Quantitative dynamic contrast-enhanced magnetic resonance imaging for renal perfusion measurement in autosomal dominant polycystic kidney disease

Introduction

Autosomal dominant polycystic kidney disease (ADPKD) is a progressive, inherited disorder and a leading cause of end-stage renal disease. It is characterized by the gradual accumulation of renal cysts, leading to increased total kidney volume (TKV), loss of renal function, and, ultimately, kidney failure (1). Height-adjusted TKV (htTKV), an Food and Drug Administration (FDA)-approved imaging biomarker, has been widely used to predict renal outcomes in ADPKD (2,3). However, despite its clinical utility, htTKV exhibits only a moderate correlation with long-term decline in glomerular filtration rate (GFR). Besides, htTKV accounted for only 35% and 22% of individual GFR decline in the CRISP and HALT-PKD cohorts, respectively (4,5), which suggests that htTKV captures only a subset of the complex pathological mechanisms driving kidney function loss in ADPKD. Furthermore, the htTKV trajectory often fluctuates and may require several years of follow-up to detect meaningful change, limiting its responsiveness as a short-term surrogate endpoint. Therefore, reliable, quantitative biomarkers are urgently needed to monitor early disease progression, guide individualized therapy, and optimize patient selection for clinical trials.

Alternative imaging biomarkers have been proposed, such as renal texture analysis, but these are highly sensitive to scanner-specific parameters, image quality, and post-processing techniques (4). Texture metrics are also not directly linked to underlying pathophysiological processes, raising concerns about their biological relevance. Emerging magnetic resonance imaging (MRI) techniques, such as blood oxygen level-dependent (BOLD) imaging, magnetization transfer imaging (MTI), intravoxel incoherent motion (IVIM), and chemical exchange saturation transfer (CEST), offer insights into renal physiology (6-9). Nonetheless, each method has its own limitations related to sensitivity, reproducibility, or physiological specificity, and none currently provides a fully validated quantitative biomarker for monitoring disease progression in ADPKD.

Given these limitations, intrarenal perfusion has emerged as a promising, biologically meaningful biomarker of ADPKD progression. Patients with ADPKD exhibit both endothelial and vascular smooth-muscle dysfunction, which contributes to impaired renal blood flow (RBF), a known independent predictor of renal outcomes in ADPKD (10,11). Notably, RBF improves existing multivariable models for GFR slope prediction, even when adjusted for htTKV and PKD genotype. Contrast-enhanced ultrasound (CEUS) has been proposed for measuring RBF; however, its relatively narrow field of view compared with CT or MRI and its dependence on operator expertise present limitations (12,13). Moreover, obtaining absolute RBF values is challenging, and CEUS is typically limited to semi-quantitative assessment (14). Dynamic CT can measure RBF (15), but concerns regarding radiation exposure and the use of nephrotoxic iodinated contrast agents remain. Arterial spin labelling (ASL) MRI enables non-contrast, quantitative assessment of kidney perfusion but is limited by low signal-to-noise ratio, motion sensitivity, and variable reproducibility (16).

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a noninvasive means of quantifying renal perfusion by tracking the kinetics of contrast agent uptake (17). This technique provides quantitative pharmacokinetic (PK) parameters such as the volume transfer constant (K^trans), which reflects capillary-level tissue perfusion (18-21). When small (<1 kDa), extracellular gadolinium-based contrast agents are used, K^trans provides a reliable estimation of perfusion. Moreover, macrocyclic gadolinium agents are considered safe even in patients with advanced chronic kidney disease, including those with stage 4 or 5 disease (22). In a study of over 6,000 patients receiving up to triple doses of gadoteridol, no serious adverse events were observed (23).

However, a major challenge in the broader application of quantitative DCE-MRI is the variability in measurements at different times due to hardware instability (24-26) and across scanners due to differences in vendor-specific hardware, pulse sequences, and image reconstruction pipelines (27). External calibration phantoms containing known concentrations of contrast agents have been proposed to detect and correct scanner-specific errors. Yet, static phantoms do not accurately model the dynamic signal changes produced by contrast agent movement through living tissue (28,29).

We developed a point-of-care portable perfusion phantom (P4) to detect and correct scanner-specific bias in quantitative DCE-MRI measurements (30,31). The P4 phantom can be scanned alongside the patient without modification to standard imaging protocols, enabling real-time quality assurance. In a recent multicenter study, P4-based correction improved data agreement [i.e., intraclass correlation coefficient (ICC)] across three MRI scanners for K^trans measurements, increasing it from 0.38 to 0.99 (31). The P4 device is low-cost, user-friendly, and compatible with any commercial MRI system, making it well-suited for multi-institutional studies.

