Share：
Share this content in WeChat
X
[Chinese][PDF]90072
• Review •
Application progress of magnetic resonance imaging in diabetic kidney disease
WANG Xiao  ZHANG Hao  LU Zhongyan  ZHAI Xiaojing  WANG Junhua 

Cite this article as: Wang X, Zhang H, Lu ZY, et al. Application progress of magnetic resonance imaging in diabetic kidney disease[J]. Chin J Magn Reson Imaging, 2021, 12(4): 118-120. DOI:10.12015/issn.1674-8034.2021.04.030.


[Abstract] Diabetic kidney disease is a chronic microvascular complication that is caused by diabetes and is extremely harmful to the human body. It has complex metabolic disorders and has become the main cause of end-stage renal disease. Early detection, intervention and treatment can effectively improve and delay the renal damage in patients with diabetic nephropathy. The current gold standard for diagnosing diabetic nephropathy is kidney pathological biopsy, but it is difficult to use as a routine examination method due to its self-limiting nature. In recent years, magnetic resonance imaging technology has been widely used in clinics, and has many advances and advantages in the early diagnosis and auxiliary clinical treatment of diabetic nephropathy. This article reviews it.
[Keywords] magnetic resonance imaging；diabetic kidney disease；fat fraction；blood perfusion；elastic hardness；water molecules diffuse；blood oxygen content

WANG Xiao   ZHANG Hao*   LU Zhongyan   ZHAI Xiaojing   WANG Junhua  

Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China

Zhang H, E-mail: zhanghao@lzu.edu.cn

Conflicts of interest   None.

Received  2020-12-11
Accepted  2021-01-28
DOI: 10.12015/issn.1674-8034.2021.04.030
Cite this article as: Wang X, Zhang H, Lu ZY, et al. Application progress of magnetic resonance imaging in diabetic kidney disease[J]. Chin J Magn Reson Imaging, 2021, 12(4): 118-120. DOI:10.12015/issn.1674-8034.2021.04.030.

