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

Abstract

Blood–brain barrier (BBB) permeability assessment remains of ongoing interest in clinical practice and research. Transitions between intravascular (IV) and extravascular (EV) gray matter (GM) compartments may provide information regarding the microstructural status of the BBB. Due to different transverse relaxation times (T2) of water protons in vessels and GM, it is possible to determine the compartment in which these protons are located. This work presents and investigates the feasibility of a simplified analytical approach for compartmentalizing the proportions of magnetically marked water protons into IV and EV GM components by biexponentially modeling T2‐weighted arterial spin labeling (ASL) data. Numerous model assumptions were used to stabilize the fit and achieve in vivo applicability. Particularly, transverse relaxation times of IV and EV water protons were determined from the analysis of two supporting T2‐weighted ASL measurements, utilizing a monoexponential signal model. This stabilized a two‐parameter biexponential fit of ASL data with T2 preparation (PLD = 0.9/1.2/1.5/1.8 s, TET2Prep = 0/30/40/60/80/120/160 ms), which thereby robustly provided estimates of the IV and EV compartment fractions. Experiments were conducted with three healthy volunteers in a 3 T scanner. Averaged over all subjects, the labeled water protons inherit T2,IV = 200 ± 18 ms initially and adapt T2,EV = 91 ± 2 ms with a longer retention time in cerebral structures. Accordingly, the EVlocated ASL signal fraction rises with increasing PLD from 0.31 ± 0.11 at the shortest PLD of 0.9 s to 0.73 ± 0.02 at the longest PLD of 1.8s. These results indicate a transition of the water protons from IV to EV space. The findings support the potential of biexponential modeling for compartmentalizing ASL spin fractions between IV and EV space. The novel integration of monoexponential parameter estimates stabilizes the two‐compartment model fit, suggesting that this technique is suitable for robustly estimating the BBB permeability in vivo.

Separating spin compartments in arterial spin labeling using delays alternating with nutation for tailored excitation (DANTE) pulse: A validation study using T2‐relaxometry and application to arterial cerebral blood volume imaging

“…However, T 2_noVS at the short PLD was systematically smaller than the reported values. 19,21,23 This is attributed to the difference in signal characteristics for vessel size sensitivity between gradient-echo-and spinecho-based sequences. 46 While spins in larger vessels are imaged by gradient-echo-based sequences, spin-echo sequences can only image spins in small vessels.…”

Current Concepts in Intracranial Interstitial Fluid Transport and the Glymphatic System: Part II—Imaging Techniques and Clinical Applications

“…Therefore, the hallmark features of AD, possibly the result of impaired glymphatic clearance, may occur later in the process than previously thought. Although the dynamic contrast-enhanced imaging method using paramagnetic contrast material is most developed for BBB integrity assessment at present, hybrid diffusion-weighted imaging-arterial spin labeling MRI and heavily T2-weighted FLAIR postcontrast imaging sequences are also being developed for this purpose, with the latter suggesting increased BBB leakage with age and worsening level of cognitive impairment (Fig 5C , 5D) (80)(81)(82)(83). These techniques are expected to be important for understanding neurodegenerative proteinopathies in relation to glymphatic function and BBB integrity.…”

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

“…Clinical experiments to determine the BBB permeability of compounds are accurate; however, they are time-consuming and labor-intensive ( Bickel, 2005 ; Massey et al, 2020 ; Schidlowski et al, 2020 ). Additionally, it is difficult to perform clinical experiments with diverse types of drug candidates ( Main et al, 2018 ; Mi et al, 2020 ).…”