Hui Edward S, Russell Glenn G, Helpern Joseph A, Jensen Jens H
Department of Diagnostic Radiology, The University of Hong Kong, Pokfulam, Hong Kong.
Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA.
Neuroimage. 2015 Feb 1;106:391-403. doi: 10.1016/j.neuroimage.2014.11.015. Epub 2014 Nov 15.
A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.
提出了一种计算框架,用于将大脑中水分子扩散的峰度张量与大脑微观结构的组织模型相关联。假设组织模型由不发生交换的隔室组成,这些隔室可能与由细胞膜分隔的各种微观结构空间相关。在每个隔室内,水分子扩散被视为高斯分布,尽管整个系统的扩散通常是非高斯的。确定模型参数以最小化测量的峰度张量与模型峰度张量之间差异的弗罗贝尼乌斯范数。这个框架被称为神经扩散组织的峰度分析(KANDO),可用于帮助对峰度张量提供的信息进行生物物理学解释。此外,KANDO与扩散峰度成像相结合,可以为开发涉及组织微观结构改变的神经病理学候选生物标志物提供一种实用方法。使用从健康人类受试者获得的数据,对简单的白质和灰质组织模型展示了KANDO。