1
Lin YC, Chang YH, Yang SY, et al. Update of pathophysiology and management of iabetic kidney disease[J]. J Formos Med Assoc, 2018, 117(8): 662-675. DOI: 10.1016/j.jfma.2018.02.007.
2
Ma J. Dixon techniques for water and fat imaging[J]. J Magn Reson Imaging, 2008, 28(3): 543-58. DOI: 10.1002/jmri.21492.
3
Kang BK, Kim M, Song SY, et al. Feasibility of modified Dixon MRI techniques for hepatic fat quantification in hepatic disorders: validation with MRS and histology[J]. Br J Radiol, 2018, 91(1089): 20170378. DOI: 10.1259/bjr.20170378.
4
Hammer S, de Vries AP, de Heer P, et al. Metabolic imaging of human kidney triglyceride content: reproducibility of proton magnetic resonance spectroscopy[J]. PLoS One, 2013, 8(4): e62209. DOI: 10.1371/journal.pone.0062209.
5
Thongnak L, Pongchaidecha A, Lungkaphin A. Renal lipid metabolism and lipotoxicity in diabetes[J]. Am J Med Sci, 2020, 359(2): 84-99. DOI: 10.1016/j.amjms.2019.11.004.
6
Sijens PE, Edens MA, Bakker SJ, et al. MRI-determined fat content of human liver, pancreas and kidney[J]. World J Gastroenterol, 2010, 16(16): 1993-1998. DOI: 10.3748/wjg.v16.i16.1993.
7
Yokoo T, Clark HR, Pedrosa I, et al. Quantification of renal steatosis in type II diabetes mellitus using dixon-based MRI[J]. J Magn Reson Imaging, 2016, 44(5): 1312-1319. DOI: 10.1002/jmri.25252.
8
Peng XG, Bai YY, Fang F, et al. Renal lipids and oxygenation in diabetic mice: noninvasive quantification with MR imaging[J]. Radiology, 2013, 269(3): 748-57. DOI: 10.1148/radiol.13122860.
9
Wang Z, Jiang T, Li J, et al. Regulation of renal lipid metabolism, lipid accumulation, and glomerulosclerosis in FVBdb/db mice with type 2 diabetes[J]. Diabetes, 2005, 54(8): 2328-2335. DOI: 10.2337/diabetes.54.8.2328.
10
Wang YC, Feng Y, Lu CQ, et al. Renal fat fraction and diffusion tensor imaging in patients with early-stage diabetic nephropathy[J]. Eur Radiol, 2018, 28(8): 3326-3334. DOI: 10.1007/s00330-017-5298-6.
11
Hernandez-Garcia L, Lahiri A, Schollenberger J. Recent progress in ASL[J]. Neuroimage, 2019, 187: 3-16. DOI: 10.1016/j.neuroimage.2017.12.095.
12
Conlin CC, Oesingmann N, Bolster B, et al. Renal plasma flow (RPF) measured with multiple-inversion-time arterial spin labeling (ASL) and tracer kinetic analysis: Validation against a dynamic contrast-enhancement method[J]. Magn Reson Imaging, 2017, 37: 51-55. DOI: 10.1016/j.mri.2016.11.010.
13
Wu WC, Su MY, Chang CC, et al. Renal perfusion 3-T MR imaging: a comparative study of arterial spin labeling and dynamic contrast-enhanced techniques[J]. Radiology, 2011, 261(3): 845-853. DOI: 10.1148/radiol.11110668.
14
Winter JD, St Lawrence KS, Cheng HL. Quantification of renal perfusion: comparison of arterial spin labeling and dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2011, 34(3): 608-615. DOI: 10.1002/jmri.22660.
15
Kim DW, Shim WH, Yoon SK, et al. Measurement of arterial transit time and renal blood flow using pseudocontinuous ASL MRI with multiple post-labeling delays: Feasibility, reproducibility, and variation[J]. J Magn Reson Imaging, 2017, 46(3): 813-819. DOI: 10.1002/jmri.25634.
16
Cutajar M, Thomas DL, Banks T, et al. Repeatability of renal arterial spin labelling MRI in healthy subjects[J]. MAGMA, 2012, 25(2): 145-53. DOI: 10.1007/s10334-011-0300-9.
17
Li LP, Tan H, Thacker JM, et al. Evaluation of renal blood flow in chronic kidney disease using arterial spin labeling perfusion magnetic resonance imaging[J]. Kidney Int Rep, 2017, 2(1): 36-43. DOI: 10.1016/j.ekir.2016.09.003.
18
Zhang N, Peng X, Huang XJ, et al. A initial study of diagnostic value of arterial spin labeling MRI in diabetic nephropathy[J]. Chin For Med Res, 2018, 16(12): 4-7. DOI: 10.14033/j.cnki.cfmr.2018.12.002.
19
Mora-Gutiérrez JM, Garcia-Fernandez N, Slon Roblero MF, et al. Arterial spin labeling MRI is able to detect early hemodynamic changes in diabetic nephropathy[J]. J Magn Reson Imaging, 2017, 46(6): 1810-1817. DOI: 10.1002/jmri.25717.
20
Liu B, Liang ML, Zhang JQ, et al. Assessment of renal cortex blood flow with arterial spin labeling MRI in patients with type 2 diabetes[J]. Chin J Med Imaging Technol, 2017, 33(5): 747-751. DOI: 10.13929/j.1003-3289.201608105.
21
Brown RS, Sun MRM, Stillman IE, et al. The utility of magnetic resonance imaging for noninvasive evaluation of diabetic nephropathy[J]. Nephrol Dial Transplant, 2020, 35(6): 970-978. DOI: 10.1093/ndt/gfz066.
22
Sarvazyan AP, Skovaroda AR, Emelianov SY, et al. Biophysical bases of elasticity imaging[M]. New York: Plenum Press, 1995: 223-240.
23
Rockey DC, Bell PD, Hill JA. Fibrosis--a common pathway to organ injury and failure[J]. N Engl J Med, 2015, 372(12): 1138-1149. DOI: 10.1056/NEJMra1300575.
24
Lee CU, Glockner JF, Glaser KJ, et al. MR elastography in renal transplant patients and correlation with renal allograft biopsy: a feasibility study[J]. Acad Radiol, 2012, 19(7): 834-841. DOI: 10.1016/j.acra.2012.03.003.
25
Kirpalani A, Hashim E, Leung G, et al. Magnetic resonance elastography to assess fibrosis in kidney allografts[J]. Clin J Am Soc Nephrol, 2017, 12(10): 1671-1679. DOI: 10.2215/CJN.01830217.
26
Warner L, Yin M, Glaser KJ, et al. Noninvasive in vivo assessment of renal tissue elasticity during graded renal ischemia using MR elastography[J]. Invest Radiol, 2011, 46(8): 509-514. DOI: 10.1097/RLI.0b013e3182183a95.
27
Korsmo MJ, Ebrahimi B, Eirin A, et al. Magnetic resonance elastography noninvasively detects in vivo renal medullary fibrosis secondary to swine renal artery stenosis[J]. Invest Radiol, 2013, 48(2): 61-68. DOI: 10.1097/RLI.0b013e31827a4990.
28
Le Bihan D. What can we see with IVIM MRI?[J]. Neuroimage, 2019, 187: 56-67. DOI: 10.1016/j.neuroimage.2017.12.062.
29
Deng Y, Yang B, Peng Y, et al. Use of intravoxel incoherent motion diffusion-weighted imaging to detect early changes in diabetic kidneys[J]. Abdom Radiol (NY), 2018, 43(10): 2728-2733. DOI: 10.1007/s00261-018-1521-4.
30
Li BD, Wang LH, Chen XY, et al. Diagnostic efficiency of IVIM-DWI in staging of type 2 diabetic nephropathy[J]. Diagn Imaging Interv Radiol, 2018, 27(4): 266-271. DOI: 10.3969/j.issn.1005-8001.2018.04.002.
31
Mogensen CE, Keane WF, Bennett PH, et al. Prevention of diabetic renal disease with special reference to microalbuminuria[J]. Lancet, 1995, 346(8982): 1080-1084. DOI: 10.1016/s0140-6736(95)91747-0.
32
Jiang ZX, Wang Y, Ding JY, et al. Assessment of renal injury in diabetic nephropathy using blood oxygenation level-dependent MRI[J]. Chin J Magn Reson Imaging, 2015, 6(7): 524-528. DOI: 10.3969/j.issn.1674-8034.2015.07.009.
33
Shu S, Wang Y, Zheng M, et al. Hypoxia and hypoxia-inducible factors in kidney injury and repair[J]. Cells, 2019, 8(3): 207. DOI: 10.3390/cells8030207.
34
Wang Q, Guo C, Zhang L, et al. BOLD MRI to evaluate early development of renalinjury in a rat model of diabetes[J]. J Int Med Res, 2018, 46(4): 1391-1403. DOI: 10.1177/0300060517743826.
35
Yin WJ, Liu F, Li XM, et al. Noninvasive evaluation of renal oxygenation in diabetic nephropathy by BOLD-MRI[J]. Eur J Radiol, 2012, 81(7): 1426-1431. DOI: 10.1016/j.ejrad.2011.03.045.

PREV Research progress of DWI-MRI and BOLD-fMRI in visual pathway diseases
NEXT Progress of MRI in evaluating the efficacy of neoadjuvant therapy for rectal cancer
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn