Hindered diffusion of MRI contrast agents in rat brain extracellular micro‐environment assessed by acquisition of dynamic T₁ and T₂ maps

Abstract

The knowledge of brain tissues characteristics (such as extracellular space and tortuosity) represents valuable information for the design of optimal MR probes for specific biomarkers targeting. This work proposes a methodology based on dynamic acquisition of relaxation time maps to quantify in vivo MRI contrast agent concentration after intra-cerebral injection in rat brain. It was applied to estimate the hindered diffusion in brain tissues of five contrast agents with different hydrodynamic diameters (Dotarem(®) ≈ 1 nm, P846 ≈ 4 nm, P792 ≈ 7 nm, P904 ≈ 22 nm and Gd-based emulsion ≈ 170 nm). In vivo apparent diffusion coefficients were compared with those estimated in an obstacle-free medium to determine brain extracellular space and tortuosity. At a 2 h imaging timescale, all contrast agents except the Gd-based emulsion exhibited significant diffusion through brain tissues, with characteristic times compatible with MR molecular imaging (<70 min to diffuse between two capillaries). In conclusion, our experiments indicate that MRI contrast agents with sizes up to 22 nm can be used to perform molecular imaging on intra-cerebral biomarkers. Our quantification methodology allows a precise estimation of apparent diffusion coefficients, which is helpful to calibrate optimal timing between contrast agent injection and MRI observation for molecular imaging studies.

Diffusion properties of conventional and calcium‐sensitive MRI contrast agents in the rat cerebral cortex

“…Table ), the free diffusion coefficient, D free was determined in phantom measurements performed at 20 °C. The median value ± standard deviation obtained was 5.18 ± 1.18 × 10 −4 mm 2 s −1 , as illustrated in Figs S4 and S5 (Supporting Information), and is in agreement with the literature . From this value and the Stokes–Einstein relationship, the hydrodynamic diameter of gadoteric acid was calculated: 0.83 ± 0.16 nm, and by taking into account the relationship between the dependence on diffusion with temperature (see Methods) the expected D free in vivo at 37 °C becomes 7.96 ± 1.82 × 10 −4 mm 2 s −1 .…”

“…Marty et al . proposed to follow the spatial distribution pattern of an intracerebrally injected T 1 relaxing agent for about 2 h. The effective diffusion coefficient ( D *) was determined from the rate of increase of the spatial width of the Gaussian distribution of the agent in the tissue. This method was used to determine the tortuosity factor that links free diffusion to the hindered diffusion found in brain tissue.…”

“…In addition, we also have to observe the agents for longer measurement times (hours with MRI as opposed to a few minutes measurement time using optical methods) in order to achieve a satisfactory dynamic range and to observe the complete diffusion time course. Marty et al (13) proposed to follow the spatial distribution pattern of an intracerebrally injected T 1 relaxing agent for about 2 h. The effective diffusion coefficient (D*) was determined from the rate of increase of the spatial width of the Gaussian distribution of the agent in the tissue. This method was used to determine the tortuosity factor that links free diffusion to the hindered diffusion found in brain tissue.…”

Metabolite and macromolecule T1 and T2 relaxation times in the rat brain in vivo at 17.2T

“…Water T 1 maps were obtained using a three‐dimensional (3D) inversion recovery (IR) fast gradient echo (FGE) sequence (TE/TR 1 /TR 2 = 3.02/10/10,000 ms, 6 segments, flip angle = 5°, resolution: 125 × 125 × 1000 µm 3 , matrix size: 256 × 96 × 14) with 60 inversion times (90 to 9530 ms) using a nonadiabatic hermit excitation pulse (1 ms, 5.4 kHz bandwidth) and an adiabatic full‐passage (AFP) HS8 inversion pulse (hyperbolic secant modulation, n = 8, 4 ms, 5 kHz bandwidth) . The T 1 maps were generated by fitting the IR‐FGE data as a function of the TI using a 3‐parameter model implemented in‐house in C/Matlab (MathWorks, Natick, MA).…”

An Efficient Computational Approach to Characterize DSC-MRI Signals Arising from Three-Dimensional Heterogeneous Tissue Structures

“…This can be achieved by updating CA concentration for each voxel at each simulation time step using a CA diffusion transition matrix ( A CA ) calculated using appropriate CA diffusion coefficients and permeability across membranes from literature [50], [51]. For the purposes of this study, we assumed that the contrast agent equilibrates within each compartment over the time it takes to acquire each DSC-MRI image (1–2 seconds).…”