Blood-brain barrier water exchange measurements using FEXI: Impact of modeling paradigm and relaxation time effects.

ABSTRACT

PURPOSE: To evaluate potential modeling paradigms and the impact of relaxation time effects on human blood-brain barrier (BBB) water exchange measurements using FEXI (BBB-FEXI), and to quantify the accuracy, precision, and repeatability of BBB-FEXI exchange rate estimates at 3 T $$ \mathrm{T} $$ . METHODS: Three modeling paradigms were evaluated (i) the apparent exchange rate (AXR) model; (ii) a two-compartment model ( 2 CM $$ 2\mathrm{CM} $$ ) explicitly representing intra- and extravascular signal components, and (iii) a two-compartment model additionally accounting for finite compartmental T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ relaxation times ( 2 CM r $$ 2{\mathrm{CM}}_r $$ ). Each model had three free parameters. Simulations quantified biases introduced by the assumption of infinite relaxation times in the AXR and 2 CM $$ 2\mathrm{CM} $$ models, as well as the accuracy and precision of all three models. The scan-rescan repeatability of all paradigms was quantified for the first time in vivo in 10 healthy volunteers (age range 23-52 years; five female). RESULTS: The assumption of infinite relaxation times yielded exchange rate errors in simulations up to 42%/14% in the AXR/ 2 CM $$ 2\mathrm{CM} $$ models, respectively. Accuracy was highest in the compartmental models; precision was best in the AXR model. Scan-rescan repeatability in vivo was good for all models, with negligible bias and repeatability coefficients in grey matter of RC AXR = 0 . 43 $$ {\mathrm{RC}}_{\mathrm{AXR}}=0.43 $$ s - 1 $$ {\mathrm{s}}^{-1} $$ , RC 2 CM = 0 . 51 $$ {\mathrm{RC}}_{2\mathrm{CM}}=0.51 $$ s - 1 $$ {\mathrm{s}}^{-1} $$ , and RC 2 CM r = 0 . 61 $$ {\mathrm{RC}}_{2{\mathrm{CM}}_r}=0.61 $$ s - 1 $$ {\mathrm{s}}^{-1} $$ . CONCLUSION: Compartmental modelling of BBB-FEXI signals can provide accurate and repeatable measurements of BBB water exchange; however, relaxation time and partial volume effects may cause model-dependent biases.