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TOMOGRAPHY, September 2017, Volume 3, Issue 3:146-152
DOI: 10.18383/j.tom.2017.00010

Can Hyperpolarized 13C-Urea be Used to Assess Glomerular Filtration Rate? A Retrospective Study

Christian Østergaard Mariager, Per Mose Nielsen, Haiyun Qi, Marie Schroeder, Lotte Bonde Bertelsen, Christoffer Laustsen

Department of Clinical Medicine, MR Research Centre, Aarhus University, Aarhus, Denmark

Abstract

Small airways disease (SAD) is one of the leading causes of airflow limitations in patients diagnosed with chronic obstructive pulmonary disease (COPD). Parametric response mapping (PRM) of computed tomography (CT) scans allows for the quantification of this previously invisible COPD component. Although PRM is being investigated as a diagnostic tool for COPD, variability in the longitudinal measurements of SAD by PRM has been reported. Here, we show a method for correcting longitudinal PRM data because of nonpathological variations in serial CT scans. In this study, serial whole-lung high-resolution CT scans over a 30-day interval were obtained from 90 subjects with and without COPD accrued as part of SPIROMICS. It was assumed in all subjects that the COPD did not progress between examinations. CT scans were acquired at inspiration and expiration, spatially aligned to a single geometric frame, and analyzed using PRM. By modeling variability in longitudinal CT scans, our method could identify, at the voxel-level, shifts in PRM classification over the 30-day interval. In the absence of any correction, PRM generated serial percent volumes of functional SAD with differences as high as 15%. Applying the correction strategy significantly mitigated this effect with differences ∼1%. At the voxel-level, significant differences were found between baseline PRM classifications and the follow-up map computed with and without correction (P < .01 over GOLD). This strategy of accounting for nonpathological sources of variability in longitudinal PRM may improve the quantification of COPD phenotypes transitioning with disease progression.

Introduction

Glomerular filtration rate (GFR) measures are essential to the daily care of patients, as either an estimate or an exact quantifiable measure (1). GFR is often estimated by the serum creatinine levels or creatinine clearance, derived from both blood and urine samples. Creatinine estimation is a relative insensitive marker of GFR owing to the GFR-dependent tubular secretion of creatinine (2). Inulin clearance is considered to be the most reproducible, quantitative index of renal function, as it not reabsorbed and thus transported freely to the urine. However, the specificity is lacking in both methods, as the total GFR can overshadow alterations in single kidney function or even in intrarenal differences (1).

Nuclear medicine-based techniques remain the reference method for quantification of the single-kidney GFR (skGFR) (1); widespread application of these, however, has been limited by the ionizing radiation associated with the examination. Several magnetic resonance (MR)-based methods have emerged as alternative methods to quantify skGFR. Contrast-based methods, such as dynamic contrast-enhanced (DCE) MR, have been used to generate GFR analytical models in both experimental disease and in humans (36). Although the methods in general show great promise, the clinical translation is lacking. This may be largely because of the lack of general consensus on model standardization, a direct consequence of the complex system in question and the obtainable signal-to-noise ratio in MR.

Recently, an alternative method for high-signal, contrast-enhanced MR has been introduced. By means of hyperpolarization of tracers containing an MR-active nucleus, the MR signal available can be enhanced by 4 orders of magnitude. In this technique, the hyperpolarized tracer itself is the origin of the signal, thereby overcoming some of the challenges associated with traditional MR contrast agents. The novel technique of hyperpolarized MR has shown applicability in a broad range of biological applications including cancer, cardiovascular, brain, liver and kidney research (79), with the primary goal to interrogate organ-specific metabolic substrate selection associated with various disease states (10, 11). The technique enables the important monitoring of treatment response, allowing tailored treatment planning of the individual renal patient without ionizing radiation (1214). An increasing body of experimental evidence supports the translation of hyperpolarized MR into the clinic as a new modality for assessing renal disease (15, 12, 1618).

In addition to metabolic imaging, several artificial or endogenous tracers have been developed for angiographic and perfusion imaging (19, 18, 5, 2022). A tracer of particular interest is 13C-urea and [13C,15N2]urea, which is an essential osmolite associated with renal function (20). [13C,15N2]urea possesses particular optimal properties for hyperpolarization, as the 15N reduces the relaxation loss and increases the T2 at low magnetic fields (23). Urea is vital for the kidneys' ability to concentrate urine, thereby preventing loss of water and essential nutrients (24). Urine concentration is directly determined by GFR, and thus, the intrarenal dependency of urea distribution in conjunction with renal function has previously been investigated in rodents and in porcine models with hyperpolarized 13C-urea. This enables assessments of perfusion, osmolality gradients, and relaxation alterations under various functional and disease conditions (20, 23, 2529).

