Quantitative assessment of renal structural and functional changes in chronic kidney disease using multi-parametric magnetic resonance imaging

Abstract

Background: Multi-parametric magnetic resonance imaging (MRI) provides the potential for a more comprehensive non-invasive assessment of organ structure and function than individual MRI measures, but has not previously been comprehensively evaluated in chronic kidney disease (CKD).

Methods: We performed multi-parametric renal MRI in persons with CKD (n = 22, 61 ± 24 years) who had a renal biopsy and measured glomerular filtration rate (mGFR), and matched healthy volunteers (HV) (n = 22, 61 ± 25 years). Longitudinal relaxation time (T1), diffusion-weighted imaging, renal blood flow (phase contrast MRI), cortical perfusion (arterial spin labelling) and blood-oxygen-level-dependent relaxation rate (R2*) were evaluated.

Results: MRI evidenced excellent reproducibility in CKD (coefficient of variation <10%). Significant differences between CKD and HVs included cortical and corticomedullary difference (CMD) in T1, cortical and medullary apparent diffusion coefficient (ADC), renal artery blood flow and cortical perfusion. MRI measures correlated with kidney function in a combined CKD and HV analysis: estimated GFR correlated with cortical T1 (r = -0.68), T1 CMD (r = -0.62), cortical (r = 0.54) and medullary ADC (r = 0.49), renal artery flow (r = 0.78) and cortical perfusion (r = 0.81); log urine protein to creatinine ratio (UPCR) correlated with cortical T1 (r = 0.61), T1 CMD (r = 0.61), cortical (r = -0.45) and medullary ADC (r = -0.49), renal artery flow (r = -0.72) and cortical perfusion (r = -0.58). MRI measures (cortical T1 and ADC, T1 and ADC CMD, cortical perfusion) differed between low/high interstitial fibrosis groups at 30-40% fibrosis threshold.

Conclusion: Comprehensive multi-parametric MRI is reproducible and correlates well with available measures of renal function and pathology. Larger longitudinal studies are warranted to evaluate its potential to stratify prognosis and response to therapy in CKD.

Keywords: chronic kidney disease; haemodynamics; magnetic resonance imaging; multi-parametric; renal function.

FIGURE 1

GFR. (A) eGFR in the HV group; eGFR and mGFR in the CKD group. (B) Correlation of eGFR with age, showing a significant correlation for both the HV (open circles) (R = −0.62, P = 0.002) and CKD (filled circles) (R = −0.63, P = 0.002) group.

FIGURE 2

MRI parameters in the CKD and HV group for (A) SE-EPI T₁ in cortex, medulla and CMD (ΔT₁), (B) ADC in cortex, medulla and CMD (ΔADC), (C) R₂* in cortex, medulla and CMD (ΔR₂*), (D) cortical perfusion as measured by ASL, total renal artery flow and global perfusion (estimated from correcting renal artery flow for kidney volume). Significant differences are seen between the CKD and HV group for cortical T₁ and ΔT₁ (SE-EPI data shown here, with the correlation between SE-EPI and bFFE T₁ measures shown in Supplementary data, Figure S2), cortical and medullary ADC, cortical perfusion, cortical and medullary ADC, total renal artery flow and global perfusion.

FIGURE 3

Correlation matrix for combined HV and CKD groups of biochemical measures [eGFR and log(UPCR)] and multi-parametric MRI measures. Significant correlations of MRI measures with biochemical measures are shown in Figure 4. Between the multi-parametric MRI measures, significant correlations are observed between cortical T₁ and cortical perfusion (R = −0.595, P < 0.001), total renal artery blood flow (R = −0.655, P < 0.001), global perfusion (R = −0.435, P = 0.001); between T₁ CMD (ΔT₁) and cortical perfusion (R = −0.587, P < 0.001) and total renal artery blood flow (R = −0.397, P = 0.05). Correlations are also seen between cortical ADC and cortical perfusion (R = 0.452 P = 0.02). Between haemodynamic measures, significant correlations were observed between cortical perfusion and total renal artery flow (R = 0.596, P = 0.002), and cortical perfusion and global perfusion (R = 0.44, P = 0.04).

FIGURE 4

MRI measures which show significant correlations with eGFR and log(UPCR) across combined HV (open circles) and CKD (filled circles) groups.

FIGURE 5

(A) Correlation matrix for the CKD group alone of biochemical measures [mGFR and log(UPCR)] and multi-parametric MRI measures. (B) MRI measures which show significant correlations with mGFR and log(UPCR) for the CKD group alone.

FIGURE 6

Fibrosis percentage binned into a binary factor ‘Low’ (blue) or ‘High’ (orange) IF based on varying the fibrosis threshold between 20% and 70%. It can be seen that changing the IF threshold results in a significant difference between ‘Low’ and ‘High’ IF groups for cortical T₁ (P = 0.014) and T₁ CMD (ΔT₁) (P = 0.017), cortical ADC (P < 0.0001) and ADC CMD (ΔADC) (P = 0.048), and ASL-derived cortical perfusion (P = 0.001) as determined by ANOVA. Significant differences (Wilcoxon P < 0.05) between ‘Low’ and ‘High’ IF groups for a given IF threshold are shown by *.