Share：
Share this content in WeChat
X
[Chinese][PDF]98593
• Review •
Intravoxel incoherent motion imaging: research advances in brain tumors
LI Junjie  ZHANG Hui 

Cite this article as: Li JJ, Zhang H. Intravoxel incoherent motion imaging: research advances in brain tumors[J]. Chin J Magn Reson Imaging, 2021, 12(3): 82-84. DOI:10.12015/issn.1674-8034.2021.03.019.


[Abstract] Intravoxel incoherent motion (IVIM) imaging has become an important supplement to conventional brain tumor imaging in recent years. It can obtain the information of brain tumor diffusion and perfusion at the same time, which is conducive to a more comprehensive understanding of tumor physiological and pathological changes and tumor microenvironment. Recently, IVIM has achieved certain results in preoperative diagnosis, grading diagnosis, genotype monitoring and prognosis evaluation of glioma. This article will review the basic principle of IVIM and its clinical application in brain tumors.
[Keywords] intravoxel incoherent motion；brain tumor；magnetic resonance imaging；diffusion weighted imaging

LI Junjie1   ZHANG Hui2*  

1 Department of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

2 Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China

Zhang H, E-mail: zhanghui_mr@163.com

Conflicts of interest   None.

Received  2020-10-30
Accepted  2021-01-21
DOI: 10.12015/issn.1674-8034.2021.03.019
Cite this article as: Li JJ, Zhang H. Intravoxel incoherent motion imaging: research advances in brain tumors[J]. Chin J Magn Reson Imaging, 2021, 12(3): 82-84. DOI:10.12015/issn.1674-8034.2021.03.019.

