Using magnetic resonance diffusion tensor imaging to evaluate renal function changes in diabetic patients with early-stage chronic kidney disease

Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes

BACKGROUND

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD.

PURPOSE

To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation.

STUDY TYPE

Prospective.

POPULATION

One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49).

FIELD STRENGTH/SEQUENCE

1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values.

ASSESSMENT

The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve.

STATISTICAL TESTS

Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05.

RESULTS

The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972).

DATA CONCLUSION

The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

The clinical and pathological evaluation of patients with immunoglobulin A nephropathy by diffusion tensor imaging and intravoxel incoherent motion diffusion-weighted imaging

OBJECTIVES

To explore the efficacy of diffuse magnetic resonance imaging (MRI) for identifying clinicopathological changes in immunoglobulin A nephropathy (IgAN) patients.

METHODS

The study enrolled IgAN patients and healthy volunteers. IgAN patients were divided into Group 1 [estimated glomerular filtration rate (eGFR) ≥ 90 mL/min/1.73 m2], Group 2 (60 ≤ eGFR < 90 mL/min/1.73 m2), and Group 3 (eGFR < 60 mL/min/1.73 m2). Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and diffusion tensor imaging (DTI) were performed via 3.0 T magnetic resonance. Diffuse MRI, clinical, and pathological indicators were collected and analysed. P < .05 was considered statistically significant.

RESULTS

Forty-six IgAN patients and twenty-seven volunteers were enrolled. The apparent diffusion coefficient, diffusion coefficient (D), perfusion fraction (f), and fractional anisotropy (FA) were significantly different among IgAN subgroups and controls. These parameters were positively correlated with eGFR and negatively with creatinine, and inversely correlated with glomerular sclerosis, interstitial fibrosis, and tubular atrophy (all P < .05). They had significantly high area under the curve (AUC) for distinguishing IgAN patients from controls, while FA had the highest AUC in identifying Group 1 IgAN patients from volunteers.

CONCLUSIONS

DTI and IVIM-DWI had the advantage of evaluating clinical and pathological changes in IgAN patients. DTI was superior at distinguishing early IgAN patients and might be a noninvasive marker for screening early IgAN patients from healthy individuals.

ADVANCES IN KNOWLEDGE

DTI and IVIM-DWI could evaluate clinical and pathological changes and correlated with Oxford classification in IgAN patients. They could also identify IgAN patients from healthy populations, while DTI had superiority in differentiating early IgAN patients.

Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope?

Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Diffusion-weighted, intravoxel incoherent motion, and diffusion kurtosis tensor MR imaging in chronic kidney diseases: Correlations with histology

OBJECTIVES

To probe the correlations of parameters derived from standard DWI and its extending models including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) with the pathological and functional alterations in CKD.

MATERIAL AND METHODS

Seventy-nine CKD patients with renal biopsy and 10 volunteers were performed with DWI, IVIM, diffusion kurtosis tensor imaging (DKTI) scanning. Correlations between imaging results and the pathological damage [glomerulosclerosis index (GSI) and tubulointerstitial fibrosis index (TBI)], as well as eGFR, 24 h urinary protein and Scr) were evaluated.CKD patients were divided into 2 groups: group 1: both GSI and TBI scores <2 points (61 cases); group 2: both GSI and TBI scores ≥2 points (18 cases).

RESULTS

There were significant difference in cortical and medullary MD, and cortical D among 3 groups and between group 1 and 2. Cortical and medullary MD, cortical D, and medullary FA were negatively correlated with GSI score (r = -0.322 to -0.386, P < 0.05). Cortical and medullary MD and D, medullary FA were also negatively correlated with TBI score (r = -0.257 to -0.395, P < 0.05). These parameters were all correlated with eGFR and Scr. Cortical MD and D showed the highest AUC of 0.790 and 0.745 in discriminating mild and moderate-severe glomerulosclerosis and tubular interstitial fibrosis, respectively.

