Quantitative Estimation of Renal Function with Dynamic Contrast-Enhanced MRI Using a Modified Two-Compartment Model

Abstract

ObjectiveTo establish a simple two-compartment model for glomerular filtration rate (GFR) and renal plasma flow (RPF) estimations by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Materials and MethodsA total of eight New Zealand white rabbits were included in DCE-MRI. The two-compartment model was modified with the impulse residue function in this study. First, the reliability of GFR measurement of the proposed model was compared with other published models in Monte Carlo simulation at different noise levels. Then, functional parameters were estimated in six healthy rabbits to test the feasibility of the new model. Moreover, in order to investigate its validity of GFR estimation, two rabbits underwent acute ischemia surgical procedure in unilateral kidney before DCE-MRI, and pixel-wise measurements were implemented to detect the cortical GFR alterations between normal and abnormal kidneys.ResultsThe lowest variability of GFR and RPF measurements were found in the proposed model in the comparison. Mean GFR was 3.03±1.1 ml/min and mean RPF was 2.64±0.5 ml/g/min in normal animals, which were in good agreement with the published values. Moreover, large GFR decline was found in dysfunction kidneys comparing to the contralateral control group.ConclusionResults in our study demonstrate that measurement of renal kinetic parameters based on the proposed model is feasible and it has the ability to discriminate GFR changes in healthy and diseased kidneys.

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Correlation of quantitative dynamic contrast‐enhanced MRI with microvascular density in necrotic, partial necrotic, and viable liver tumors in a rabbit model

“…The AIF was manually measured by visual inspection of concentration time curves near the heart in blood vessels ${\mathrm{normalC}}_b$ through the hematocrit for each rabbit: $C_{p}false(tfalse)=\frac{C_b(t)}{(1–H_{ct})}$ where the $H_{\text{italicct}}$ is the hematocrit, estimated in rabbits to be 0.45 (17) . For estimation of ${\mathrm{normalK}}^{\text{trans}},{\mathrm{normalv}}_{\mathrm{normalp}},{\mathrm{normalv}}_{\mathrm{normale}}$, and ${\mathrm{normalt}}_0$, nonlinear least squares fitting was implemented using the lsqnonlin function from the MATLAB Optimization Toolbox (MathWorks Inc., Natick, MA).…”

“…where the H ct is the hematocrit, estimated in rabbits to be 0.45. (17) For estimation of K trans , v p , v e , and t 0 , nonlinear least squares fitting was implemented using the lsqnonlin function from the MATLAB Optimization Toolbox (MathWorks Inc., Natick, MA).…”