Here we combine a simple, model-free analysis of renal hemodynamics and a simple, nonarterial input function (arterial input function [AIF]) GFR model, the so-called Baumann–Rudin (BR) model, on data describing the kinetics of hyperpolarized [13C,15N2]urea handling in the rodent kidney. This indirect model of GFR assumes 2 distinct compartments—cortex and medulla; the cortex and medulla predominately contain blood and urine, respectively (Figure 1). The model assumes a unidirectional transport of contrast from the cortical space to the medullary/pelvic region. Our aim in performing this retrospective data analysis was to determine if hyperpolarized 13C-urea could be used to estimate GFR in the rodent kidney.

Figure 1.

Illustration of the Baumann–Rudin (BR) model. Transport between the cortex to the medulla can be estimated to be a linear relationship (mass conservation). Actual line profiles of a diseased (ischemia-reperfusion [I/R]) and a healthy contralateral (CL) kidney, showing the transport from the cortical space (2 cortical peaks) to the medullary space (center peak).

media/vol3/issue3/images/tom0031700970001.jpg

Methodology

The data presented here were derived through retrospective analysis of hyperpolarized [13C,15N]urea imaging data acquired previously from the kidneys of control rats (n = 5), early diabetes rats (n = 6) (26), and ischemia-reperfusion (I/R) rats 24 hours after reperfusion (n = 6) (30). Originally, the data were analyzed to determine renal perfusion (Figure 2); here, new analytical tools were applied to extract a putative GFR based on hyperpolarized MR data (our calculated GFR will be referred to here as hGFR). To verify the findings in the previously acquired data, 9 additional animals were examined. The additional examinations include accurate hyperpolarized [13C,15N]-urea T1 relaxation estimation using a single pulsed global NMR experiment (n = 4) and a gold-standard inulin clearance GFR estimation accompanying a single-kidney DCE magnetic resonance imaging (MRI) GFR estimation with the BR model and the model-free perfusion model (31).

Figure 2.

Anatomical T2-weighted scan overlaid with a 13C-urea image (single timepoint of 23 seconds after the start of injection) and a few selected time points illustrating the line profiles and the temporal dependencies on the distribution pattern of the hyperpolarized urea (red, left I/R kidney, blue, right contralateral kidney). Interestingly, a second peak is seen at late time points (lower line profile plot).

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Animal Handling

Experimental details have been described previously in the original perfusion imaging publications (26, 30). To summarize, [13C,15N]urea imaging was performed on similar conditioned female Wistar rats (220 g) 2 weeks after streptozotocin treatment (55 mg/kg) to induce a prediabetic nephropathy model (early signs of renal dysfunction, increased oxygen consumption). In similar conditioned female Wistar rats (220 g), [13C,15N]urea imaging was performed 24 hours after reperfusion following severe I/R injury in the left kidney (60 minutes of ischemia) (30). Additional similar conditioned 9 female Wistar rats (220 g) were anesthetized with inactin (120 mg/kg subcutaneously) for evaluation of 13C global T1, DCE skGFR, and inulin clearance.

Fluorescein Isothiocyanate-Inulin Clearance

GFR was determined using an intravenous bolus injection of fluorescein isothiocyanate (FITC)-inulin. A solution of 1.5% FITC-inulin was prepared and dialyzed (membrane molecular weight cutoff: 1000). Before injection, the FITC-inulin solution was filtered through a 0.22-μm syringe filter for sterilization. Animals were given an injection of 2 μL/kg. Further, 100 μL blood samples were collected at 1, 3, 5, 10, 15, 35, 55, and 75 minutes. Hereafter the collected plasma fractions were isolated. During the experiments, FITC-inulin were protected from light and kept on ice. Samples were diluted in 1:10 in a HEPES buffer (pH 7.4) and measured in duplicate on a 384-well plate. The original FITC-inulin solution was diluted in 1:100. Analyses were performed on a PHERAstar FS micro plate reader (Em/Ex 485 nm/520 nm; BMG Labtech, Birkerød, Denmark). FITC-inulin clearance was analyzed with a noncompartmental pharmacokinetic model (3234).