CONCLUSIONS

The corrected diffusion-related indices, including cortical and medullary D and MD, as well as medullary FA were superior to ADC, perfusion-related and kurtosis indices for evaluating the severity of renal pathology and function in CKD patients.

Early assessment of acute kidney injury in severe acute pancreatitis with multimodal DWI: an animal model

OBJECTIVES

To evaluate the feasibility of multimodal diffusion-weighted imaging (DWI) for detecting the occurrence and severity of acute kidney injury (AKI) caused by severe acute pancreatitis (SAP) in rats.

METHODS

SAP was induced in thirty rats by the retrograde injection of 5.0% sodium taurocholate through the biliopancreatic duct. Six rats underwent MRI of the kidneys 24 h before and 2, 4, 6, and 8 h after this AKI model was generated. Conventional and functional MRI sequences were used, including intravoxel incoherent motion imaging (IVIM), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DTI). The main DWI parameters and histological results were analyzed.

RESULTS

The fast apparent diffusion coefficient (ADC) of the renal cortex was significantly reduced at 2 h, as was the fractional anisotropy (FA) value of the renal cortex on DTI. The mean kurtosis (MK) values for the renal cortex and medulla gradually increased after model generation. The renal histopathological score was negatively correlated with the medullary slow ADC, fast ADC, and perfusion scores for both the renal cortex and medulla, as were the ADC and FA values of the renal medulla in DTI, whereas the MK values of the cortex and medulla were positively correlated (r = 0.733, 0.812). Thus, the cortical fast ADC, medullary MK, FADTI, and slow ADC were optimal parameters for diagnosing AKI. Of these parameters, cortical fast ADC had the highest diagnostic efficacy (AUC = 0.950).

CONCLUSIONS

The fast ADC of the renal cortex is the core indicator of early AKI, and the medullary MK value might serve as a sensitive biomarker for grading renal injury in SAP rats.

CLINICAL RELEVANCE STATEMENT

The multimodal parameters of renal IVIM, DTI, and DKI are potential beneficial for the early diagnosis and severity grading of renal injury in SAP patients.

KEY POINTS

• The multimodal parameters of renal DWI, including IVIM, DTI, and DKI, may be valuable for the noninvasive detection of early AKI and the severity grading of renal injury in SAP rats. • Cortical fast ADC, medullary MK, FA, and slow ADC are optimal parameters for early diagnosis of AKI, and cortical fast ADC has the highest diagnostic efficacy. • Medullary fast ADC, MK, and FA as well as cortical MK are useful for predicting the severity grade of AKI, and the renal medullary MK value exhibits the strongest correlation with pathological scores.

The value of functional magnetic resonance imaging in the evaluation of diabetic kidney disease: a systematic review and meta-analysis

Background

The diversity of clinical trajectories in diabetic kidney disease (DKD) has made blood and biochemical urine markers less precise, while renal puncture, the gold standard, is almost impossible in the assessment of diabetic kidney disease, and the value of functional magnetic resonance imaging in the evaluation of diabetic pathological alterations is increasingly recognized.

Methods

The literature on functional magnetic resonance imaging (fMRI) for the assessment of renal alterations in diabetic kidney disease was searched in PubMed, Web of Science, Cochrane Library, and Embase databases. The search time limit is from database creation to March 10, 2023. RevMan was used to perform a meta-analysis of the main parameters of fMRIs extracted from DKD patients and healthy volunteers (HV).

Results

24 publications (1550 subjects) were included in this study, using five functional MRIs with seven different parameters. The renal blood flow (RBF) values on Arterial spin labeling magnetic resonance imaging (ASL-MRI) was significantly lower in the DKD group than in the HV group. The [WMD=-99.03, 95% CI (-135.8,-62.27), P<0.00001]; Diffusion tensor imaging magnetic resonance imaging (DTI-MRI) showed that the fractional anisotropy (FA) values in the DKD group were significantly lower than that in HV group [WMD=-0.02, 95%CI (-0.03,-0.01), P<0.0001]. And there were no statistically significant differences in the relevant parameters in Blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) or Intro-voxel incoherent movement magnetic resonance imaging (IVIM-DWI).

