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Updated: Jul 9, 2026

Multilevel Microdissection and Functional-Structural Profiling of Human Renal Arterial Branches
Published on: September 5, 2025
MRI-measurement of perfusion and glomerular filtration in the human kidney with a separable compartment model
Steven P Sourbron1, Henrik J Michaely, Maximilian F Reiser
1Institute of Clinical Radiology, Ludwig-Maximilians-Universität München, Munich, Germany. steven.sourbron@med.uni-muenchen.de
Objectives:
Recent animal studies with dynamic contrast enhanced magnetic resonance imaging have demonstrated that a separable compartment model provides more accurate assessments of glomerular filtration than the Patlak model. In this study, the feasibility of the separable compartment model for the measurement of perfusion and filtration in healthy humans is investigated.
Methods:
Dynamic contrast enhanced magnetic resonance imaging was performed in 15 healthy volunteers. Contrast enhancement curves were analyzed with the separable compartment model on whole kidney regions, cortex regions, and the pixel level. The region of interest values for the kinetic parameters were compared with those obtained from the Patlak model and from a model-free deconvolution analysis.
Results:
The separable compartment model provides a good fit to the data over the entire dynamic range. All values of filtration (30 +/- 7.2 and 20 +/- 11 mL/100 mL/Min for kidney and cortex, respectively) are significantly higher than those of the Patlak model (24 +/- 6.4 and 15 +/- 11 mL/100 mL/Min). Values produced by the Patlak model have a higher variability. Whole kidney values of perfusion (229 +/- 57 mL/100 mL/Min) are significantly higher than those of a deconvolution analysis (210 +/- 50 mL/100 mL/Min).
Conclusions:
The separable compartment model is feasible for application in humans and sufficiently robust for a pixel analysis. Increased filtration values compared with the Patlak model suggest that the difference in accuracy observed in animal studies is relevant in humans. Increased perfusion values suggest that the separable compartment model corrects for known underestimations in the deconvolution analysis.
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