Imaging

In both studies, a 2D fully balanced steady-state sequence with (repetition time/echo time/field of view/spectral width/matrix/ section thickness of 4.8 milliseconds/2.4 milliseconds/60 × 60 mm2/20 kHz/32 × 32/10 mm), separated by 3 seconds was used to allow perfusion assessment of the renal hemodynamics (20). The experiments were performed on a 9.4 T (Agilent, Palo Alto, California) horizontal preclinical MRI system, equipped with a 1H/13C Litz coil (Doty Scientific, Columbia, South Carolina) for transmission and reception. 1H DCE-MRI was performed with similar experimental setup, with a standard gradient spoiled echo sequence with fat suppression (repetition time/echo time/field of view/spectral width/matrix/section thickness of 14 milliseconds/1.8 milliseconds/60 × 60 mm2/50 kHz/128 × 128/2 mm) covering both kidneys with a temporal resolution of 1.75 seconds. Hyperpolarized [13C,15N2]-urea T1 relaxation estimation was performed with a dynamic series of nonselective spectroscopic acquisitions (repetition time/spectral width/flip angle of 2 seconds/20 kHz/10°).

Hyperpolarization

In both studies, a clinically ready 5 T SPINLAB polarizer was used (35). The samples was prepared by adding a mixed ratio of 200 μL of [13C,15N]urea (Sigma-Aldrich, Brøndby, Denmark), glycerol (Sigma-Aldrich, Brøndby, Denmark), and AH111501 (GE Healthcare, Brøndby, Denmark) (6.4 M concentration; 0.30:0.68:0.02) to a fluid path and placing it in the 5 T SPINLab polarizer (GE Healthcare, Brøndby, Denmark) for more than 2 hours to achieve a reproducible polarization of >30%. The sample was subsequently rapidly dissolved and transferred to the rats already placed in a 9.4 T preclinical MR scanner, with an injection volume of ∼1.0 mL (26, 30).

Data Analysis

Renal blood flow (RBF) was estimated by using the model-free formulation by Johansson et al. (5), in which the area-under-the-curve (AUC) ratio between the AIF and the cortical tissue curve is defined as follows (in mL/min per mL cortical tissue):

(1)
RBF=AUCcortexΔtAIF
where Δt represents the interimage delay (here 3 seconds). A correction for the plasma hematocrit, assumed to be 0.45, was used, which is similar to that used by Johansson et al. (5). Before fitting, the signal was smoothed with a lowess filter in the temporal dimension and corrected for T1 relaxation with a T1 relaxation time using a single exponential correction of 24 seconds (global [13C,15N2]urea T1 relaxation time as measured experimentally; see Results). GFR was estimated by calculating the kinetic rate (Kcl) of appearance of the signal in the medulla/pelvic region (36).
(2)
dC(t)medulladt=KclC(t)cortex
The upslope of the curve showing [13C,15N]urea signal in the medulla was estimated (Figure 3), in MATLAB (The MathWorks, Inc., Natick, Massachusetts), between the initial point of the cortical slope and the peak of the medullary slope (gray area in Figure 3). GFR (estimated Kcl) was then obtained by dividing the medullary slope with the mean renal cortex concentration during the upslope period (37). The GFR was expressed in milliliter per minute, to allow for comparison with previously reported values for skGFR and total GFR (38). We assumed a cortical and medullary tissue density of 1 for the conversion of the perfusion and GFR values.

Figure 3.

Region of interest (ROI) analysis signal as a function of time, showing a representative I/R (red) and contralateral (blue) medullary/pelvic signal curve. The gray area illustrates the timing to the maximum peak of the medullary signal. The bottom image shows the linear relationship between the signal intensity and the inflow over time in the medullary ROI.

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Statistics

Normality was assessed with quantile–quantile plots. A P-value <.05 was considered statistically significant. Statistical analysis was performed using GraphPad Prism (GraphPad Software, Inc., La Jolla, California). A 2-way paired ANOVA (left and right kidney paired) was used for statistical analysis of the renal perfusion and the GFR estimations; a post hoc Sidak multiple comparisons correction was used when appropriate. An unpaired Student t test was used for statistical analysis of the total GFR and the plasma creatinine concentration between the I/R group and the control group.

Results

A [13C,15N]urea T1 relaxation was found to be 24.5 ± 4 seconds (n = 4, in vivo at 9.4 T), allowing T1 correction of the hemodynamic acquisitions. A significant renal blood flow variation was observed among the 3 groups (P = .018), with a tendency toward an increased RBF in the I/R group.

No significant group difference was observed between control 1H DCE-derived RBF and control hyperpolarized [13C,15]N-urea RBF estimations (P = .23) (Table 1). A significant variation in hGFR was observed among the individual kidneys (P = .02), with a significant difference among the groups (interaction term group × kidney, P = .02) originating from a reduction to skGFR within the I/R group with a difference of −1.6 mL/min (P = .005) between each animal's 2 kidneys. No difference was seen between the control group (P = .99) and the diabetes group (P = .99) or within these groups.

