Diffusion Tensor Imaging of TBI: Potentials and Challenges.
Diffusion Tensor Imaging of TBI: Potentials and Challenges.
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DOI：
10.1097/rmr.0000000000000062
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发表时间：
2015-10
期刊：
影响因子：
--
通讯作者：
Wintermark M
中科院分区：
文献类型：
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作者：
Douglas DB;Iv M;Douglas PK;Anderson A;Vos SB;Bammer R;Zeineh M;Wintermark M
神经影像学在创伤性脑损伤（TBI）的诊断中起着至关重要的作用。弥散张量成像（DTI）是一种先进的磁共振成像技术，能够提供大脑神经解剖连接体的丰富信息。本文的目的是系统地回顾DTI和先进的扩散技术在TBI诊断中的作用，包括扩散峰度成像（DKI）、神经突取向弥散和密度成像、扩散谱成像和q球成像。我们讨论了DTI的临床应用，并回顾了与TBI相关的DTI文献。尽管在过去的20年里，DTI技术和相关的扩散技术不断进步，但DTI技术仅在群体水平上对TBI敏感，没有足够的证据表明DTI在个人水平上起作用。最后，我们讨论了TBI中DTI研究的未来方向，包括机器学习在TBI模式分类中的作用。
Neuroimaging plays a critical role in the setting in traumatic brain injury (TBI). Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging technique that is capable of providing rich information on the brain’s neuroanatomic connectome. The purpose of this article is to systematically review the role of DTI and advanced diffusion techniques in the setting of TBI, including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging, diffusion spectrum imaging, and q-ball imaging. We discuss clinical applications of DTI and review the DTI literature as it pertains to TBI. Despite the continued advancements in DTI and related diffusion techniques over the past 20 years, DTI techniques are sensitive for TBI at the group level only and there is insufficient evidence that DTI plays a role at the individual level. We conclude by discussing future directions in DTI research in TBI including the role of machine learning in the pattern classification of TBI.