Share：
Share this content in WeChat
X
[Chinese][PDF]87087
• Clinical Article •
Preliminary research of the classification of the brain acute stroke by diffusion kurtosis imaging parameters
CHEN Fang  YANG Yong-gui  LIU Xin  GUO Gang 

DOI:10.12015/issn.1674-8034.2018.03.002.


[Abstract] Objective: To classify the acute stroke lesions according to the diffusion kurtosis imaging (DKI) parameters and discuss the significance of the classification in diagnosis and prognosis.Materials and Methods: We chose 46 acute stroke lesions in brain and divided into 4 types. MK, Ka and Kr show hyper-intensity in Type Ⅰ. MK, Ka and Kr show isointensity or hypo-intensity in Type Ⅱ. MK, Ka and Kr show mixed signal intensity in Type Ⅲ. The ranges or intensity of MK, Ka and Kr are diverse in Type IV. The difference of DKI parameters between four types was accessed by one-way analysis of variance. We chose one case of each type to analyze the prognosis.Results: It shows that MK%, Ka% and Kr% of four types have a significant difference. Other parameters have no significant difference. Type Ⅰ may indicate the bad prognosis. Type Ⅱ and Type Ⅲ may indicate better prognosis, the volume of lesions may decrease.Conclusions: To some degree, the classification of acute stroke lesion by DKI parameters has significance in diagnosis, treatment and prognosis.
[Keywords] Diffusion kurtosis imaging；Acute stroke；Classification；Diffusion weighted imaging

CHEN Fang Department of Radiology, the Second Hospital of Xiamen, Xiamen 361012, China

YANG Yong-gui Department of Radiology, the Second Hospital of Xiamen, Xiamen 361012, China

LIU Xin Department of Radiology, the Second Hospital of Xiamen, Xiamen 361012, China

GUO Gang* Department of Radiology, the Second Hospital of Xiamen, Xiamen 361012, China

*Correspondence to: Guo G, Email: guogangxm@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This study was supported by Xiamen Program of Joint Effort for Tackling Major Diseases No. 3502Z20149032 Xiamen Science and Technology Plan Project No. 3502Z20144050
Received  2017-11-10
Accepted  2018-01-30
DOI: 10.12015/issn.1674-8034.2018.03.002
DOI:10.12015/issn.1674-8034.2018.03.002.

[1]
Chong J, Lu D, Aragao F, et al. Diffusion-weighted MR of acute cerebral infarction: comparison of data processing methods. AJNR Am J Neuroradiol, 1998, 19(9): 1733-1739.
[2]
Meng KL, Liu PH, Xu ZX, et al. Implications of the apparent diffusion coefficient (ADC) in periodization of the acute brain stroke. Hebei Med J, 2010, 32(20): 2849-2850.孟凯龙，刘彭华，许忠新，等．表观弥散系数对脑梗死分期的指导意义．河北医药, 2010, 32(20): 2849-2850.
[3]
Kidwell CS, Saver JL, Mattiello J, et al. Thrombolytic reversal of acute human cerebral ischemic injury shown by diffusion/perfusion magnetic resonance imaging. Ann Neurol, 2000, 47(4): 462-469.
[4]
Veraart J, Van HW, Sijbers J. Constrained maximum likelihood estimation of the diffusion kurtosis tensor using a Rician noise model. Magn Reson Med, 2011, 66(3): 678-686.
[5]
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.
[6]
Wu EX, Cheung MM. MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed, 2010, 23(7): 836-848.
[7]
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.
[8]
Lanzafame S, Giannelli M, Garaci F, et al. Differences in Gaussian diffusion tensor imaging and non-Gaussian diffusion kurtosis imaging model-based estimates of diffusion tensor invariants in the human brain. Med Phys, 2016, 43(5): 2464.
[9]
Shao HY. Investigating the effect of aging on the microstructure of brain with novel diffusion imaging techniques. Chin J Magn Reson Imaging, 2016, 7(3): 218-221.邵函钰．利用新型扩散成像技术研究年老对大脑微观结构的影响．磁共振成像, 2016, 7(3): 218-221.
[10]
Guo YL, Li SJ, Zhang ZP, et al. Parameters of diffusional kurtosis imaging for the diagnosis of acute cerebral infarction in different brain regions. Exp Ther Med, 2016, 12(2): 933-938.
[11]
Astrup J, Symon L, Branston N, et al. Cortical evoked potential and extracellular K+ and H+ at critical levels of brain ischemia. Stroke, 1977, 8(1): 51.
[12]
Sharp FR, LuA, Tang Y, et al. Multiple molecular penumbras after focal cerebral ischemia. J Cereb Blood Flow Metab, 2000, 20(7): 1011-1032.
[13]
Sun HZ, Zhang H, Ni HY, et al. The comparative study of ischemic stroke early DKI, DWI lesions predicted the ifnal transition. Chin J Magn Reson Imaging, 2016, 7(10): 732-737.孙海珍，张慧，倪红艳，等．缺血性脑卒中早期DKI、DWI预测病灶最终转变的对比研究．磁共振成像, 2016, 7(10):732-737.
[14]
He H, Gao PY. Comparative study of multi-band EPI diffusion kurtosis imaging and diffusion weighted imaging in identifying infarct lesion in acute ischemic stroke. Chin J Stroke, 2016, 11(3): 184-190.何欢，高培毅．磁共振多层并采扩散峰度成像与传统扩散加权成像识别急性缺血性卒中梗死核心的研究．中国卒中杂志, 2016, 11(3): 184-190.
[15]
Cheung JS, Wang E, Lo EH, et al. Stratification of heterogeneous diffusion MRI ischemic lesion with kurtosis imaging: evaluation of mean diffusion and kurtosis MRI mismatch in an animal model of transient focal ischemia. Stroke, 2012, 43(8): 2252.
[16]
Zhang S, Yao Y, Shi J, et al. The temporal evolution of diffusional kurtosis imaging in an experimental middle cerebral artery occlusion (MCAO) model. J Magn Reson Imaging, 2016, 34(7): 889-895.
[17]
Jensen JH, Falangola MF, Hu C, et al. Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction. NMR Biomed, 2011, 24(5): 452-457.
[18]
Spampinato MV, Chan C, Jensen JH, et al. Diffusional kurtosis imaging and motor outcome in acute ischemic stroke. AJNR Am J Neuroradiol, 2017, 38(7): 1328.

PREV The correlation between brain MR amide proton imaging and scores of clinical psychometric scales in patients with Alzheimer,s disease
NEXT The alterations of resting-state brain functional connectivity after physiological food intake
  


LINK


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