t-τ_2)/λ), t>τ_2 \tag{7}$$

$$m_t(t)=exp(-(t-τ_2)/T_{1t} ), t>τ_2 \tag{8}$$

with $$$T_{1t}$$$ and $$$λ$$$ being the T1 relaxation of tissue and blood-brain partition coefficient, respectively.

Experiments: Four healthy volunteers (two female, age=25±3) were scanned on a Siemens Prisma 3T scanner using pCASL with background suppressed single-shot 3D GRASE sequence. The imaging parameters were: voxel size=2.5×2.5×7.25mm3, TE/TR=36.4/4000ms, 10 slices, tagging duration=1.5s, 60-72 image volumes per scan. Scans with six post-labeling delays (PLDs) were acquired in succession (PLD = 0, 400, 800, 1200, 1600, 1900 ms). Peripheral pulse signal was recorded using a pulse oximeter during scan and was used to retrospectively sort control and labeled images into 5 to 6 cardiac bins for each PLD. Arterial region of interest (ROI) was created by thresholding the ASL subtraction images averaged over all cardiac phases at PLD=0.

In order to demonstrate the impact of blood volume on ASL signal across cardiac cycle, a pilot hypercapnia scan (5% CO2 for 4 minutes) was carried out on one subject after a control scan using retrospectively-gated pCASL sequence (TR= 1.7s, TD =1.0s, PLD=0, 4 minutes per each scan).

Data analysis: Voxel-wise estimation of aCBV was carried out at each cardiac phase by fitting the multi-PLD data to equations (1) to (8). Unknown parameters were $$$f$$$, $$$τ_1$$$, and $$$τ_2$$$. IVC was calculated using temporal peaks of aCBV as $$$IVC=(aCBV_{max}-aCBV_{min})/(∆p.aCBV_{min})$$$ where $$$Δp$$$ denotes the pulse pressure measured using an MR-compatible cuff sphygmomanometer. aCBV and IVC values were spatially averaged on each axial slice.

Results and Discussion

Pulsatile modulations of the ASL signal averaged in 3D ROI were similar to those of the blood velocity measured in the left middle cerebral artery (LMCA) using phase-contrast MRI (Figure 1a), indicating the prominent impact of cardiac pulsations on the arterial ASL signal. The change in the arterial ASL signal observed in this study was notably larger compared to the previously reported cardiac-induced alterations in ASL perfusion signal⁸. This could reflect the function of the arterial tree in damping out the blood pressure fluctuations prior to tissue perfusion.

Similarly, averaged aCBV exhibited fluctuations consistent with those of the blood velocity (Figure 1c). The phase mismatch between aCBV and velocity time-course could be caused by the voxel-wise variations in arrival times ($$$τ_1$$$, and $$$τ_2$$$) on each axial slice.

While the peak-to-peak ASL signal was reduced by ~38% in response to the hypercapnia challenge, the temporal mean of ASL fluctuations was increased by ~13% (Figure 2). This trend reflects the vasodilation of cerebral arteries in response to hypercapnia, which can reduce cardiac-induced aCBV variation.

Spatially averaged IVC estimated in the current study, agreed well with previously reported values⁴. Voxel-wise aCBV and IVC maps demonstrated larger compliance values within the circle of Willis and its major branches compared to distal vessels (Figure 1d and 1e). Also, IVC values were reduced with the distance from the most caudal axial plane (Figure 3). The existence of larger arteries in areas with observed larger IVC indicates the impact of vessel size on vascular compliance as suggested in previous studies^4,5.

Conclusion

Acknowledgements

References

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