Table 1.

Hemodynamic and Physiological Parameters from 1H DCE and FITC-Inulin

RBF Left Kidney RBF Right Kidney Body Weight Kidney Weight Cortical Weight Total GFR (DCE) GFR (inulin)
(mL/min/mL Tissue) (mL/min/mL Tissue) (g) (g) (g) (mL/min) (mL/min)
1 4.02 4.71 256 0.8 0.75 5 3.8
2 4.14 3.47 235 0.75 0.7 4.7 7.3
3 6.17 5.92 266 0.8 0.72 4.4
4 6.78 6.99 250 0.81 0.74 1.2 8
5 3.38 1.9 222 0.68 0.58 2.3 2.3
Mean ± SD 4.9 ± 1.3 4.6 ± 1.8 245.8 ± 15.6 0.77 ± 0.05 0.7 ± 0.06 3.5 ± 1.5 5.3 ± 2.4

i] Abbreviations: RBF, renal blood flow; GFR, glomerular filtration rate; DCE, dynamic contrast-enhanced.

A filtration fraction of ≈40% was found in both early diabetic rats (44.8% ± 9.5%) and healthy controls (42.8% ± 13.7%), whereas the filtration fraction reduced in the I/R group to 25.1% ± 12.2%. To evaluate the accuracy of the GFR estimation, the MR-derived “total hGFR” (sum of both kidneys) was compared with the plasma creatinine concentration between the controls [normal animals (26) + pre-I/R (30)] of 5.1 ± 1.6 mL/min and the I/R group of 3.4 ± 1.4 mL/min (30). The total hGFR was not significantly different between the control and the I/R group (unpaired t test, P = .28), albeit an increased plasma creatinine concentration associated with I/R 24 hours after reperfusion was observed (unpaired t test, P = .0045) (Figure 4). Furthermore, no statistical significant difference was found between 1H GFR and inulin measurements (paired t test, P = .33). In addition, no statistical difference was observed between any combinations of total GFR estimation (1-way ANOVA, P = .4) (Figure 4E).

Figure 4.

Renal blood flow (A), glomerular filtration (B), and filtration fraction (C), showing a clear tendency toward a general reduction in kidney function in the ischemic kidney, whereas no difference is seen between the early diabetes group and the controls. * denotes P < .05, mean ± SEM. Correlation between the plasma creatinine concentration and the total hGFR (sum of both kidneys) (D), showing a tendency toward decreased hGFR with increasing plasma creatinine concentrations. The lines depict the mean of the total GFR estimated with fluorescein isothiocyanate (FITC)-inulin. A comparison between the different GFR estimations shows no significant difference between the estimations with any combination of methods (E). * denotes P < .05 (mean ± SEM).

media/vol3/issue3/images/tom0031700970004.jpg

Discussion

The main finding in this study is the proof-of-concept that first-pass hyperpolarized [13C,15N2]urea transport can be used to estimate GFR. GFR findings by use of hyperpolarized MR showed good agreement with gold-standard inulin clearance values and with 1H DCE GFR values found under similar conditions and in congruence with previously reported values in similarly conditioned female rats, measured via the gold-standard inulin clearance (32) and with DCE-MRI (38).

Further, our estimated hGFRs compare well (1–3 mL/min lower) with human values, which are known to be on the order of 120 mL/min/70 kg, following conversion to rodent values by the allometric relationship described by Rhodin et al. (39). The allometric GFR was estimated by using the exponent range 2/3–3/4 (1.6–2.6 mL/min) (39). As previously reported (30), rats subjected to 60 min of ischemia and 24 h of reperfusion showed unaltered urine output. These data support our findings of a substantial reduction to single-kidney hGFR, whereas the total hGFR was only slightly reduced (indicated by increased plasma creatinine concentration and largely maintained hGFR, owing to residual function of the contralateral kidney). Interestingly, the data presented here suggest a compensatory effect on RBF in the contralateral kidney not subjected to I/R, although this trend did not reach significance (Figure 4). It is important to note that the inverse correlation between plasma creatinine concentration and I/R damage was not apparent in the paired experiments from the original study (30). This might be explained by the additional animals included here in the control group (n = 11) (5 from the study by Qi et al., 26, and 6 pre-IR from the study by Nielsen et al., 30) compared with the I/R group (n = 6).

