Improved accuracy of human cerebral blood perfusion measurements using arterial spin labeling: accounting for capillary water permeability

Abstract

A two-compartment exchange model for perfusion quantification using arterial spin labeling (ASL) is presented, which corrects for the assumption that the capillary wall has infinite permeability to water. The model incorporates an extravascular and a blood compartment with the permeability surface area product (PS) of the capillary wall characterizing the passage of water between the compartments. The new model predicts that labeled spins spend longer in the blood compartment before exchange. This makes an accurate blood T(1) measurement crucial for perfusion quantification; conversely, the tissue T(1) measurement is less important and may be unnecessary for pulsed ASL experiments. The model gives up to 62% reduction in perfusion estimate for human imaging at 1.5T compared to the single compartment model. For typical human perfusion rates at 1.5T it can be assumed that the venous outflow signal is negligible. This simplifies the solution, introducing only one more parameter than the single compartment model, PS/v(bw), where v(bw) is the fractional blood water volume per unit volume of tissue. The simplified model produces an improved fit to continuous ASL data collected at varying delay time. The fitting yields reasonable values for perfusion and PS/v(bw).