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Bikbov B, Purcell CA, Levey AS, et al. Global, regional, and national burden of chronic kidney disease, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2020; 395:709\u2013733.","DOI":"10.1016\/S0140-6736(20)30045-3"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Afkarian M, Sachs MC, Kestenbaum B, et al. Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol 2013; 24:302\u2013308.","DOI":"10.1681\/ASN.2012070718"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Levin A, Tonelli M, Bonventre J, et al. Global kidney health 2017 and beyond: A roadmap for closing gaps in care, research, and policy. Lancet 2017; 390:1888\u20131917.","DOI":"10.1016\/S0140-6736(17)30788-2"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Makvandi K, Hockings PD, Jensen G, et al. Multiparametric magnetic resonance imaging allows non-invasive functional and structural evaluation of diabetic kidney disease. Clin Kidney J 2022; 15:1387\u20131402.","DOI":"10.1093\/ckj\/sfac054"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Selby NM, Blankestijn PJ, Boor P, et al. Magnetic resonance imaging biomarkers for chronic kidney disease: A position paper from the European Cooperation in Science and Technology Action PARENCHIMA. Nephrol Dial Transplant 2018; 33(suppl_2):ii4\u2013ii14.","DOI":"10.1093\/ndt\/gfy152"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011; 305:1553\u20131559.","DOI":"10.1001\/jama.2011.451"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Glassock RJ, Warnock DG, Delanaye P. The global burden of chronic kidney disease: Estimates, variability and pitfalls. Nat Rev Nephrol 2017; 13:104\u2013114.","DOI":"10.1038\/nrneph.2016.163"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Francis ST, Selby NM, Taal MW. Magnetic resonance imaging to evaluate kidney structure, function, and pathology: Moving toward clinical application. Am J Kidney Dis 2023; 82:491\u2013504.","DOI":"10.1053\/j.ajkd.2023.02.007"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Zhao K, Seeliger E, Niendorf T, Liu Z. Noninvasive assessment of diabetic kidney disease with MRI: Hype or hope? J Magn Reson Imaging 2024; 59:1494\u20131513.","DOI":"10.1002\/jmri.29000"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Wolf M, de Boer A, Sharma K, et al. Magnetic resonance imaging T1- and T2-mapping to assess renal structure and function: A systematic review and statement paper. Nephrol Dial Transplant 2018; 33(suppl_2):ii41\u2013ii50.","DOI":"10.1093\/ndt\/gfy198"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Friedli I, Crowe LA, Berchtold L, et al. 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Eur Radiol 2023; 33:4138\u20134147.","DOI":"10.1007\/s00330-022-09329-7"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Greite R, Derlin K, Hartung D, et al. Diffusion weighted imaging and T2 mapping detect inflammatory response in the renal tissue during ischemia induced acute kidney injury in different mouse strains and predict renal outcome. Biomedicines 2021; 9:1071.","DOI":"10.3390\/biomedicines9081071"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Niendorf T, Pohlmann A, Arakelyan K, et al. How bold is blood oxygenation level-dependent (BOLD) magnetic resonance imaging of the kidney? Opportunities, challenges and future directions. Acta Physiol (Oxf) 2015; 213:19\u201338.","DOI":"10.1111\/apha.12393"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Inoue T, Kozawa E, Okada H, et al. Noninvasive evaluation of kidney hypoxia and fibrosis using magnetic resonance imaging. J Am Soc Nephrol 2011; 22:1429\u20131434.","DOI":"10.1681\/ASN.2010111143"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Pruijm M, Mendichovszky IA, Liss P, et al. Renal blood oxygenation level-dependent magnetic resonance imaging to measure renal tissue oxygenation: A statement paper and systematic review. Nephrol Dial Transplant 2018; 33(suppl_2):ii22\u2013ii28.","DOI":"10.1093\/ndt\/gfy243"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Pruijm M, Hofmann L, Vogt B, et al. Renal tissue oxygenation in essential hypertension and chronic kidney disease. Int J Hypertens 2013; 2013:696598.","DOI":"10.1155\/2013\/696598"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. Neugarten J, Golestaneh L. Blood