Several limitations are apparent in this study. First, GFR estimation requires high spatiotemporal resolution. The interimage delay of 3 seconds reduced the accuracy of the time curve estimation and thus the fitting of the upslope of the medulla time curve. A similar effect is observed in 1H GFR methods (40), and it can be largely solved by increasing the temporal resolution when acquiring the hyperpolarized images. However, the available signal must be taken into consideration, as it is limited by the hyperpolarized radio frequency (RF) signal depletion, with a lower effective T1 seen in the imaging section (T1eff = 19 ± 3 seconds, estimated from the bSSFP images) owing to the imaging acquisition. It is difficult to compensate for the RF depletion (estimated to be 67% in these experiments; 41), as the imaging section is replenished by flowing spins into the imaging section. This is particularly important for the bolus differentiation perfusion assessment (5), potentially reducing the accuracy of the assessment, as the acquisition did not fully saturate the signal between images. The spatial resolution of 1.9 × 1.9 mm2 is a limiting factor as well, as shown in Figure 3. Here, the medullary signal was contaminated by the cortical signal owing to partial volume effects, thereby reducing the accuracy of the method. Furthermore, it should be stressed that because of the significant reabsorption of urea, it is likely that that urea estimate GFR is apparent by nature. Furthermore, the retrospective use of data and comparison with other methods (DCE and inulin) in additional groups is a limitation of the study. Further studies are needed to fully determine the observed correlation between true GFR and the estimated hGFR.

We selected the BR model because of its ease of implementation, its lack of reliance on AIF sampling, and the need for estimating only the upslope of the signal, removing the need to sample beyond the T1 relaxation decay. More complex models often depend on rapid and accurate sampling of the AIF and thus are particularly sensitive to appropriate placement of the imaging section and partial volumes effects of the intense signal from blood. The imaging section is typically a 1-cm mean intensity profile slab (permitted by the lack of background signal in 13C MR), containing kidneys, aorta, and vena cava. However, the simplicity of the BR model also presents limitations, namely, it assumes 2 distinct separated volumes (blood and urine), when in reality, both the cortical and medullary compartments contain blood and urine (3, 36). In future, advanced AIF sampling schemes may enable the use of more sophisticated models to improve the hGFR estimation. Importantly, the current knowledge on the relaxation behavior of the hyperpolarized [13C,15N2]urea tracer (T1 = 24 seconds at 9.4 T) limits the correction to a global T1 correction. In future, more appropriate relaxation models, both T1 and T2, could be incorporated that take compartmentalized relaxation properties into account (23, 28, 42, 43). Interestingly Reed et al. (23) have shown intrarenal compartmentalized T2 relaxation behavior at 3 T contrary to the reported T2 relaxation times at 9.4 T, finding only 1 T2 component (28). This implies that this would be particularly important at 3 T and supported by using the novel T1 and T2 mapping sequences (23) for accurate GFR assessment.

Finally, the use of hyperpolarized [13C,15N2]urea could potentially give rise to variations in GFR estimation, as 50% of urea is reabsorbed (24). Thus, alternative molecules such as creatinine, which are reabsorbed to a lesser degree, could potentially improve the hyperpolarized MR-based GFR estimation. Although, while typical GFR estimations are performed with free filtered-tracers (32, 2), the reabsorption of urea could represent a potential advantage over existing methods by allowing simultaneous estimation of the reabsorption (23, 25, 26, 20, 30, 29, 27). Furthermore, it has been demonstrated that several perfusion tracers can be hyperpolarized and imaged simultaneously (44), allowing more detailed knowledge on the filtration and reabsorption by combining biomarkers with different hemodynamic profiles (21, 45).

Conclusion

In conclusion, this study shows that hyperpolarized MR is a promising method for functional imaging of the kidneys. The study found that the estimated 13C-urea GFR was in good agreement with GFR calculated from inulin clearance and DCE MRI, as well as plasma creatinine measurements and literature findings. Future work to optimize MR data acquisition schemes and to quantitatively evaluate this approach is warranted.

Notes

[2] Abbreviations:

GFR

Glomerular filtration rate

MR

magnetic resonance

skGFR

single-kidney GFR

DCE

dynamic contrast-enhanced

AIF

arterial input function

BR

Baumann–Rudin

I/R

ischemia-reperfusion

MRI

magnetic resonance imaging

RBF

renal blood flow

Acknowledgments

The authors would like to acknowledge Henrik Vestergaard for laboratory assistance.

Ethical Approval: The study complied with the guidelines for use and care of laboratory animals and was approved by the Danish Inspectorate of Animal Experiments.

Disclosures: No disclosures to report.

Conflict of Interest: The authors have no conflict of interest to declare.

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