Mapping water exchange across the blood-brain barrier using 3D diffusion-prepared arterial spin labeled perfusion MRI

Abstract

Purpose: To present a novel MR pulse sequence and modeling algorithm to quantify the water exchange rate (kw ) across the blood-brain barrier (BBB) without contrast, and to evaluate its clinical utility in a cohort of elderly subjects at risk of cerebral small vessel disease (SVD).

Methods: A diffusion preparation module with spoiling of non-Carr-Purcell-Meiboom-Gill signals was integrated with pseudo-continuous arterial spin labeling (pCASL) and 3D gradient and spin echo (GRASE) readout. The tissue/capillary fraction of the arterial spin labeling (ASL) signal was separated by appropriate diffusion weighting (b = 50 s/mm2 ). kw was quantified using a single-pass approximation (SPA) model with total generalized variation (TGV) regularization. Nineteen elderly subjects were recruited and underwent 2 MRIs to evaluate the reproducibility of the proposed technique. Correlation analysis was performed between kw and vascular risk factors, Clinical Dementia Rating (CDR) scale, neurocognitive assessments, and white matter hyperintensity (WMH).

Results: The capillary/tissue fraction of ASL signal can be reliably differentiated with the diffusion weighting of b = 50 s/mm2 , given ~100-fold difference between the (pseudo-)diffusion coefficients of the 2 compartments. Good reproducibility of kw measurements (intraclass correlation coefficient = 0.75) was achieved. Average kw was 105.0 ± 20.6, 109.6 ± 18.9, and 94.1 ± 19.6 min-1 for whole brain, gray and white matter. kw was increased by 28.2%/19.5% in subjects with diabetes/hypercholesterolemia. Significant correlations between kw and vascular risk factors, CDR, executive/memory function, and the Fazekas scale of WMH were observed.

Conclusion: A diffusion prepared 3D GRASE pCASL sequence with TGV regularized SPA modeling was proposed to measure BBB water permeability noninvasively with good reproducibility. kw may serve as an imaging marker of cerebral SVD and associated cognitive impairment.

Keywords: arterial spin labeling (ASL); blood-brain barrier (BBB); diffusion; gradient and spin echo (GRASE); perfusion; small vessel disease (SVD); water permeability.

© 2018 International Society for Magnetic Resonance in Medicine.

Figures

Figure 1.

(a) Sequence diagram of 3D DP-pCASL. (b) Diffusion preparation module: Non-selective pulses were used to compensate for field inhomogeneity, timing of gradients was optimized to minimize eddy current. De-phasing gradient was added along y-axis (4π dephasing per voxel) before tip-up to eliminate phase sensitivity of GRASE readout. Strong spoiler along three axes were added after tip-up to remove residual transverse magnetization. A pair of re-phasing and de-phasing gradients were added at both sides of EPI readout. (c) GRASE readout: Non-selective excitation was used to improve slab profile, re-phasing and rewound de-phasing gradients were added at two sides of EPI readout to maintain MG condition.

Figure 2.

Perfusion map with six diffusion weightings acquired at PLD=1500ms, 1800ms and 2100 ms, respectively. Gray scale indicates relative perfusion signal intensity compared to average perfusion signal acquired with b = 0 s/mm2 at PLD = 1500 ms.

Figure 3.

Average perfusion signals from four subjects with six diffusion weightings acquired at PLD=1500ms, 1800ms and 2100ms. Error bar indicates the standard deviation of kw measurements across four subjects. Bi-exponential fitting results are shown in the upper right corner. Capillary/tissue fraction were 24%/76% when PLD = 1500 ms, 15%/85% when PLD = 1800 ms and 11%/89% when PLD = 2100 ms, respectively.

Figure 4.

Comparison of direct modeling with Gaussian smoothing (first row) and regularized SPA modeling (second row). (a) Perfusion map without diffusion weighting acquired at PLD=900ms. (b) Perfusion map with b=14 s/mm2 (VENC=7.5cm/s to suppress vascular signal) acquired at PLD = 900 ms. (c) ATT map. (d) Perfusion map without diffusion weighting acquired at PLD = 1800 ms. (e) Perfusion map with b=50 s/mm2 acquired at PLD = 1800ms. (f) kw map. Red arrows indicate the local regions with noise induced spuriously high kw values using direct modeling (first row). kw map from regularized SPA modeling was relatively smooth (second row).

Figure 5.

kw map of six slices from one representative subject’s test and retest scans.

Figure 6.

(a) Average kw values from test-retest experiments using the proposed 3D DP-pCASL sequence. Horizontal and vertical axis indicates the kw measurements from the first and second MRI scan, respectively. (b) Average global CBF values from test-retest experiments. Horizontal and vertical axis indicates the CBF measurements from the first and second MRI scan, respectively.

Figure 7.

(a-b): Bar plot of average kw in normal subjects versus subjects with diabetes (a) and hypercholesterolemia (b). (c) Bar plot of average kw versus vascular risk factors. (d) Bar plot of average kw versus Fazekas scale. (e-f): Bar plot of average kw versus clinical dementia rating scales CDR-SB (e) and CDR-GS (f). (g-j): Scatter plots of average kw versus NIH toolbox measurements: Flanker (g), DCCS (h), PSMTa (i) and PSMTb (j). Error bars in bar plot indicate standard deviation of kw across subjects. Mixed effects regression coefficients β and P values are listed in bar/scatter plots. Slopes and R2 of linear regressions (without controlling age/gender, indicated by the black dashed lines) are listed in each scatter plot.