oxygenation level-dependent MRI for assessment of renal oxygenation. Int J Nephrol Renovasc Dis 2014; 7:421\u2013435.","DOI":"10.2147\/IJNRD.S42924"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. Lanzman RS, Ljimani A, Pentang G, et al. Kidney transplant: Functional assessment with diffusion-tensor MR imaging at 3T. Radiology 2013; 266:218\u2013225.","DOI":"10.1148\/radiol.12112522"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Hueper K, Hartung D, Gutberlet M, et al. Magnetic resonance diffusion tensor imaging for evaluation of histopathological changes in a rat model of diabetic nephropathy. Invest Radiol 2012; 47:430\u2013437.","DOI":"10.1097\/RLI.0b013e31824f272d"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Liu Z, Xu Y, Zhang J, et al. Chronic kidney disease: Pathological and functional assessment with diffusion tensor imaging at 3T MR. Eur Radiol 2015; 25:652\u2013660.","DOI":"10.1007\/s00330-014-3461-x"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Virgincar RS, Wong AK, Barck KH, et al. Diffusion tensor MRI is sensitive to fibrotic injury in a mouse model of oxalate-induced chronic kidney disease. Am J Physiol Renal Physiol 2024; 327:F235\u2013F244.","DOI":"10.1152\/ajprenal.00099.2024"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Sigmund EE, Vivier PH, Sui D, et al. Intravoxel incoherent motion and diffusion-tensor imaging in renal tissue under hydration and furosemide flow challenges. Radiology 2012; 263:758\u2013769.","DOI":"10.1148\/radiol.12111327"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988; 168:497\u2013505.","DOI":"10.1148\/radiology.168.2.3393671"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Federau C. Measuring perfusion: Intravoxel incoherent motion MR imaging. Magn Reson Imaging Clin N Am 2021; 29:233\u2013242.","DOI":"10.1016\/j.mric.2021.01.003"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. 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Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI. MAGMA 2020; 33:177\u2013195.","DOI":"10.1007\/s10334-019-00790-y"},{"key":"32","doi-asserted-by":"crossref","unstructured":"32. Odudu A, Nery F, Harteveld AA, et al. Arterial spin labelling MRI to measure renal perfusion: A systematic review and statement paper. Nephrol Dial Transplant 2018; 33(suppl_2):ii15\u2013ii21.","DOI":"10.1093\/ndt\/gfy180"},{"key":"33","doi-asserted-by":"crossref","unstructured":"33. Winter JD, St Lawrence KS, Cheng HLM. Quantification of renal perfusion: Comparison of arterial spin labeling and dynamic contrast-enhanced MRI. J Magn Reson Imaging 2011; 34:608\u2013615.","DOI":"10.1002\/jmri.22660"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34. Zhang JL, Lee VS. Renal perfusion imaging by MRI. J Magn Reson Imaging 2020; 52:369\u2013379.","DOI":"10.1002\/jmri.26911"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35. Gillis KA, McComb C, Patel RK, et al. Non-contrast renal magnetic resonance imaging to assess perfusion and corticomedullary differentiation in health and chronic kidney disease. Nephron 2016; 133:183\u2013192.","DOI":"10.1159\/000447601"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36. Cai YZ, Li ZC, Zuo PL, et al. Diagnostic value of renal perfusion in patients with chronic kidney disease using 3D arterial spin labeling. J Magn Reson Imaging 2017; 46:589\u2013594.","DOI":"10.1002\/jmri.25601"},{"key":"37","doi-asserted-by":"crossref","unstructured":"37. Buchanan CE, Mahmoud H, Cox EF, et al. Quantitative assessment of renal structural and functional changes in chronic kidney disease using multi-parametric magnetic resonance imaging. Nephrol Dial Transplant 2020; 35:955\u2013964.","DOI":"10.1093\/ndt\/gfz129"},{"key":"38","doi-asserted-by":"crossref","unstructured":"38. Huang Y, Sadowski EA, Artz NS, et al. 