Department of Diagnostic Radiology, University Medical Center, University of Freiburg, Freiburg, 79106, Germany.
Department of Diagnostic Radiology, University Medical Center, University of Freiburg, Freiburg, 79106, Germany.
Neuroimage. 2019 Apr 1;189:543-550. doi: 10.1016/j.neuroimage.2019.01.015. Epub 2019 Jan 16.
Biophysical modeling lies at the core of evaluating tissue cellular structure using diffusion-weighted MRI, albeit with shortcomings. The challenges lie not only in the complexity of the diffusion phenomenon, but also in the need to know the diffusion-specific properties of diverse cellular compartments in vivo. The likelihood function obtained from the commonly acquired Stejskal-Tanner diffusion-weighted MRI data is degenerate with different parameter constellations explaining the signal equally well, thereby hindering an unambiguous parameter estimation. The aim of this study is to measure the intra-axonal water diffusivity which is one of the central parameters of white matter models. Estimating intra-axonal diffusivity is complicated by (i) the presence of other compartments, and (ii) the orientation dispersion of axons. Our measurement involves an efficient signal suppression of water in extra-axonal space and all cellular processes oriented outside a narrow cone around the principal fiber direction. This is achieved using a planar water mobility filter that suppresses signal from all molecules that are mobile in the plane transverse to the fiber bundle. After the planar filter, the diffusivity of the remaining intra-axonal signal is measured using linear and spherical diffusion encoding. We find the average intra-axonal diffusivity D=2.25±0.03μm/ms for the timing of the applied gradients, which gives D≈2.0μm/ms when extrapolated to infinite diffusion time. The result imposes a strong limitation on the parameter selection for biophysical modeling of diffusion-weighted MRI.
生物物理建模是评估组织细胞结构的核心，使用扩散加权 MRI，但存在一些缺点。挑战不仅在于扩散现象的复杂性，还在于需要了解体内不同细胞区室的扩散特性。从通常获取的 Stejskal-Tanner 扩散加权 MRI 数据获得的似然函数在不同的参数组合下是退化的，这些参数组合同样可以很好地解释信号，从而阻碍了明确的参数估计。本研究旨在测量轴内水扩散系数，这是白质模型的核心参数之一。估计轴内扩散系数很复杂，原因有二：（i）存在其他区室，（ii）轴突的方向分散。我们的测量方法涉及到一种有效的信号抑制技术，用于抑制细胞外空间和所有沿主纤维方向周围狭窄圆锥体外的所有细胞过程中的水信号。这是通过使用平面水流动性滤波器来实现的，该滤波器可以抑制与纤维束垂直的平面上所有可移动分子的信号。在平面滤波器之后，使用线性和球形扩散编码来测量剩余的轴内信号的扩散系数。我们发现，对于应用梯度的时间，平均轴内扩散系数 D=2.25±0.03μm/ms，当外推到无限扩散时间时，D≈2.0μm/ms。该结果对扩散加权 MRI 生物物理建模的参数选择施加了严格的限制。