This study aimed to verify that intrarenal K^trans measurements obtained using quantitative DCE-MRI are highly reproducible when P4-based error correction is applied, and that the corrected K^trans serves as a reliable biomarker for assessing disease severity and progression in patients with ADPKD. Additionally, other PK parameters were evaluated and compared to K^trans in terms of their diagnostic accuracy for ADPKD prognosis. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1764/rc).

Methods

Human subjects

The protocols comply with current regulatory requirements for studies involving human subjects and are approved by the IRB of the University of Alabama at Birmingham (IRB-300011141). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All participants were fully informed about the experimental nature of the study and provided written informed consent before the scans. Five healthy subjects were recruited from October 2020 to November 2020, and twenty ADPKD patients were recruited from March 2024 to March 2025 at the University of Alabama at Birmingham (UAB). Table 1 summarizes the clinical profiles of all participants. The healthy subject cohort consisted of five individuals [all females; 3 White, 2 Black; aged 23–41 years; estimated glomerular filtration rate (eGFR) >90 mL/min/1.73 m²]. The patient cohort consisted of twenty individuals (4 males, 16 females; 15 White, 4 Black, 1 Asian; aged 20–46 years), 10 with mild ADPKD (eGFR ≥60 mL/min/1.73 m² and htTKV ≤750 mL/m) and 10 with severe ADPKD (eGFR <60 mL/min/1.73 m² or htTKV >750 mL/m). Mayo Imaging Classification (MIC), a method for assessing ADPKD severity, incorporates age as a factor (1). As a result, one patient in the mild ADPKD group was classified as 1D. In healthy subjects, three imaging sessions were conducted on three different 3T MRI scanners (GE Signa, Chicago, USA; Siemens Prisma, Erlangen, Germany; and Philips Ingenia, Best, The Netherlands) within a week to assess the reproducibility of PK parameter measurements across scanners. For patients, two imaging sessions were conducted on a 3T GE Signa MRI scanner, spaced 4.2±2.5 days apart (range, 1–9 days). However, the second DCE-MRI scan for a patient with mild ADPKD (subject 11 in Table 1) was unsuccessful due to improper catheter placement. In addition, the first DCE-MRI scan for a patient with severe ADPKD (subject 16 in Table 1) was terminated at the subject’s request because of the cold sensation from the contrast injection. Participants fasted for at least 12 hours before imaging to minimize the effects of salt intake on RBF (32) and to reduce abdominal peristaltic motion artifacts in MRI. Also, the participants were refrained from consuming caffeine and alcohol for 24 hours before imaging, as these substances can affect kidney perfusion.

MRI parameters

Participants were positioned on the phantom package containing three P4s with a torso-phased array coil placed around the abdomen as demonstrated in a previous study (31). For healthy subjects, the MRI parameters and protocols are the same as described in a previous study (31). For kidney and cyst volume measurement of patient cohort, T2-weighted (T2W) images were acquired with single shot fast spin echo sequence (SSFSE) in the coronal plane with the following parameters: field of view =44–48 cm, matrix size =512×512, slice thickness =4–5 mm, number of slices =40–57, repetition time (TR) =605–944 ms, echo time (TE) =70–90 ms, and number of averages =0.6–0.7. Additionally, AIR Recon DL (GE Healthcare, Waukesha, WI, USA) was employed for denoising of T2W images (33). Prior to DCE-MRI, T1-weighted (T1W) images were obtained using a fast spoiled gradient echo sequence (FSPGR) with four flip angles (FAs) (2°, 5°, and 10°) for T1 mapping (34). The imaging protocol included a field of view of 40×40 cm, with phase and frequency encoding set to 160 and 192, respectively. A total of 12 slices were acquired using a matrix of 256×256, with a slice thickness of 5 mm and no interslice gap. The acquisition was performed with a TR of 3.9 ms and a TE of 2.1 ms. The scan utilized a single average and a SENSE acceleration factor of 2, achieving a temporal resolution of 2.9 seconds. T1-weighted images were acquired over a 30-second free-breathing period, during which images captured in the expiratory phase were automatically identified and averaged for each FA. DCE-MRI was also performed in free-breathing mode using the same imaging sequence and parameters as for T1W imaging, with a FA of 15°. DCE-MRI was performed over approximately 9 minutes. Intravenous injection of gadoteridol (0.1 mmol/kg), a macrocyclic contrast agent approved by the FDA, was initiated 30 seconds after the start of imaging, followed by a 20-mL saline flush delivered at a steady rate of 2 mL/s. For phantom imaging, each of the three P4 phantoms received a 4-mL injection of 100 mM gadoteridol at a rate of 0.24 mL/s, beginning 15 seconds after image acquisition commenced, using a syringe pump (NE-1600).