Discussion

ASL and DWI can identify the differences between DKD and HV. DTI has a significant advantage in assessing renal cortical changes; IVIM has some value in determining early diabetic kidney disease from the cortex or medulla. We recommend combining multiple fMRI parameters to assess structural or functional changes in the kidney to make the assessment more comprehensive. We did not observe a significant risk of bias in the present study.

Systematic review registration

https://www.crd.york.ac.uk, identifier CRD42023409249.

Effectiveness of functional magnetic resonance imaging for early identification of chronic kidney disease: A systematic review and network meta-analysis

PURPOSE

The commonly used clinical indicators are not sensitive enough on detecting early chronic kidney disease (CKD), whether functional magnetic resonance imaging (fMRI) can be regarded as a new noninvasive method to identify early stages of CKD and even different stages remains unknown. We performed a network meta-analysis to explore the question.

METHODS

Five databases were searched to identify eligible articles from 2000 to 2022. The outcome indicators were imaging biomarkers of fMRI techniques, including apparent diffusion coefficient (ADC) by diffusion-weighted imaging (DWI), fractional anisotropy (FA) by diffusion tensor imaging (DTI), diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) by intravoxel incoherent motion imaging (IVIM), and apparent relaxation rate (R2*) by blood oxygen level-dependent (BOLD).

RESULTS

A total of 21 articles with 1472 patients were included for analysis. Cortical FA, f, and R2* values in CKD stages 1-2 were found statistically different with healthy controls (mean difference (MD), -0.03, 95% confidence interval (CI) -0.05, -0.01; MD, -0.04, 95% CI -0.06, -0.02; MD, 2.22, 95% CI 0.87, 3.57, respectively), and cortical ADC values were significantly different among different CKD stages (stages 3 and 1-2: MD, -0.15, 95% CI -0.23, -0.06; stages 4-5 and 3: MD -0.27, 95% CI -0.39, -0.14).

CONCLUSION

The results indicated fMRI techniques had great efficacy in assessing early stages and different stages of CKD, among which DTI, IVIM, and BOLD exerted great superiority in differentiating early CKD patients from the general population, while DWI showed the advantage in distinguishing different CKD stages.

Diffusion tensor imaging for the study of early renal dysfunction in patients affected by bardet-biedl syndrome

Kidney structural abnormalities are common features of Bardet-Biedl syndrome (BBS) patients that lead to a progressive decline in renal function. Magnetic resonance diffusion tensor imaging (DTI) provides useful information on renal microstructures but it has not been applied to these patients. This study investigated using DTI to detect renal abnormalities in BBS patients with no overt renal dysfunction. Ten BBS subjects with estimated glomerular filtration rates over 60 ml/min/1.73m² and 14 individuals matched for age, gender, body mass index and renal function were subjected to high-field DTI. Fractional anisotropy (FA), and mean, radial and axial diffusivity were evaluated from renal cortex and medulla. Moreover, the corticomedullary differentiation of each DTI parameter was compared between groups. Only cortical FA statistically differed between BBS patients and controls (p = 0.033), but all the medullary DTI parameters discriminated between the two groups with lower FA (p < 0.001) and axial diffusivity (p = 0.021) and higher mean diffusivity (p = 0.043) and radial diffusivity (p < 0.001) in BBS patients compared with controls. Corticomedullary differentiation values were significantly reduced in BBS patients. Thus, DTI is a valuable tool for investigating microstructural alterations in renal disorders when kidney functionality is preserved.

Effect of type 2 diabetes on Gd-EOB-DTPA uptake into liver parenchyma: replication study in human subjects

PURPOSE

Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) is a contrast agent for magnetic resonance imaging (MRI), which specifically taken up by hepatocytes through organic anion-transporting polypeptides (OATPs). Previous research in mice has shown that type 2 diabetes is associated with reduced uptake of Gd-EOB-DTPA into the liver parenchyma, reflecting reduced expression of OATP. Since considerable differences in OATP expression exist between mice and humans, human studies are necessary to clarify the effect of diabetes to Gd-EOB-DTPA uptake. The purpose of this study was to validate the effect of diabetes to Gd-EOB-DTPA liver uptake by a confirmatory study in humans.