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J Int Med Res 2018; 46:1391\u20131403.","DOI":"10.1177\/0300060517743826"},{"key":"42","doi-asserted-by":"crossref","unstructured":"42. dos Santos EA, Li LP, Ji L, Prasad PV. Early changes with diabetes in renal medullary hemodynamics as evaluated by fiberoptic probes and BOLD magnetic resonance imaging. Invest Radiol 2007; 42:157\u2013162.","DOI":"10.1097\/01.rli.0000252492.96709.36"},{"key":"43","doi-asserted-by":"crossref","unstructured":"43. S\u00f8rensen SS, Gullaksen S, Vernstr\u00f8m L, et al. Evaluation of renal oxygenation by BOLD-MRI in high-risk patients with type 2 diabetes and matched controls. Nephrol Dial Transplant 2023; 38:691\u2013699.","DOI":"10.1093\/ndt\/gfac186"},{"key":"44","doi-asserted-by":"crossref","unstructured":"44. Yin WJ, Liu F, Li XM, et al. Noninvasive evaluation of renal oxygenation in diabetic nephropathy by BOLD-MRI. Eur J Radiol 2012; 81:1426\u20131431.","DOI":"10.1016\/j.ejrad.2011.03.045"},{"key":"45","doi-asserted-by":"crossref","unstructured":"45. Wang ZJ, Kumar R, Banerjee S, Hsu CY. Blood oxygen level-dependent (BOLD) MRI of diabetic nephropathy: Preliminary experience. J Magn Reson Imaging 2011; 33:655\u2013660.","DOI":"10.1002\/jmri.22501"},{"key":"46","doi-asserted-by":"crossref","unstructured":"46. Lu L, Sedor JR, Gulani V, et al. Use of diffusion tensor MRI to identify early changes in diabetic nephropathy. Am J Nephrol 2011; 34:476\u2013482.","DOI":"10.1159\/000333044"},{"key":"47","doi-asserted-by":"crossref","unstructured":"47. Ma J, Cao C, Que C, et al. Prospective evaluation of arterial spin labeling and diffusion tensor imaging-magnetic resonance imaging for the non-invasive assessment of renal allograft dysfunction. Quant Imaging Med Surg 2025; 15:6882\u20136896.","DOI":"10.21037\/qims-2025-604"},{"key":"48","doi-asserted-by":"crossref","unstructured":"48. Chen X, Xiao W, Li X, He J, Huang X, Tan Y. In vivo evaluation of renal function using diffusion weighted imaging and diffusion tensor imaging in type 2 diabetics with normoalbuminuria versus microalbuminuria. Front Med 2014; 8:471\u2013476.","DOI":"10.1007\/s11684-014-0365-8"},{"key":"49","doi-asserted-by":"crossref","unstructured":"49. Wang YT, Yan X, Pu H, Yin LL. In vivo evaluation of early renal damage in type 2 diabetic patients on 3.0 T MR diffusion tensor imaging. World J Radiol 2018; 10:83\u201390.","DOI":"10.4329\/wjr.v10.i8.83"},{"key":"50","doi-asserted-by":"crossref","unstructured":"50. Ye XJ, Cui SH, Song JW, et al. Using magnetic resonance diffusion tensor imaging to evaluate renal function changes in diabetic patients with early-stage chronic kidney disease. Clin Radiol 2019; 74:116\u2013122.","DOI":"10.1016\/j.crad.2018.09.011"},{"key":"51","doi-asserted-by":"crossref","unstructured":"51. Cakmak P, Ya\u011fc\u0131 AB, Dursun B, Herek D, Fenk\u00e7i SM. Renal diffusion-weighted imaging in diabetic nephropathy: Correlation with clinical stages of disease. Diagn Interv Radiol 2014; 20:374\u2013378.","DOI":"10.5152\/dir.2014.13513"},{"key":"52","doi-asserted-by":"crossref","unstructured":"52. Srivastava A, Cai X, Lee J, et al. Kidney functional magnetic resonance imaging and change in eGFR in individuals with CKD. Clin J Am Soc Nephrol 2020; 15:776\u2013783.","DOI":"10.2215\/CJN.13201019"},{"key":"53","doi-asserted-by":"crossref","unstructured":"53. Mora-Guti\u00e9rrez JM, Garcia-Fernandez N, Slon Roblero MF, et al. Arterial spin labeling MRI is able to detect early hemodynamic changes in diabetic nephropathy. J Magn Reson Imaging 2017; 46:1810\u20131817.","DOI":"10.1002\/jmri.25717"},{"key":"54","doi-asserted-by":"crossref","unstructured":"54. Artz NS, Sadowski EA, Wentland AL, et al. Arterial spin labeling MRI for assessment of perfusion in native and transplanted kidneys. 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Berchtold L, Friedli I, Crowe LA, et al. Validation of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for kidney fibrosis detection: A cross-sectional study. Nephrol Dial Transplant 2020; 35:937\u2013945.","DOI":"10.1093\/ndt\/gfy389"},{"key":"59","doi-asserted-by":"crossref","unstructured":"59. Noda Y, Ito K, Kanki A, et al. Measurement of renal cortical thickness using noncontrast-enhanced steady-state free precession MRI with spatially selective inversion recovery pulse: Association with renal function. J Magn Reson Imaging 2015; 41:1615\u20131621.","DOI":"10.1002\/jmri.24719"},{"key":"60","doi-asserted-by":"crossref","unstructured":"60. Noda Y, Kanki A, Yamamoto A, et al. Age-related change in renal corticomedullary differentiation: Evaluation with noncontrast-enhanced steady-state free precession (SSFP) MRI with spatially selective inversion pulse using variable inversion time. 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