Streamlined calculation of kidney function using dynamic contrast-enhanced MRI with population-based arterial input function and a whole-kidney model

Abstract Objectives Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can assess kidney function, but artifacts and complex post-processing limit its use. We calculated estimated glomerular filtration rate (eGFR) and renal plasma flow (RPF) by combining a population-based arterial input function (pAIF) with a whole-kidney pharmacokinetic model (WKPM). We also compared DCE-MRI eGFR and RPF with serum eGFR and arterial spin labeling (ASL) derived RPF, respectively. Materials and methods In a prospective single-center study, 43 patients (30 M/13 F, 59.0 ± 11.8 y) with renal masses underwent multiparametric 1.5-T MRI, before and 3 months after nephrectomy ( n = 15), including coronal, fat-saturated volumetric DCE-MRI (5-s temporal resolution) and background-suppressed pseudocontinuous ASL. DCE-MRI eGFR and RPF were measured by WKPM, incorporating individual-based arterial input function (iAIF) and population-based arterial input function (pAIF) as inputs. Pearson correlation, Bland-Altman analysis, and Mann-Whitney U statistics were used. Results Serum eGFR (mean 67.55 mL/min/1.73 m²) and DCE-MRI (mean eGFR pAIF 59.49, iAIF 63.60 mL/min/1.73 m²) were measured in 51 MRIs: correlation with serum eGFR was stronger for pAIF ( r = 0.61, p < 0.001) than iAIF ( r = 0.33, p = 0.018), with comparable Bland-Altman bias (-11.9% and -9.1%, respectively). RPF was measured by both DCE-MRI and ASL in 21 MRIs: mean RPF was 229.3 (ASL), 229.7 (pAIF), and 390.4 (iAIF) mL/min ( p = 0.018). Correlation of pAIF RPF with ASL-derived RPF ( r = 0.65, p < 0.001) was stronger than for iAIF RPF (r = 0.53, p = 0.014), with lower Bland-Altman bias (pAIF -1.0% versus iAIF 39.5%). Conclusion DCE-MRI using pAIF and WKPM provides simplified, robust single-kidney function estimates. Relevance statement This study proposes a simplified DCE-MRI post-processing method using a population-based arterial input function combined with a whole-kidney pharmacokinetic model. It avoids complex corticomedullary segmentation and minimizes aortic region-of-interest variability, and enables clinically feasible estimation of single-kidney function, supporting broader adoption of renal DCE-MRI in clinical practice. Key Points Population-based arterial input function reduces inter-observer variability and sensitivity to aortic region-of-interest placement artifacts. Whole-kidney modeling avoids complex segmentation of the cortex and medulla regions. DCE-MRI using population-AIF and whole-kidney modeling yields eGFR and RPF significantly correlated with serum and ASL references. Streamlined post-processing workflow supports broader routine clinical use of DCE-MRI. Graphical Abstract