Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China.
Comput Math Methods Med. 2012;2012:820847. doi: 10.1155/2012/820847. Epub 2012 Aug 29.
Diffusion kurtosis imaging (DKI) is a new diffusion magnetic resonance imaging (MRI) technique to go beyond the shortages of conventional diffusion tensor imaging (DTI) from the assumption that water diffuse in biological tissue is Gaussian. Kurtosis is used to measure the deviation of water diffusion from Gaussian model, which is called non-Gaussian, in DKI. However, the high-order kurtosis tensor in the model brings great difficulties in feature extraction. In this study, parameters like fractional anisotropy of kurtosis eigenvalues (FAek) and mean values of kurtosis eigenvalues (Mek) were proposed, and regional analysis was performed for 4 different tissues: corpus callosum, crossing fibers, thalamus, and cerebral cortex, compared with other parameters. Scatterplot analysis and Gaussian mixture decomposition of different parametric maps are used for tissues identification. Diffusion kurtosis information extracted from kurtosis tensor presented a more detailed classification of tissues actually as well as clinical significance, and the FAek of D-eigenvalues showed good sensitivity of tissues complexity which is important for further study of DKI.
扩散峰度成像（DKI）是一种新的扩散磁共振成像（MRI）技术，旨在弥补传统扩散张量成像（DTI）的不足，因为传统 DTI 假设水在生物组织中的扩散是符合高斯分布的。峰度用于测量水扩散偏离高斯模型的程度，在 DKI 中称为非高斯分布。然而，模型中的高阶峰度张量在特征提取方面带来了很大的困难。在这项研究中，提出了像各向异性峰度本征值的分数（FAek）和峰度本征值的均值（Mek）这样的参数，并与其他参数一起对 4 种不同的组织：胼胝体、交叉纤维、丘脑和大脑皮层进行了区域分析。通过不同参数图的散点图分析和高斯混合分解来进行组织识别。从峰度张量中提取的扩散峰度信息实际上对组织的分类更加详细，并且 D 型本征值的 FAek 对组织复杂性具有很好的敏感性，这对进一步研究 DKI 很重要。
