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Objective <p>In the kidney, the medulla is most susceptible to damage in case of hampered perfusion or oxygenation. Due to separate regulation of cortical and medullary perfusion, measurement of both is crucial to improve the understanding of renal pathophysiology. We aim to develop and evaluate a physiologically accurate model to measure renal inner medullary (F<sub>med</sub>) and cortical perfusion (F<sub>cor</sub>) separately.</p> Materials and methods <p>We developed a 7-compartment model of renal perfusion and used an iterated approach to fit 10 free parameters. Model stability and accuracy were tested on both patient data and simulations. Cortical perfusion and F<sub>T</sub> (tubular flow or glomerular filtration rate per unit of tissue volume) were compared to a conventional 2-compartment filtration model.</p> Results <p>Average (standard deviation) F<sub>med</sub> was 37(23)mL/100&#xa0;mL/min. Fitting stability as expressed by the median (interquartile range) coefficient of variation between fits was 0.0(0.0–5.8)%, with outliers up to 81%. In simulations, F<sub>med</sub> was underestimated by around 8%. Intra-class correlation coefficients for F<sub>cor</sub> and F<sub>T</sub> as measured with the 2- and 7- compartment model were 0.87 and 0.63, respectively.</p> Discussion <p>We developed a pharmacokinetic model closely following renal physiology. Although the results were vulnerable for overfitting, relatively stable results could be obtained even for F<sub>med</sub>.</p>

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A method to measure renal inner medullary perfusion using MR renography

  • 期刊论文

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  • 发表日期：

    2025年2月15日

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  • A. de Boer,
  • K. Sharma,
  • B. Alhummiany,
  • S. P. Sourbron

摘要

Objective

In the kidney, the medulla is most susceptible to damage in case of hampered perfusion or oxygenation. Due to separate regulation of cortical and medullary perfusion, measurement of both is crucial to improve the understanding of renal pathophysiology. We aim to develop and evaluate a physiologically accurate model to measure renal inner medullary (Fmed) and cortical perfusion (Fcor) separately.

Materials and methods

We developed a 7-compartment model of renal perfusion and used an iterated approach to fit 10 free parameters. Model stability and accuracy were tested on both patient data and simulations. Cortical perfusion and FT (tubular flow or glomerular filtration rate per unit of tissue volume) were compared to a conventional 2-compartment filtration model.

Results

Average (standard deviation) Fmed was 37(23)mL/100 mL/min. Fitting stability as expressed by the median (interquartile range) coefficient of variation between fits was 0.0(0.0–5.8)%, with outliers up to 81%. In simulations, Fmed was underestimated by around 8%. Intra-class correlation coefficients for Fcor and FT as measured with the 2- and 7- compartment model were 0.87 and 0.63, respectively.

Discussion

We developed a pharmacokinetic model closely following renal physiology. Although the results were vulnerable for overfitting, relatively stable results could be obtained even for Fmed.