Share:
Share this content in WeChat
X
Review
Research progress of DWI multi-model quantitative technique in glioma
WANG Peng  GAO Yang  WU Qiong  WANG Hongru  WANG Shaoyu  ZHANG Huapeng 

Cite this article as: Wang P, Gao Y, Wu Q, et al. Research progress of DWI multi-model quantitative technique in glioma. Chin J Magn Reson Imaging, 2020, 11(11): 1040-1043. DOI:10.12015/issn.1674-8034.2020.11.019.


[Abstract] Glioma is a kind of nervous system tumor originated from glial cells and neuron cells. Due to the advantages of non-radiation and high resolution of MRI, it is widely used in glioma grading, postoperative survival evaluation and treatment effect. Diffusion weighted magnetic resonance imaging is a technique that can quantify the diffusion of water molecules in micro tissues and show the microstructure changes of pathological tissues. It can be used to display the complex micro pathological changes of glioma, so it has been widely used in recent years. This paper introduces the basic concept of diffusion magnetic resonance imaging and its extended multi-model quantitative technique in clinical application.
[Keywords] magnetic resonance imaging;glioma;intravoxel incoherent motion;diffusion tensor imaging;diffusion kurtosis imaging;mean apparent propagator magnetic resonance imaging

WANG Peng Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China

GAO Yang* Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China

WU Qiong Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China

WANG Hongru Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China

WANG Shaoyu Siemens Healthineers, Shanghai 201318, China

ZHANG Huapeng Siemens Healthineers, Shanghai 201318, China

*Correspondence to: Gao Y, E-mail: 1390903990@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by Inner Mongolia Autonomous Region Science and Technology Plan Project No. 2019GG047
Received  2020-06-05
Accepted  2020-09-28
DOI: 10.12015/issn.1674-8034.2020.11.019
Cite this article as: Wang P, Gao Y, Wu Q, et al. Research progress of DWI multi-model quantitative technique in glioma. Chin J Magn Reson Imaging, 2020, 11(11): 1040-1043. DOI:10.12015/issn.1674-8034.2020.11.019.

