Müller Hans-Peter, Kassubek Jan
Department of Neurology, University of Ulm.
J Vis Exp. 2013 Jul 28(77):50427. doi: 10.3791/50427.
Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels. In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.
扩散张量成像(DTI)技术可在体内提供有关脑白质(WM)微观结构过程的信息。目前的应用旨在通过与匹配的对照组进行不同的DTI分析,研究不同脑部疾病,尤其是神经退行性疾病中WM受累模式的差异。DTI数据分析以多种方式进行,即基于区域扩散方向的指标(如分数各向异性(FA))的体素级比较,以及在组水平上进行纤维追踪(FT)并伴有纤维束分数各向异性统计(TFAS),以识别沿WM结构的FA差异,旨在定义组水平上WM改变的区域模式。转换到立体定向标准空间是组研究的先决条件,并且需要进行彻底的数据处理以保留方向间的依赖性。目前的应用展示了在组水平数据分析的空间归一化过程中,用于保留定量和方向信息的优化技术方法。在此基础上,FT技术可应用于组平均数据,以量化FT定义的指标信息。此外,在个体受试者基础上进行纵向分析时,应用DTI方法,即立体定向对齐后FA图的差异,可揭示有关神经疾病进展的信息。通过应用控制消除高噪声水平的梯度方向,可在预处理期间进一步提高基于DTI的结果质量。总之,DTI通过结合基于全脑和基于纤维束的DTI分析,用于定义不同脑部疾病独特的WM病理解剖结构。