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.

Increased blood–brain barrier permeability to water in the aging brain detected using noninvasive multi‐TE ASL MRI

“…Mean ASL images were generated by a pairwise subtraction of the control and labeled images. The processing pipeline as seen in Figure 1 was used to determine cortical (11) ΔM = 2M 0 CBF exp −TI × R1 app exp (min (TI, + ) ΔR) − exp ( ΔR) ΔR (12) T w = − a F I G U R E 1 Processing pipeline to measure the exchange time (T w ) as an index of blood-brain barrier permeability to water using control data to determine extravascular tissue T 2 (T2 EV ), multiple-echo-time (multi-TE) arterial spin-labeling (ASL) data to determine intravascular/ extravascular ASL signal intensities (ΔM IV /ΔM EV ), intravascular T 2 (T2 IV ) and tissue transit time (δ), and multi-TI (inflow time) ASL data to determine arterial transit time (δ a ). Dotted lines indicate parameters with results for the individual animals and solid lines indicate parameters with result that is shared across the group.…”

Imaging Mechanisms of Disease Progression in Multiple Sclerosis: Beyond Brain Atrophy

“…It should be noted that GBCA permeability reflects a fundamentally different aspect of BBB function (passive diffusion through intercellular pathways) compared to water permeability, which likely includes both passive (transcellular dominating intercellular) and active transport pathways 76 . Indeed, recent work has demonstrated that BBB water permeability measures can be obtained using noncontrast MRI techniques 77‐79 . Active water flux across the capillary endothelium is facilitated by transmembrane transporters and may provide high‐spatial resolution information on regional metabolic activity 75 …”

Blood–brain barrier permeability measurement by biexponentially modeling whole‐brain arterial spin labeling data with multiple T2‐weightings

“…Moreover, there is no need for further measurements with signalcrushing or diffusion-weighting gradients. 35 Common ASL quantities, such as ATT and CBF, can still be calculated from the acquired data. The main advantage of the concept is the fit stabilization based on external T 2,IV and T 2,EV estimates.…”