Synopsis

Information on renal microvascular architecture is still hardly accessible by clinical MRI techniques. This study explores the feasibility of using spin- and gradient-echo (SAGE) based dynamic susceptibility-contrast MRI for the quantification of renal microvascular diameter/density. Microvascular diameter (VSI) map, Q map and vessel density (D) map were calculated based on the differential dependence of R2/R2* relaxation rates on the capillary-sized vascular structures, which may provide a potentially important MR biomarker for the detection and staging of renal cell carcinoma

Target Audience

Introduction

Purpose

Materials and Methods

Animal Preparation

Eight New Zealand white rabbits (weight range: 2.5-3.5 kg) were included in this study. All the experiments were performed in accordance with local Institutional Animal Care and Use Committee protocols.

MR Imaging

MRI was performed on a 3.0 Tesla MRI scanner (Achieva, Philips Medical Systems, Best, Netherlands). VSI data were acquired using a SAGE-EPI pulse sequence shown in Fig. 1. A five-echo SAGE acquisition was implemented with TE_1-5 = 12, 34, 56, 78 and 100 ms, and with echo-train duration of 18 ms. The last echo train acquired at TE₅ = TE_SE coincided with the SE formation. Detailed imaging
parameters were: TR = 2.0 s, FOV = 120 × 120 mm², Voxel Size = 1.6 × 1.6 mm², slice thickness = 5.0 mm, dynamic phase = 100. Fat suppression was applied in the SAGE-EPI acquisition. After 20 s of baseline scan, 0.1 mmol/kg gadopentetate dimeglumine (Gd-DTPA) was injected followed by a 10-ml saline flush during the dynamic scans.

Image Analysis

The SAGE-derived R2 and R2* time-courses were obtained using nonlinear least squares fits as described previously⁶. The baseline signals were averaged to obtain the pre-bolus signal. ΔR2 and ΔR2* were calculated as the difference between the relaxation rates during contrast agent passage and the mean pre-contrast relaxation rate. Parametric maps were generated using a home-built MATLAB program. Q map and density (D) map were calculated pixelwisely according to Eq. [1] and Eq. [2].

$$Q=\triangle R2/(\triangle R2*)^\frac{2}{3}$$ [1]

$$N=Q^3/(D\cdot(1.678k)^3)$$ [2]

where k is dependent on the distribution of microvascular radii, D is the diffusion coefficient. The vessel diameter (VSI) was determined by fitting a linear dependence between ΔR2^3/2 and ΔR2* during the bolus passage according to Eq. [3].

$$VSI=0.867(\zeta D)^\frac{1}{2}\cdot\triangle R2*/\triangle R2^\frac{3}{2}$$ [3]

where ζ is the blood volume fraction.

Results

Fig. 2 shows an example of baseline T2w image and contrast-enhanced ΔR2/ΔR2* maps following Gd-DTPA injection. Fig. 3 shows the transient variations in the relaxation rates (ΔR2/ΔR2*) in healthy renal cortex and medulla. Note that the dependence between ΔR2^3/2 and ΔR2* is slightly different during the increase and decrease of the contrast agent concentration in tissue. This can be understood as a manifestation of the changing involvement of the arterial and venous pools in the signal dephasing. The estimated renal VSI, Q and vessel density maps were shown in Fig. 4.

Pixel-wise measures of microvascular diameter were found to vary between 3.38 μm and 5.02 μm within the cortex, and between 2.34 μm and 5.73 μm within medulla. The microvascular densities were found to vary between 899.59 mm^-2 and 1504.27 mm^-2 within cortex, while between 647.02 mm^-2 and 1976.69 mm^-2 within medulla. The average microvascular diameter was significantly smaller in cortex and outer medulla than that in the inner medulla (both with P < 0.01), while the microvascular density and Q values were significantly higher in cortex and outer medulla than that in the inner medulla (both with P < 0.01). But no statistical differences of microvascular diameter and vessel density were found between cortex and outer medulla (Fig. 5).

Conclusion

Acknowledgements

References

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