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Diffusion kurtosis imaging: research advances in brain tumors
LIANG Xiao  SHI Wenwei  TAN Yan 

Cite this article as: Liang X, Shi WW, Tan Y. Diffusion kurtosis imaging: research advances in brain tumors. Chin J Magn Reson Imaging, 2020, 11(3): 221-223. DOI:10.12015/issn.1674-8034.2020.03.013.


[Abstract] Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI) technology, which can quantify the non-gaussian diffusion characteristics of water molecules in tissues. It is more sensitive to the complex microstructure of human tissues than other technologies, and it can provide more information about structural changes and reflect the pathophysiological changes of diseases, which is conducive to early detection of diseases and early guidance of clinical decision-making. At present, DKI technology has been widely applied in clinical diseases and scientific research. This paper mainly reviews the research progress of DKI in brain tumors at home and abroad.
[Keywords] magnetic resonance imaging；diffusion kurtosis imaging；brain tumor

LIANG Xiao College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

SHI Wenwei College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

TAN Yan* Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China

*Corresponding to: Tan Y, E-mail: tanyan123456@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by the National Natural Science Foundation No. 81701681
Received  2019-10-11
Accepted  2020-02-12
DOI: 10.12015/issn.1674-8034.2020.03.013
Cite this article as: Liang X, Shi WW, Tan Y. Diffusion kurtosis imaging: research advances in brain tumors. Chin J Magn Reson Imaging, 2020, 11(3): 221-223. DOI:10.12015/issn.1674-8034.2020.03.013.

[1]
Hui ES, Cheung MM, Qi L, et al. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage, 2008, 42(1): 122-34.
[2]
Veraart J, Van Hecke W, Sijbers J. Constrained maximum likelihood estimation of the diffusion kurtosis tensor using a Rician noise model. Magn Reson Med, 2011, 66(3): 678-86.
[3]
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): 1432-40.
[4]
Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed, 2010, 23(7): 698-710.
[5]
Yokosawa S, Sasaki M, Bito Y, et al. Optimization of scan parameters to reduce acquisition time for diffusion kurtosis imaging at 1.5 T. Magn Reson Med Sci, 2016, 15(1): 41-8.
[6]
Hui ES, Cheung MM, Qi L, et al. Advanced MR diffusion characterization of neural tissue using directional diffusion kurtosis analysis. Conf Proc IEEE Eng Med Biol Soc, 2008, 2008: 3941-3944.
[7]
Jackson RJ, Fuller GN, Abi-Said D, et al. Limitations of stereotactic biopsy in the initial management of gliomas. Neuro Oncol, 2001, 3(3): 193-200.
[8]
Raab P, Hattingen E, Franz K, et al. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology, 2010, 254(3): 876-81.
[9]
Van Cauter S, Veraart J, Sijbers J, et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology, 2012, 263(2): 492-501.
[10]
Jiang R, Jiang J, Zhao L, et al. Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget, 2015, 6(39): 42380-93.
[11]
Zhao J, Wang YL, Li XB, et al. Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status. J Neurooncol, 2019, 141(1): 195-203.
[12]
Falk DA, Nilsson M, Van Westen D, et al. Glioma grade discrimination with MR diffusion kurtosis imaging: a meta-analysis of diagnostic accuracy. Radiology, 2018, 287(1): 119-127.
[13]
Claus EB, Bondy ML, Schildkraut JM, et al. Epidemiology of intracranial meningioma. Neurosurgery, 2005, 57(6): 1088-1095.
[14]
Xing F, Tu N, Koh TS, et al. MR diffusion kurtosis imaging predicts malignant potential and the histological type of meningioma. Eur J Radiol, 2017, 95: 286-292.
[15]
Rauscher A, Sedlacik J, Barth M, et al, Reichenbach jr. magnetic susceptibility-weighted MR phase imaging of the human brain. AJNR Am J Neuroradiol, 2005, 26(4): 736-742.
[16]
Ostrom QT, Bauchet L, Davis FG, et al. The epidemiology of glioma in adults: a "state of the science" review. Neuro Oncol, 2014, 16(7): 896-913.
[17]
Tan Y, Wang XC, Zhang H, et al. Differentiation of high-grade-astrocytomas from solitary-brain-metastases: Comparing diffusion kurtosis imaging and diffusion tensor imaging. Eur J Radiol, 2015, 84(12): 2618-24.
[18]
张帆，张辉，王效春，等. DKI在高级别胶质瘤与单发脑转移瘤鉴别诊断的价值.临床放射学杂志, 2015, 34(7): 1057-1060.
[19]
谭朝元，杨晶，严开心，等．扩散峰度成像对高级别胶质瘤及单发脑转移瘤的鉴别诊断价值．放射学实践, 2017, 32(3): 218-222.
[20]
龙拥军，卓丽华，张海容，等. MR扩散峰度成像鉴别诊断高级别胶质瘤与单发脑转移瘤的meta分析．肿瘤影像学, 2019, 28(2): 106-111.
[21]
Pang H, Ren Y, Dang X, et al. Diffusional kurtosis imaging for differentiating between high-grade glioma and primary central nervous system lymphoma. J Magn Reson Imaging, 2016, 44(1): 30-40.
[22]
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.
[23]
Diamandis P, Aldape KD. Insights from molecular profiling of adult glioma. J Clin Oncol, 2017, 35(21): 2386-2393.
[24]
Parsons DW, Jones S, Zhang X, et al. An integrated genomic analysis of human glioblastoma multiforme. Science, 2008, 321(5897): 1807-12.
[25]
Hartmann C, Hentschel B, Wick W, et al. Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol, 2010, 120(6): 707-718.
[26]
Van den Bent MJ, Hartmann C, Preusser M, et al. Interlaboratory comparison of IDH mutation detection. J Neurooncol, 2013, 112(2): 173-178.
[27]
Hempel JM, Bisdas S, Schittenhelm J, et al. In vivo molecular profiling of human glioma using diffusion kurtosis imaging. J Neurooncol, 2017, 131(1): 93-101.
[28]
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.
[29]
Wang XC, 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 Monit, 2018, 24: 8822-8830.
[30]
Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain. AJR Am J Roentgenol, 2014, 202: W26-33.
[31]
Tietze A, Hansen MB, Ostergaard L, et al. Mean diffusional kurtosis in patients with glioma: initial results with a fast imaging method in a clinical setting. AJNR Am J Neuroradiol, 2015, 36: 1472-1478.
[32]
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): 3680-3688.
[33]
Ion-Mărgineanu A, Van Cauter S, Sima DM, et al. Classifying glioblastoma multiforme follow-up progressive vs. responsive forms using multi-parametric MRI features. Front Neurosci, 2016, 10: 615.
[34]
Bisdas S, Shen H, Thust S, et al. Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study. Sci Rep, 2018, 8(1): 6108.

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