[1]
Ostrom QT, Gittleman H, Stetson L, et al. Epidemiology of gliomas. Cancer Treat Res, 2015, 163(3): 1-14. DOI: 10.1007/978-3-319-12048-5_1
[2]
Chen R, Smith-Cohn M, Cohen AL, et al. Glioma Subclassifications and Their Clinical Significance. Neurotherapeutics, 2017, 14(2): 284-297. DOI: 10.1007/s13311-017-0519-x
[3]
Reni M, Mazza E, Zanon S, et al. Central nervous system gliomas. Crit Rev Oncol Hematol, 2017, 113: 213-234. DOI: 10.1016/j.critrevonc.2017.03.021
[4]
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol, 2016, 131(6): 803-820. DOI: 10.1007/s00401-016-1545-1
[5]
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
[6]
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, 2015, 18(1): 132-141. DOI: 10.1093/neuonc/nov147
[7]
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): 651-657. DOI: 10.1016/j.crad.2019.03.020
[8]
王超超,董海波,丁方,等.体素内不相干运动扩散加权成像和三维动脉自旋标记成像评估脑胶质瘤Ki-67标记指数和分级的价值.中华医学杂志, 2019, 99(5): 338-342. DOI: 10.3760/cma.j.issn.0376-2491.2019.05.004
[9]
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
[10]
王伟,杨治花,折虹,等.扩散张量成像定量参数与脑胶质瘤病理参数的相关性.中国老年学杂志, 2017, 37(24): 6173-6175. DOI: 10.3969/j.issn.1005-9202.2017.24.073
[11]
丁治民,翟建,陈基明,等. MRI弥散张量成像各项异性分数定量评估胶质瘤Ki-67标记指数的价值.牡丹江医学院学报, 2019, 40(6): 14-17. DOI: 10.13799/j.cnki.mdjyxyxb.2019.06.005
[12]
Xiong J, Tan W, Wen J, et al. Combination of diffusion tensor imaging and conventional MRI correlates with isocitrate dehydrogenase 1/2 mutations but not 1p/19q genotyping in oligodendroglial tumours. Eur Radiol, 2016, 26(6): 1705-1715. DOI: 10.1007/s00330-015-4025-4
[13]
Tan WL, Huang WY, Yin B, et al. Can diffusion tensor imaging noninvasively detect IDH1 gene mutations in astrogliomas? A Retrospective study of 112 cases. AJNR Am J Neuroradiol, 2014, 35(5): 920-927. DOI: 10.3174/ajnr.A3803
[14]
Augelli R, Ciceri E, Ghimenton C, et al. Magnetic resonance diffusion-tensor imaging metrics in high grade gliomas: correlation with IDH1 gene status in WHO 2016 era. Eur J Radiol, 2019, 116: 174-179. DOI: 10.1016/j.ejrad.2019.04.020
[15]
龚军伟,罗天友,吴少平,等.瘤周水肿区扩散张量成像定量参数在胶质瘤分级中的诊断价值.中国医学影像学杂志, 2018, 26(2): 86-89, 93. DOI: 10.3969/j.issn.1005-5185.2018.02.002
[16]
Metz M, Molina-Romero M, Lipkova J, et al. Predicting glioblastoma recurrence from preoperative MR scans using fractional-anisotropy maps with free-water suppression. Cancers, 2020, 12(3): 728. DOI: 10.3390/cancers12030728
[17]
Jütten K, Mainz V, Gauggel S, et al. Diffusion tensor imaging reveals microstructural heterogeneity of normal-appearing white matter and related cognitive dysfunction in glioma patients. Front Oncol, 2019, 26(9): 536. DOI: 10.3389/fonc.2019.00536
[18]
Costabile JD, Alaswad E, D Souza S, et al. Current applications of diffusion tensor imaging and tractography in intracranial tumor resection. Front Oncol, 201929, 9:426. DOI: 10.3389/fonc.2019.00426
[19]
张琰君,郭娟,袁灿亮,等.磁共振纤维束成像指导的靶区勾画对483例脑胶质瘤放疗疗效的影响.现代肿瘤医学, 2019, 27(11): 2009-2013. DOI: 10.3969/j.issn.1672-4992.2019.11.038
[20]
Azad TD, Duffau H. Limitations of functional neuroimaging for patient selection and surgical planning in glioma surgery. Neurosurg Focus, 2020, 48(2): E12. DOI: 10.3171/2019.11.FOCUS19769
[21]
Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med, 2005, 53(6): 1432-1440. DOI: 10.1002/mrm.20508
[22]
Raab P, Hattingen E, Franz K, et al. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology, 2010, 254(3): 876-881. DOI: 10.1148/radiol.09090819
[23]
王玉亮,赵静,李欣蓓,等.扩散峰度成像在胶质瘤分级和预测细胞增殖中的诊断效能.中国医学影像技术, 2017, 33(2): 177-182. DOI: 10.13929/j.1003-3289.201608068
[24]
Jiang R, Jiang J, Zhao L, et al. Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget, 20158, 6(39): 42380-42393. DOI: 10.18632/oncotarget.5675
[25]
Wang X, Lei Y, Wang L, et al. Diffusion kurtosis imaging reflects glial fibrillary acidic protein (GFAP), topo IIα, and O6-methylguanine-DNA methyltransferase (MGMT) expression in astrocytomas. Med Sci Monitor, 2018, 24: 8822-8830. DOI: 10.12659/MSM.911631
[26]
武文杰,张辉,王效春,等.扩散峰度成像在WHO Ⅱ级脑胶质瘤IDH基因分型的预测研究.磁共振成像, 2018, (10): 742-746. DOI: 10.12015/issn.1674-8034.2018.10.005
[27]
Tan Y, Zhang H, Wang X, et al. Comparing the value of DKI and DTI in detecting isocitrate dehydrogenase genotype of astrocytomas.Clin Radiol, 2019, 74(4): 314-320. DOI: 10.1016/j.crad.2018.12.004
[28]
Zhang J, Jiang J, Zhao L, et al. Survival prediction of high-grade glioma patients with diffusion kurtosis imaging. Am J Transl Res, 2019, 11(6): 3680
[29]
Wang X, Li FY, Wang DW, et al. Diffusion kurtosis imaging combined with molecular markers as a comprehensive approach to predict overall survival in patients with gliomas. Eur J Radiol, 2020, 128: 108985. DOI: 10.1016/j.ejrad.2020.108985
[30]
Özarslan E, Koay CG, Shepherd TM, et al. Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. Neuroimage, 2013, 78: 16-32. DOI: 10.1016/j.neuroimage.2013.04.016
[31]
Karmacharya S, Gagoski B, Ning L, et al. Advanced diffusion imaging for assessing normal white matter development in neonates and characterizing aberrant development in congenital heart disease. NeuroImage, 2018, 19: 360-373. DOI: 10.1016/j.nicl.2018.04.032
[32]
Ma K, Zhang X, Zhang H, et al. Mean apparent propagator-MRI: A new diffusion model which improves temporal lobe epilepsy lateralization. Eur J Radiol, 2020, 126: 108914. DOI: 10.1016/j.ejrad.2020.108914

PREV Alveolar soft part sarcoma with lung metastases: a case report
NEXT Advances in research on resting brain network functional magnetic resonance imaging for PD with cognitive impairmente
  



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