Image processing

The automatic segmentation of kidneys and cysts was performed using a lab-trained nnUNet model (35), trained on 604 3D volumes from the CRISP dataset, tested on our study subjects, and compared against semi-automatically generated ground truth. Training incorporated extensive data augmentation, including random rotations, scaling, intensity shifts, and elastic deformations, to improve robustness to anatomical variability and imaging artifacts. The model was optimized using a combination of weighted Dice and Jaccard losses over 1,000 epochs with a learning rate schedule. The kidney and cyst regions in ten patients with mild (n=7) and severe (n=3) ADPKD were segmented using a semi-automatic method described in a previous study (35), which served as the ground truth for evaluating the segmentation accuracy of the nnUNet model. Model performance was assessed using the Dice similarity coefficient (DSC). The processing of DCE-MRI data to generate PK maps involved seven sequential steps. First, images were spatially aligned using a B-spline registration method based on the expiratory phase (36). Second, local FA variations were quantified via B1 mapping. Third, T1 relaxation maps were generated using the variable flip angle (VFA) method (34). Fourth, a look-up table was constructed to relate the reference contrast enhancement coefficients (CECs) of P4 phantoms to those measured by MRI (30). Fifth, maps of contrast agent concentration (CAC) in tissues were computed (37), with FA inhomogeneity corrected using the B1 maps from step two. Sixth, CAC maps were adjusted using the established look-up table from step four. Lastly, intrarenal PK parameter maps were derived using a population-based arterial input function (38) to standardize outputs and reduce variability. All image processing steps were performed using a lab-made software package developed in LabVIEW v17.0, with the SSM-based PK mapping module implemented in MATLAB v2020a.

PK parameter mapping

Eleven PK maps were generated using three modeling approaches: the extended Tofts model (ETM), which provided K^trans, k_ep, v_e, and v_p (39); the Tofts model (TM), which yielded K^trans, k_ep, and v_e (40); and the Shutter Speed Model (SSM), which produced K^trans, k_ep, v_e, and τ_i (41). The detailed equations for each model are provided in a previous publication (27). In these models, K^trans represents the volume transfer constant, k_ep is the flux rate constant, v_e denotes the extravascular extracellular volume fraction, v_p is the fractional plasma volume, and τ_i is the mean intracellular water lifetime.

Statistical analysis

The reproducibility (or repeatability) of PK parameter measurement was assessed using the within-subject coefficient of variation (wCV) or ICC. Comparisons of intrarenal PK parameters between mild and severe ADPKD groups were performed using analysis of variance (ANOVA). Correlations between clinical indicators (e.g., htTKV, total cyst volume (TCV), and eGFR) and intrarenal PK metrics were evaluated using Pearson correlation coefficient. The sensitivity, specificity, and overall accuracy of each PK parameter in differentiating mild ADPKD from severe ADPKD were assessed using receiver operating characteristic (ROC) curve analysis (42). Optimal cutoff values were determined using the Youden index (43). All data in this manuscript are presented as mean ± standard deviation (SD). Statistical significance was defined as a P value of less than 0.05. Statistical analyses were performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA).

Results

AI-generated segmentations exhibited excellent agreement with the semi-automatically derived ground truth, yielding DSCs of 91% for kidney regions and 98% for cyst regions (Figure 1). The reproducibility (wCV) of renal K^trans measurement across three scanners was 50–80%, while the repeatability measured on a single scanner was 20–25% before P4-based error correction (Table 2). However, after applying P4-based error correction, reproducibility and repeatability improved to comparable levels. The P4-based error correction enhanced the repeatability of ETM-derived K^trans measurements by approximately threefold, while the repeatability of v_e and v_p remained largely unchanged across all PK models. Figure 2A shows representative ETM-based K^trans maps of a patient with ADPKD, acquired on days 0 and 2, illustrating the visual impact of P4-based correction. Figure 2B shows the scatter plot of ETM-based K^trans values from two repeated measurements (Scan 1 and Scan 2) in ADPKD patients, demonstrating improved agreement after P4-based correction. TM-derived K^trans repeatability improved twofold following correction, with minimal changes observed for v_e and k_ep. In contrast, SSM-derived repeatability metrics for K^trans and k_ep showed no meaningful improvement, and v_e and τ_i remained stable pre- and post-correction.

Figure 1 AI-based kidney and cyst segmentation. T2-weighted MRI images of two representative patients with mild and severe ADPKD, while the kidney and cyst regions were determined using the nnUNet model. ADPKD, autosomal dominant polycystic kidney disease; AI, artificial intelligence; MRI, magnetic resonance imaging.

Figure 2 P4-based error correction improved the repeatability of renal DCE-MRI metrics in ADPKD patients. (A) ETM-based K^trans maps from a representative patient with ADPKD acquired on days 0 and 2, shown before (top row) and after (bottom row) P4-based error correction. The renal region is outlined with a dotted contour in each panel. A uniform color scale (0.0–0.5 min⁻¹) was applied across all maps for consistent comparison. (B,C) Scatter plots of ETM-based K^trans values from two repeated measurements (Scan 1 and Scan 2) in ADPKD patients, demonstrating improved agreement after P4-based correction. ICCs represent the measurement consistency between the two sessions. K^trans, volume transfer constant. ADPKD, autosomal dominant polycystic kidney disease; DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; ETM, extended Tofts model; ICC, intraclass correlation coefficient.

Following P4-based error correction, K^trans and v_e values were significantly higher in the mild ADPKD group compared with the severe group, regardless of the PK model used (Table 3). The v_p and τ_i values also remained significantly higher in the mild group. However, k_ep derived from the TM and ETM models showed no significant difference between groups after correction. The renal K^trans values of healthy subjects were approximately 25–60% higher than those of patients with mild ADPKD, although the difference did not reach statistical significance (P>0.05). In Figure 3, the representative ETM-based PK maps (K^trans, v_e, and v_p) from a healthy subject and two patients with mild and severe ADPKD illustrate these differences.

Figure 3 PK parameter maps in healthy and ADPKD kidneys. The ETM derived K^trans, v_e, and v_p maps are shown for a representative healthy subject (top row) and two representative patients with mild (middle row) and severe (bottom row) ADPKD. A uniform color scale was applied across all subjects for each PK parameter to enable direct visual comparison. K^trans, volume transfer constant; v_e, extravascular extracellular space; v_p, fractional plasma volume. ADPKD, autosomal dominant polycystic kidney disease; ETM, extended Tofts model; PK, pharmacokinetic.

ETM-based K^trans achieved the highest accuracy (95%) in distinguishing between mild and severe ADPKD following error correction, compared with only 50% before correction (Table 4). Accuracy for v_e improved from 70–80% to 85–90% across all PK models after correction. TM-based and SSM-based K^trans accuracy increased from 60% and 55% to 90% and 70%, respectively, whereas k_ep accuracy remained unchanged. Accuracy for v_p rose from 50% to 70%, and for τ_i from 60% to 75% after correction.

Significant correlations between PK parameters (K^trans, v_e, v_p, and τ_i) and htTKV, TCV, and eGFR were observed across all PK models, even before correction (Table 5). These correlations were further strengthened for K^trans, v_p, and τ_i after correction. The most notable improvements were observed for ETM-based K^trans correlations with htTKV (r=−0.68, P=0.001 before vs. r=−0.79, P<0.001 after), TCV (r=−0.69, P=0.001 before vs. r=−0.77, P<0.001 after), and eGFR (r=0.64, P=0.002 before vs. r=0.69, P<0.001 after). Of note, the ETM-based K^trans after P4-based error correction was significantly correlated with htTKV in healthy subjects and mild ADPKD patients (n=15; r=−0.56, P=0.029) and severe ADPKD patients (n=10; r=−0.64, P=0.048).

Discussion

Based on the PK models, K^trans measurements from quantitative DCE-MRI scans distinguished between mild and severe ADPKD and were strongly correlated with htTKV, TCV, and eGFR in both groups. As the disease progresses, microcysts form within the renal parenchyma, which are often undetectable by conventional MRI due to its limited spatial resolution. These microcysts reduce v_e and concurrently diminish the vascular fraction (v_p), which delivers oxygen and nutrients. This reduction in vascular support results in decreased tissue perfusion, as reflected by lower K^trans values. Notably, because both v_e and K^trans decrease in tandem, the flux rate constant, k_ep=K^trans/v_e, remains relatively stable throughout disease progression. A similar trend was observed between healthy subjects and mild ADPKD patients, although the difference did not reach statistical significance with the current small sample size. These findings suggest that the proposed method may be useful for monitoring early disease progression at the individual level.

Mild ADPKD patients (eGFR ≥60 mL/min/1.73 m² and htTKV ≤750 mL/m) exhibited a shorter intracellular water lifetime (τ_i) compared to severe ADPKD patients (eGFR <60 mL/min/1.73 m² or htTKV >750 mL/m). Recent investigations have identified τᵢ as a surrogate marker of cellular metabolic activity, showing an inverse association with Na⁺-K⁺-ATPase (NKA) pump activity. NKA, driven by phosphate derived from ATP hydrolysis, is essential for maintaining both intracellular and extracellular ion gradients, particularly those of potassium and sodium (44-46). Our results support the notion that as ADPKD severity and microcyst burden increase, the amount of functional renal parenchyma decreases, thereby lowering ATP consumption. This reduction likely diminishes NKA activity, thereby prolonging τᵢ.

Our findings also indicate that implementing the P4 method significantly enhanced the repeatability of Ktrans measurements by up to 3-fold, improving differentiation between mild and severe ADPKD. In contrast, the repeatability of k_ep and v_e remained lower than that of K^trans, even prior to applying the P4-based correction, which aligns with previous observations (47,48). This discrepancy is attributable to the physiological nature of these parameters: k_ep, representing the rate constant for the return of contrast agent from the extracellular space to plasma, is largely governed by the washout phase, during which contrast concentration changes are relatively limited. In comparison, K^trans reflects the extravasation rate during the uptake phase, where broader fluctuations in contrast concentration occur, particularly in high-field MRI systems, leading to increased susceptibility to quantification errors. By correcting these fluctuations, the P4 approach effectively reduced variability and improved the reliability of K^trans measurements. The limited repeatability of v_p was mainly attributed to its inherently low signal-to-noise ratio.

Notably, the K^trans values derived from the SSM were approximately twice as high as those calculated using the TM or ETM after applying P4-based error correction. This discrepancy arises because TM and ETM assume instantaneous water exchange between the intracellular and extracellular compartments, whereas the SSM explicitly accounts for finite transmembrane water-exchange kinetics (49). As a result, TM and ETM tend to underestimate perfusion-related parameters, particularly in renal parenchyma with microcysts. Therefore, SSM-derived K^trans may more accurately reflect renal perfusion in this patient population.

In this study, although the reproducibility of renal PK parameter measurements in healthy subjects was evaluated across three MRI scanners, the data from ADPKD patients were acquired on a single scanner. Consequently, while the results are promising, they should be interpreted with caution and validated in larger, multicenter studies. Expanding the investigation to include diverse clinical settings would enhance the generalizability of the findings and help identify potential challenges in implementing the P4-phantom protocol across various MRI platforms and institutional workflows.

Conclusions

In conclusion, this study highlights the potential of quantitative DCE-MRI, enhanced by P4-based error correction, to accurately characterize the severity of ADPKD by detecting the changes in renal perfusion caused by microstructural and functional alterations in the renal parenchyma as cysts develop and grow in patients with ADPKD. The P4 method substantially improved the repeatability of PK parameter measurements in the TM and ETM, although its impact was less pronounced for parameters derived from the SSM. Future studies over a longer timeframe, including larger patient cohorts across multiple institutions and different scanners, may further support the use of K^trans in tandem with htTKV for ADPKD risk stratification.

Acknowledgments

The authors thank Ms. Quenteeria Mooney and Ms. Deja Cunningham for patient recruitment, and Ms. Nicole Haynes and Mr. Nicholas Hatfield for image acquisition.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1764/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1764/dss

Funding: This study was supported by a PKD Foundation research grant (No. 1022615) and the National Cancer Institute (No. UG3 CA232820). M.M. was also in part supported by the U.S. Department of Veterans Affairs (No. 1-I01-BX006266-01), National Institute of Diabetes and Digestive and Kidney Diseases (No. U54-DK126087), and the Detraz Endowed Research Fund in Polycystic Kidney Disease.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1764/coif). M.M. reports research support from Otsuka, Sanofi, Vertex, AbbVie, and has been a member of advisory panels/boards for Sanofi, Santa Barbara Nutrients, and PKD Foundation, and has provided consultancy for Otsuka, Sanofi, Vertex, AbbVie, and Regulus. H.K. reports funding from the PKD Foundation and patents on the P4-based error correction strategy (US 10,578,702 B2 &US Provisional Application No. 63/394836). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All experiments comply with current regulatory requirements, and are approved by the IRB of the University of Alabama at Birmingham (IRB-300011141), and abide by the laws of the United States of America. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All participants were fully informed about the experimental nature of the study and provided written informed consent before the scans.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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