METHODS

Patients who underwent Gd-EOB-DTPA-enhanced MRI were retrospectively reviewed and divided into two groups: severe or uncontrolled diabetic group (patients with insulin therapy and/or HbA1c ≥ 8.4%) and the control group. Liver-to-spleen ratio (LSR) and relative enhancement of the liver (REL) were calculated to represent Gd-EOB-DTPA liver uptake.

RESULTS

A total of 94 patients fulfilled the criteria. The severe or uncontrolled diabetic group (n = 15) showed significantly lower LSR (1.74 ± 0.26 vs. 1.98 ± 0.31, p = 0.007) and REL (0.69 ± 0.23 vs. 0.87 ± 0.31, p = 0.005), compared to the control group (n = 79).

CONCLUSION

Our study revealed decreased uptake of Gd-EOB-DTPA into liver parenchyma in the severe or uncontrolled diabetic patients. Further studies to determine the impact of the reduced liver enhancement on clinical diagnostic practice will be needed.

The Value of Brain Resting-State Functional Magnetic Resonance Imaging on Image Registration Algorithm in Analyzing Abnormal Changes of Neuronal Activity in Patients with Type 2 Diabetes

The aim of this paper was to analyze the application value of resting-state functional magnetic resonance imaging (FMRI) parameters and rigid transformation algorithm in patients with type 2 diabetes (T2DM), which could provide a theoretical basis for the registration application of FMRI. 107 patients confirmed pathologically as T2DM and 51 community medical healthy volunteers were selected and divided into an experimental group and a control group, respectively. Besides, all the subjects were scanned with FMRI. Then, the rigid transformation-principal axis algorithm (RT-PAA), Levenberg-Marquardt iterative closest point (LMICP), and Demons algorithm were applied to magnetic resonance image registration. It was found that RT-PAA was superior to LMICP and Demons in image registration. The amplitude of low-frequency fluctuation (ALFF) values of the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients from the experimental group were lower than those of the control group (P < 0.05). The Montreal cognitive assessment (MoCA) score was extremely negatively correlated with the ALFF of the left middle temporal gyrus (r = -0.451 and P < 0.001) and highly positively associated with the ALFF of the right posterior cerebellar lobe (r = -0.484 and P < 0.001). In addition, the MoCA score of patients had a dramatically negative correlation with the ALFF of the left middle temporal gyrus (r = -0.602 and P < 0.001) and had a greatly positive correlation with the ALFF of the right posterior cerebellar lobe (r = -0.516 and P < 0.001). The results showed that RT-PAA based on rigid transformation in this study had a good registration effect on magnetic resonance images. Compared with healthy volunteers, the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients with T2DM showed abnormal neuronal changes and reduced cognitive function.

Assessment of kidney function in chronic kidney disease by combining diffusion tensor imaging and total kidney volume

OBJECTIVE

This study aimed to investigate the value and feasibility of combining fractional anisotropy (FA) values from diffusion tensor imaging (DTI) and total kidney volume (TKV) for the assessment of kidney function in chronic kidney disease (CKD).

MATERIALS AND METHODS

Fifty-one patients were included in this study. All MRI examinations were performed with a 3.0 T scanner. DTI was used to measure FA values, and TKV was obtained from DTI and T2-weighted imaging (T2WI). Patients were divided into three groups (mild, moderate, severe) according to eGFR, which was calculated with serum creatinine. Differences in the FA values of the cortex and medulla were analysed among the three groups, and the relationships of FA values, TKV, and the product of the FA values and TKV with eGFR were analysed. Receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficiency of the FA values, TKV, and the product of the FA values and TKV for kidney function in different CKD stages.

