Comparison of ferumoxytol-based cerebral blood volume estimates using quantitative R1 and R2* relaxometry

Abstract

Purpose: Cerebral perfusion is commonly assessed clinically with dynamic susceptibility contrast MRI using a bolus injection of gadolinium-based contrast agents, resulting in semi-quantitative values of cerebral blood volume (CBV). Steady-state imaging with ferumoxytol allows estimation of CBV with the potential for higher precision and accuracy. Prior CBV studies have focused on the signal disrupting T2* effects, but ferumoxytol also has high signal-enhancing T₁ relaxivity. The purpose of this study was to investigate and compare CBV estimation using T₁ and T2*, with the goal of understanding the contrast mechanisms and quantitative differences.

Methods: Changes in R₁ (1/T₁ ) and R2* (1/ T2*) were measured after the administration of ferumoxytol using high-resolution quantitative approaches. Images were acquired at 3.0T and R₁ was estimated from an ultrashort echo time variable flip angle approach, while R2* was estimated from a multiple gradient echo sequence. Twenty healthy volunteers were imaged at two doses. CBV was derived and compared from relaxometry in gray and white matter using different approaches.

Results: R₁ measurements showed a linear dependence of blood R₁ with respect to dose in large vessels, in contrast to the nonlinear dose-dependence of blood R2* estimates. In the brain parenchyma, R2* showed linear dose-dependency whereas R₁ showed nonlinearity. CBV calculations based on R2* changes in tissue and ferumoxytol blood concentration estimates based on R₁ relaxivity showed the lowest variability in our cohort.

Conclusions: CBV measurements were successfully derived using a combined approach of R₁ and R2* relaxometry. Magn Reson Med 79:3072-3081, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Keywords: R2*; R1; cerebral blood volume; ferumoxytol; gray matter; white matter.

Figure 1

Representative R2*, R1, ΔR2*, and ΔR1 maps from a single slice of the volume from one subject acquired with the UTE-VFA T1 and multi-echo T2* imaging techniques at different doses of ferumoxytol (0 mg FE/kg, 1 mg FE/kg, 5 mg FE/kg). R2* based imaging provides more sensitivity to signal; however, vessels are better depicted in R1 maps. R1 maps also demonstrate lower spatial distortions compared to R2* images. Some regional differences in ΔR1 and ΔR2* are observed, in this case in the frontal lobe gray and white matter, likely arising due to through plane blooming in R2* from vessels in adjacent slices.

Figure 2

Box plots displaying a.) the R1 and b.) R2* changes observed in all subjects (n=20) in gray matter and white matter. The R1 estimates show a weaker correlation with dose-response at high concentrations of ferumoxytol, with a coefficient of determination R² = 0.27 in WM and 0.71 in GM. While the R2* exhibits a stronger dose response in tissue having a coefficient of determination R² = 0.91 for both GM and WM.

Figure 3

Box plots showing the a.) R1 and b.) R2* changes observed in ROI’s in a blood vessel (superior sagittal sinus) across all subjects (n=20). In large vessels, R1 in blood exhibits a linear response to dose of ferumoxytol with R² = 0.96 for the linear fit. While, R2* exhibits a non-linear response to dose, in contrast with a linear relationship between dose and R2* found in tissue. A quadratic model fit the data best with an adjusted coefficient of determination R²_adj = 0.86. The superior sagittal sinus was selected to perform blood measurements in order to decrease the variance and possible sources of error. These sources of error include flow induced errors, B1 differences, B0 inhomogeneities and partial volume effects.

Figure 4

Pixel wise relationship between ΔR2* and ΔR1 shown (a, d) for 1mgFE/kg and (b, d) 5mgFE/kg dose of ferumoxytol. The median is shown overlaid on 25–75% quartiles. The pixel wise correlation (a, b) shows a wide range of variation in ΔR1 where ΔR2* does not change. It should be noted that there are fewer voxels with these values, as partially indicated by the wider quartile range. When plotting ΔR2* vs ΔR1, ΔR2* (c, d) tends to exhibit changes where the ΔR1 does not change, suggesting the vessel size is overestimated in R2* images. Similarly, at high ΔR1, ΔR2* is weakly dependent on ΔR1. Many of these voxels are those containing small and medium sized vessels, where the dose response is non-linear and less sensitive to those in the capillary bed. These effects obfuscate the correlation of R2* and R1 outside of the homogenous tissues such as gray and white matter.

Figure 5

Box plots showing different methods for CBV estimates in GM and WM across all subjects (n = 20). The CBV values were higher in both GM and WM when exploiting both: R2* linearity of ferumoxtyol in tissue using a steady state approach and blood R1 to estimate ferumoxytol concentration in blood (from left to right 1^st box-blue). Similar but lower CBV estimates were found in GM and WM when using Nadler’s equations to estimate ferumoxytol concentration in blood (2^nd box-black). The CBV estimation from fitting 3 parameters into a two-compartment water exchange limited relaxation model were lowest in magnitude and higher in data spread for both GM and WM (3^rd box-red). To investigate the spread of the data CBV was estimated from a 2 parameters fit assuming a value of ${\tau }_b=$ 0.233 ± 0.012 s (4^th box-gray). Although the CBV estimates from the 2 parameter fit are forced by the R2* CBV estimates, the reduction in the spread of the data is real, revealing the PS product uncertainties will increase the spread of the CBV estimates.