Jespersen Sune N, Kroenke Christopher D, Østergaard Leif, Ackerman Joseph J H, Yablonskiy Dmitriy A
Center of Functionally Integrative Neuroscience, Aarhus University Hospital-Arhus Sygehus, Nørrebrogade 44, Building 30, 8000 Arhus C, Denmark.
Neuroimage. 2007 Feb 15;34(4):1473-86. doi: 10.1016/j.neuroimage.2006.10.037. Epub 2006 Dec 22.
Diffusion-weighted imaging (DWI) provides a noninvasive tool to probe tissue microstructure. We propose a simplified model of neural cytoarchitecture intended to capture the essential features important for water diffusion as measured by NMR. Two components contribute to the NMR signal in this model: (i) the dendrites and axons, which are modeled as long cylinders with two diffusion coefficients, parallel (D(L)) and perpendicular (D(T)) to the cylindrical axis, and (ii) an isotropic monoexponential diffusion component describing water diffusion within and across all other structures, i.e., in extracellular space and glia cells. The model parameters are estimated from 153 diffusion-weighted images acquired from a formalin-fixed baboon brain. A close correspondence between the data and the signal model is found, with the model parameters consistent with literature values. The model provides an estimate of dendrite density from noninvasive MR diffusion measurements, a parameter likely to be of value for understanding normal as well as abnormal brain development and function.
扩散加权成像(DWI)提供了一种探测组织微观结构的非侵入性工具。我们提出了一种简化的神经细胞结构模型,旨在捕捉对核磁共振(NMR)测量的水扩散重要的基本特征。该模型中核磁共振信号由两个部分组成:(i)树突和轴突,被建模为具有两个扩散系数的长圆柱体,分别平行于(D(L))和垂直于(D(T))圆柱轴;(ii)一个各向同性单指数扩散成分,描述水在所有其他结构内和跨这些结构的扩散,即在细胞外空间和神经胶质细胞中的扩散。模型参数是根据从福尔马林固定的狒狒大脑获取的153幅扩散加权图像估计的。发现数据与信号模型之间有密切对应关系,模型参数与文献值一致。该模型通过非侵入性磁共振扩散测量提供了树突密度的估计值,这一参数对于理解正常以及异常的脑发育和功能可能具有重要价值。