RESULTS

Medullary FA values (m-FA), TKV, and the product of the m-FA values and TKV (m-FA-TKV) were significantly correlated with eGFR (r = 0.653, 0.685, and 0.797, respectively; all P < 0.001). ROC curve analysis showed that m-FA-TKV exhibited better diagnostic performance than m-FA values (P = 0.022).

CONCLUSION

m-FA-TKV obtained by DTI significantly improves the accuracy of kidney function assessment in CKD patients.

Recent advances in medical image processing for the evaluation of chronic kidney disease

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.

DTI-based radiomics signature for the detection of early diabetic kidney damage

OBJECTIVE

To explore whether a radiomics signature based on diffusion tensor imaging (DTI) can detect early kidney damage in diabetic patients.

MATERIALS AND METHODS

Twenty-eight healthy volunteers (group A) and thirty type 2 diabetic patients (group B) with micro-normoalbuminuria, a urinary albumin-to-creatinine ratio (ACR) < 30 mg/g and an estimated glomerular filtration rate (eGFR) of 60-120 mL/(min 1.73 m²) were recruited. Kidney DTI was performed using 1.5T magnetic resonance imaging (MRI).The radiologist manually drew regions of interest (ROI) on the fractional anisotropy (FA) map of the right kidney ROI including the cortex and medulla. The texture features of the ROIs were extracted using MaZda software. The Fisher coefficient, mutual information (MI), and probability of classification error and average correlation coefficient (POE + ACC) methods were used to select the texture features. The most valuable texture features were further selected by the least absolute shrinkage and selection operator (LASSO) algorithm. A LASSO regression model based on the radiomics signature was established. The diagnostic performance of the model for detecting early diabetic kidney changes was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Empower (R), R, and MedCalc15.8 software were used for statistical analysis RESULTS: A total of 279 texture features were extracted from ROI of the kidney, and 30 most valuable texture features were selected from groups A and B using MaZda software. After LASSO-logistic regression, a diagnostic model of diabetic kidney damage based on texture features was established. Model discrimination evaluation: AUC = 0.882 (0.770 ± 0.952). Model calibration evaluation: Hosmer-Lemeshow X2 = 5.3611, P = 0.7184, P > 0.05, the model has good calibration.

CONCLUSION

The texture features based on DTI could play a promising role in detecting early diabetic kidney damage.

Non-invasive evaluation of renal structure and function of healthy individuals with multiparametric MRI: Effects of sex and age

Clinically, when applying multiparametric magnetic resonance imaging (MRI) examinations in renal diseases, assessment of renal structure and function has to account for age- and sex-related effects. The aim of this study was to investigate the influence of age and sex on multiparametric MRI assessment of renal structure and function in healthy human beings. Studies on 33 healthy volunteers were performed using multiparametric MRI on a 3.0-Tesla MR scanner, including T1-weighted imaging, blood oxygen level-dependent MRI (BOLD MRI), diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI). Our results revealed that the mean renal cortical thickness (RCT), ratio of cortex to parenchyma (CPR), and cortical R2* values were higher in males than in females. The cortical R2* value was higher in older group than in younger group (18.57 ± 0.99 vs 17.53 ± 0.58, p = 0.001); there was no significant difference in medullary R2* between the older and younger groups (38.18 ± 2.96 vs 36.45 ± 2.47, p = 0.077). The parenchymal thickness (PT) and medullary fractional anisotropy (FA) were lower in older group than in younger group (1.547 ± 0.06 vs 1.604 ± 0.05, p = 0.005 and 0.343 ± 0.03 vs 0.371 ± 0.03, p = 0.016, respectively). Pearson's correlation analysis showed that PT and medullary FA were inversely related with age (r = -0.483, p = 0.004; r = -0.446, p = 0.009) while cortical R2* values was positively related (r = 0.511, p = 0.002, respectively). The medullary apparent diffusion coefficient (ADC) value had a significant association with PT (r = 0.359, p = 0.04). This study indicated that multiparametric renal MRI parameters are age and sex dependent.