Combination of renal apparent diffusion coefficient and renal parenchymal volume for better assessment of split renal function in chronic kidney disease

Integrated Diffusion Tensor Imaging and Renal Parenchymal Volume for Early Detection and Grading of Split Renal Functional Impairment in Lupus Nephritis

RATIONALE AND OBJECTIVES

To investigate the effectiveness of combining split diffusion tensor imaging (DTI) measurements with split renal parenchymal volume (RPV) for assessing split renal functional impairment in patients with lupus nephritis (LN).

MATERIALS AND METHODS

Seventy-four participants [48 LN patients and 26 healthy volunteers (HV)] were included in the study. All participant underwent conventional MR and DTI (b = 0, 400, and 600 s/mm2) examinations using a 3.0 T MRI scanner to determine the split renal DTI measurements and split RPV. In LN patients, renography glomerular filtration rate (rGFR) was measured using 99mTc-DTPA scintigraphy based on Gates' method, serving as the reference standard to categorize all split kidneys of LN patients into LN with mild impairment (LNm, n = 65 kidneys) and LN with moderate to severe (LNms, n = 31 kidneys) groups according to the threshold of 30 ml/min in spilt rGFR. All statistical analyses were performed using SPSS 25.0 and MedCalc 20.0 software packages.

RESULTS

Only split medullary fractional anisotropy (FA) and the product of split medullary FA and RPV could distinguish pairwise subgroups among the HV and each LN subgroup (all p < 0.05). ROC curve analysis demonstrated that split medullary FA (AUC = 0.866) significantly outperformed other parameters in differentiating HV from LNm groups, while the product of split medullary FA and split RPV was superior in distinguishing LNm and LNms groups (AUC = 0.793) than other parameters. The combination of split medullary FA and split RPV showed best correlation with split rGFR (r = 0.534, p < 0.001).

CONCLUSION

Split medullary FA, and its combination with split RPV, are valuable biomarkers for detecting early functional changes in renal alterations and predicting disease progression in patients with LN.

Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy

BACKGROUND

Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS.

METHODS

Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2 weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF.

RESULTS

SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r =  - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all).

CONCLUSIONS

NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.

Evaluation of renal function in chronic kidney disease using histogram analysis based on multiple diffusion models

OBJECTIVES

To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD).

METHODS

A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models.

RESULTS

All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features.

CONCLUSIONS

Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD.

ADVANCES IN KNOWLEDGE

Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

Noninvasive assessment of clinical and pathological characteristics of patients with IgA nephropathy by diffusion kurtosis imaging

Objectives

To explore the diagnostic performance of diffusion kurtosis imaging (DKI) in evaluating the clinical and pathological characteristics of patients with immunoglobulin A nephropathy (IgAN) compared with conventional DWI.

Materials and methods

A total of 28 IgAN patients and 14 healthy volunteers prospectively underwent MRI examinations including coronal T2WI, axial T1WI, T2WI, and DWI sequences from September 2020 to August 2021. We measured mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) by using MR Body Diffusion Toolbox v1.4.0 (Siemens Healthcare). Patients were divided into three groups according to their estimated glomerular filtration rate (eGFR) (Group1, healthy volunteers without kidney disease or other diseases that affect renal function; Group2, IgAN patients with eGFR > 60 mL/min/1.73 m²; Group3, IgAN patients with eGFR < 60 mL/min/1.73 m²). One-way analysis of variance, Pearson or Spearman correlation, and receiver operating characteristic curves were applied in our statistical analysis.

Results

MK_Cortex and ADC_Cortex showed significant differences between the Group1 and Group2. MK_Cortex, MD_Cortex, ADC_Cortex, MK_Medulla, and ADC_Medulla showed significant differences between Group2 and Group3. MK_Cortex had the highest correlation with CKD stages (r = 0.749, p < 0.001), and tubulointerstitial lesion score (r = 0.656, p < 0.001). MD_Cortex had the highest correlation with glomerular lesion score (r = − 0.475, p = 0.011). MK_Cortex had the highest AUC (AUC = 0.923) for differentiating Group1 from Group2, and MD_Cortex had the highest AUC (AUC = 0.924) for differentiating Group2 from Group3, followed by MK_Medulla (AUC = 0.923).

Conclusions

DKI is a feasible and reliable technique that can assess the clinical and pathological characteristics of IgAN patients and can provide more valuable information than conventional DWI, especially MK_Cortex.

Characteristics of diffusion-weighted and blood oxygen level-dependent magnetic resonance imaging in Tubulointerstitial nephritis: an initial experience

BACKGROUND

Diffusion-weighted (DW) and blood oxygen level-dependent (BOLD) magnetic resonance imaging are classical sequences of functional MR, but the exploration in non-transplanted kidney disease is limited.

OBJECTS

To analyze the characteristics of apparent diffusion coefficient (ADC) and R2* value using DW and BOLD imaging in tubulointerstitial nephritis (TIN).

METHODS

Four acute TIN, thirteen chronic TIN patients, and four controls were enrolled. We used multiple gradient-echo sequences to acquire 12 T2*-weighted images to calculate the R2* map. DW imaging acquired ADC values by combining a single-shot spin-echo echo-planar imaging pulse sequence and the additional motion probing gradient pulses along the x,y, z-axes with two b values:0 and 200, as well as 0 and 800 s/mm2. ATIN patients performed DW and BOLD magnetic resonance at renal biopsy(T0) and the third month(T3). We assessed the pathological changes semiquantitatively, and conducted correlation analyses within functional MR, pathological and clinical indexes.

RESULTS

In ATIN, ADCs were significantly lower(b was 0,200 s/mm2, 2.86 ± 0.19 vs. 3.39 ± 0.11, b was 0,800 s/mm2, 1.76 ± 0.12 vs. 2.16 ± 0.08, P < 0.05) than controls, showing an obvious remission at T3. Cortical and medullary R2* values (CR2*,MR2*) were decreased, significant difference was only observed in MR2*(T0 24.3 ± 2.1vs.T3 33.1 ± 4.1,P < 0.05). No relationship was found between functional MR and histopathological indexes.MR2* had a close relationship with eGFR (R = 0.682,P = 0.001) and serum creatinine(R = -0.502,P = 0.012). Patients with lower ADC when b was 0,200 s/mm2 showed more increase of ADC(R = -0.956,P = 0.044) and MR2*(R = -0.949,P = 0.05) after therapy. In CTIN group, lowered MR2* and MR2*/CR2* provided evidence of intrarenal ischemia. CTIN with advanced CKD (eGFR< 45) had significantly lower ADCb200 value.

CONCLUSIONS

We observed the reduction and remission of ADC and R2* values in ATIN case series. ATIN patients had concurrently decreased ADCb800 and MR2*. The pseudo normalization of CR2* with persistently low MR2* in CTIN suggested intrarenal hypoxia.

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.

Deep convolutional neural network for chronic kidney disease prediction using ultrasound imaging

Objectives

Chronic kidney disease (CKD) is a common disease and it is related to a higher risk of cardiovascular disease and end-stage renal disease that can be prevented by the earlier recognition and diagnosis of individuals at risk. Even though risk factors for CKD have been recognized, the effectiveness of CKD risk classification via prediction models remains uncertain. This paper intends to introduce a new predictive model for CKD using US image.

Methods

The proposed model includes three main phases “(1) preprocessing, (2) feature extraction, (3) and classification.” In the first phase, the input image is subjected to preprocessing, which deploys image inpainting and median filtering processes. After preprocessing, feature extraction takes place under four cases; (a) texture analysis to detect the characteristics of texture, (b) proposed high-level feature enabled local binary pattern (LBP) extraction, (c) area based feature extraction, and (d) mean intensity based feature extraction. These extracted features are then subjected for classification, where “optimized deep convolutional neural network (DCNN)” is used. In order to make the prediction more accurate, the weight and the activation function of DCNN are optimally chosen by a new hybrid model termed as diversity maintained hybrid whale moth flame optimization (DM-HWM) model.

Results

The accuracy of adopted model at 40th training percentage was 44.72, 11.02, 5.59, 3.92, 3.92, 3.57, 2.59, 1.71, 1.68, and 0.42% superior to traditional artificial neural networks (ANN), support vector machine (SVM), NB, J48, NB-tree, LR, composite hypercube on iterated random projection (CHIRP), CNN, moth flame optimization (MFO), and whale optimization algorithm (WOA) models.

Conclusions

Finally, the superiority of the adopted scheme is validated over other conventional models in terms of various measures.

Application of multiparametric MR imaging to predict the diversification of renal function in miR29a-mediated diabetic nephropathy

Diabetic nephropathy (DN) is one of the major leading cause of kidney failure. To identify the progression of chronic kidney disease (CKD), renal function/fibrosis is playing a crucial role. Unfortunately, lack of sensitivities/specificities of available clinical biomarkers are key major issues for practical healthcare applications to identify the renal functions/fibrosis in the early stage of DN. Thus, there is an emerging approach such as therapeutic or diagnostic are highly desired to conquer the CKD at earlier stages. Herein, we applied and examined the application of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) to identify the progression of fibrosis between wild type (WT) and miR29a transgenic (Tg) mice during streptozotocin (STZ)-induced diabetes. Further, we also validate the potential renoprotective role of miR29a to maintain the renal perfusion, volume, and function. In addition, Ktrans values of DCE-MRI and apparent diffusion coefficient (ADC) of DWI could significantly reflect the level of fibrosis between WT and Tg mice at identical conditions. As a result, we strongly believed that the present non-invasive MR imaging platforms have potential to serveas an important tool in research and clinical imaging for renal fibrosis in diabetes, and that microenvironmental changes could be identified by MR imaging acquisition prior to histological biopsy and diabetic podocyte dysfunction.

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.

One-stop assessment of renal function and renal artery in hypertensive patients with suspected renal dysfunction: non-enhanced MRI using spatial labeling with multiple inversion pulses

OBJECTIVES

To assess whether spatial labeling with multiple inversion pulses (SLEEK) sequence can be employed as a one-stop assessment method for evaluating renal function and displaying renal artery in hypertensive patients with suspected renal dysfunction.

METHODS

A total of 78 patients with suspected hypertensive renal damage were enrolled in this retrospective study. All patients underwent MRI examinations, and both SLEEK and DWI sequences were performed simultaneously. According to estimated glomerular filtration rate (eGFR), patients were divided into three groups (Group 1, eGFR> 90; Group 2, eGFR = 60-90; Group 3, eGFR< 60). Twenty-two of these patients also underwent CT angiography (CTA) examination. Comparison between CTA, DWI, and eGFR was performed to assess the value of SLEEK in evaluating renal function and displaying renal artery.

RESULTS

The performance of SLEEK to display renal artery was highly consistent with the results of CTA (kappa = 0.713). The corticomedullary contrast ratio positively correlated with eGFR (p = 0.004, r = 0.322) and was significantly higher in SLEEK images than in DWI images in all three groups (p < 0.001). There was no significant difference in corticomedullary contrast ratio in SLEEK images between Group 1 and Group 2 (p = 0.285). However, the minimal renal cortical thickness, which significantly correlated with eGFR (p < 0.001, r = 0.866), was significantly different between Group 1 and Group 2 (p < 0.001). ROC analysis showed good diagnostic performance when differentiating patients with eGFR> 60 from those with eGFR< 60.

CONCLUSIONS

The SLEEK sequence could evaluate simultaneously renal function through corticomedullary differentiation and renal arteries, enabling one-stop assessment in hypertensive patients with suspected renal dysfunction.

KEY POINTS

• Spatial labeling with multiple inversion pulses (SLEEK) improves renal corticomedullary differentiation in hypertensive patients with renal dysfunction compared with DWI. • SLEEK clearly displays renal artery in hypertensive patients with renal dysfunction. • SLEEK could be utilized as a one-stop assessment method for evaluating renal function and renal artery in hypertensive patients.