1
Le Bihan D, Breton E, Lallemand D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology, 1986,161(2): 401-407. DOI: 10.1148/radiology.161.2.3763909
2
Shim WH, Kim HS, Choi CG, et al. Comparison of apparent diffusion coefficient and intravoxel incoherent motion for differentiating among glioblastoma, metastasis, and lymphoma focusing on diffusion-related parameter. PloS One, 2015, 10(7): e0134761. DOI: 10.1371/journal.pone.0134761
3
Yamashita K, Hiwatashi A, Togao O, et al. Diagnostic utility of intravoxel incoherent motion mr imaging in differentiating primary central nervous system lymphoma from glioblastoma multiforme. J Mag Reson Imaging, 2016, 44(5): 1256-1261. DOI: 10.1002/jmri.25261
4
Suh CH, Kim HS, Lee SS, et al. Atypical imaging features of primary central nervous system lymphoma that mimics glioblastoma: utility of intravoxel incoherent motion MR imaging. Radiology, 2014, 272(2): 504-513. DOI: 10.1148/radiol.14131895
5
Song SS, Zhao JP, Ma MG, et al. Intravoxel incoherent motion DWI in differential diagnosis of high-grade gliomas and metastasis. Chin J Med Imaging Technol, 2018, 34(6): 826-830. DOI: 10.13929/j.1003-3289.201709136
6
Bisdas S, Koh TS, Roder C, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: feasibility of the method and initial results. Neuroradiology, 2013, 55(10): 1189-1196. DOI: 10.1007/s00234-013-1229-7
7
Catanese A, Malacario F, Cirillo L, et al. Application of intravoxel incoherent motion (IVIM) magnetic resonance imaging in the evaluation of primitive brain tumours. Neuroradiology J, 2018, 31(1): 4-9. DOI: 10.1177/1971400917693025
8
Federau C, Meuli R, O'brien K, et al. Perfusion measurement in brain gliomas with intravoxel incoherent motion MRI. AJNR Am J Neuroradiology, 2014, 35(2): 256-262. DOI: 10.3174/ajnr.A3686
9
Shen N, Zhao L, Jiang J, et al. Intravoxel incoherent motion diffusion-weighted imaging analysis of diffusion and microperfusion in grading gliomas and comparison with arterial spin labeling for evaluation of tumor perfusion. J Magn Reson Imaging, 2016, 44(3): 620-632. DOI: 10.1002/jmri.25191
10
Hino T, Togao O, Hiwatashi A, et al. Clinical efficacy of simplified intravoxel incoherent motion imaging using three b-values for differentiating high- and low-grade gliomas. PloS One, 2018, 13(12): e0209796. DOI: 10.1371/journal.pone.0209796
11
Wang X, Chen XZ, Shi L, et al. Glioma grading and IDH1 mutational status: assessment by intravoxel incoherent motion MRI. Clin Radiol, 2019, 74(8): 651.e7-651.e14. DOI: 10.1016/j.crad.2019.03.020
12
Togao O, Hiwatashi A, Yamashita K, et al. Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging. Neuro Oncol, 2016, 18(1): 132-141. DOI: 10.1093/neuonc/nov147
13
Yan LF, Sun YZ, Zhao SS, et al. Perfusion, diffusion, or brain tumor barrier integrity: which represents the glioma features best?Cancer Manag Res, 2019, 11: 9989-10000. DOI: 10.2147/CMAR.S197839
14
Zou T, Yu H, Jiang C, et al. Differentiating the histologic grades of gliomas preoperatively using amide proton transfer-weighted (APTW) and intravoxel incoherent motion MRI. NMR Biomed, 2018, 31(1): 3850. DOI: 10.1002/nbm.3850
15
Cao M, Suo S, Han X, et al. Application of a simplified method for estimating perfusion derived from diffusion-weighted MR imaging in glioma grading. Front Aging Neurosci, 2017, 9: 432. DOI: 10.3389/fnagi.2017.00432
16
Wu WC, Chen YF, Tseng HM, et al. Caveat of measuring perfusion indexes using intravoxel incoherent motion magnetic resonance imaging in the human brain. Eur Radiol, 2015, 25(8): 2485-2492. DOI: 10.1007/s00330-015-3655-x
17
Bai Y, Lin Y, Tian J, et al. Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology, 2016, 278(2): 496-504. DOI: 10.1148/radiol.2015142173
18
Luciani A, Vignaud A, Cavet M, et al. Liver cirrhosis: intravoxel incoherent motion MR imaging: pilot study. Radiology, 2008, 249(3): 891-899. DOI: 10.1148/radiol.2493080080
19
Lu YP, Shek KW, Wen JB, et al. Evaluation parameters between intra-voxel incoherent motion and diffusion-weighted imaging in grading and differentiating histological subtypes of meningioma: a prospective pilot study. J Neurol Sci, 2017, 372: 60-69. DOI: 10.1016/j.jns.2016.11.037
20
Zampini MA, Buizza G, Paganelli C, et al. Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with dynamic susceptibility contrast and intravoxel incoherent motion MRI. Magn Reson Imaging, 2020, 67: 69-78. DOI: 10.1016/j.mri.2019.12.003
21
Bohara M, Nakajo M, Kamimura K, et al. Histological grade of meningioma: prediction by intravoxel incoherent motion histogram parameters. Acad Radiol, 2020, 27(3): 342-353. DOI: 10.1016/j.acra.2019.04.012
22
Liu YQ, Chai RC, Wang YZ, et al. Amino acid metabolism-related gene expression-based risk signature can better predict overall survival for glioma. Cancer Sci, 2019, 110(1): 321-333. DOI: 10.1111/cas.13878
23
Wang X, Cao M, Chen H, et al. Simplified perfusion fraction from diffusion-weighted imaging in preoperative prediction of IDH1 mutation in WHO grade II-III gliomas: comparison with dynamic contrast-enhanced and intravoxel incoherent motion MRI. Radiol Oncol, 2020, 54(3): 301-310. DOI: 10.2478/raon-2020-0037
24
Wu SM, Qiu TM, Yang M, et al. Primary application of non-gaussian diffusion MRI in predicting IDH1 mutation in low-grade gliomas. Chin Comput Med Imag, 2018, 24(5): 410-416. DOI: 10.19627/j.cnki.cn31-1700/th.2018.05.010
25
Smits M, van den Bent MJ. Imaging correlates of adult glioma genotypes. Radiology, 2017, 284(2): 316-331. DOI: 10.1148/radiol.2017151930
26
Tian BW, Yang GQ, Qin JB, et al. Application value of IVIM in predicting MGMT promoter methylation status in high-grade gliomas. Chin J Magn Reson Imaging, 2020, 11(7): 506-510. DOI: 10.12015/issn.1674-8034.2020.07.006
27
Wang CC, Dong HB. Ki-67 labeling index and the grading of cerebral gliomas by using intravoxel incoherent motion diffusion-weighted imaging and three-dimensional arterial spin labeling magnetic resonance imaging. Acta Radiol, 2020, 61(8): 1057-1063. DOI: 10.1177/0284185119891694
28
Ren Y, Pang HP, Feng XY, et al. Non-gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index. Neuroradiology, 2016, 58(2): 121-132. DOI: 10.1007/s00234-015-1606-5
29
Kim HS, Suh CH, Kim N, et al. Histogram analysis of intravoxel incoherent motion for differentiating recurrent tumor from treatment effect in patients with glioblastoma: initial clinical experience. AJNR Am J Neuroradiol, 2014, 35(3): 490-497. DOI: 10.3174/ajnr.A3719
30
Miyoshi F, Shinohara Y, Kambe A, et al. Utility of intravoxel incoherent motion magnetic resonance imaging and arterial spin labeling for recurrent glioma after bevacizumab treatment. Acta Radiol, 2018, 59(11): 1372-1379. DOI: 10.1177/0284185118759707
31
Liao D, Liu YC, Tang B, et al. Comparison of DWI based on monoexponential, biexponential and stretched-exponential models in differentiating tumor recurrence and pseudoprogression of glioblastoma. Chin J Med Imaging Technol, 2019, 35(10): 1450-1455. DOI: 10.13929/j.1003-3289.201903059
32
Puig J, Sánchez-González J, Blasco G, et al. Intravoxel incoherent motion metrics as potential biomarkers for survival in glioblastoma. PloS One, 2016,11(7):e0158887. DOI: 10.1371/journal.pone.0158887
33
Federau C, Cerny M, Roux M, et al. IVIM perfusion fraction is prognostic for survival in brain glioma. Clin Neuroradiol, 2017, 27(4): 485-492. DOI: 10.1007/s00062-016-0510-7
34
Zhu LN, Wu J, Zhang H, et al. The value of intravoxel incoherent motion imaging in predicting the survival of patients with astrocytoma. Acta Radiol, 2020, 284185120926907. DOI: 10.1177/0284185120926907
35
Maximov II, Vellmer S. Isotropically weighted intravoxel incoherent motion brain imaging at 7T. Magn Reson Imaging, 2019, 57: 124-132. DOI: 10.1016/j.mri.2018.11.007

PREV Primary mucosa-associated lymphoid tissue extranodal marginal zone lymphoma of the liver: a case report
NEXT Advances in imaging differentiation of pseudoprogression and recurrence of brain gliomas after treatment
  


LINK


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