Multiparametric Magnetic Resonance Investigations on Acute and Long‐Term Kidney Injury

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 Evidence1Technical EfficacyStage 2

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

Zhao,

Seeliger,

Niendorf

et al. 2023

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

Stabinska,

Wittsack,

Lerman

et al. 2023

Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre‐urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion‐weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion‐weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non‐Gaussian diffusion, and hybrid IVIM‐DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI.Level of Evidence5Technical EfficacyStage 2

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

Yang,

Tian,

Liu

et al. 2024

BackgroundDiffusion 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.PurposeTo 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 TypeProspective.PopulationOne 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/Sequence1.5‐T, diffusion spectrum imaging (DSI) with 9 different b‐values.AssessmentThe 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 TestsMann–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.ResultsThe 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 ConclusionThe 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 Evidence2Technical EfficacyStage 2