脑白质轴突内扩散系数的研究
Intra-axonal Diffusivity in Brain White Matter
摘要：
生物物理建模是使用扩散加权MRI评估生物组织的细胞结构的中介。然而，这是微结构MRI的瓶颈。除了扩散的复杂性之外，目前的发展受到生物物理模型严重依赖于仍然未知并且必须在体内测量的不同细胞区室的扩散特异性特性的事实的阻碍。通过直接拟合获得这些参数受到似然函数的退化景观的阻碍，特别是，针对多个扩散方向和中等扩散加权强度获得的信号不足以估计这些参数：不同的参数星座同样很好地解释了信号。本研究的目的是测量白质模型的中心参数，即正常人脑轴突内水扩散率。由于（i）轴突内和轴突外水室的存在和（ii）轴突的取向分散，该参数的正确估计是复杂的。我们的测量涉及到一个有效的抑制外轴突空间和所有的细胞过程以外的一个狭窄的圆锥体周围的主要纤维方向。这是通过使用平面水流动性过滤器实现的--一种强扩散加权，其抑制来自在横向于纤维束的平面中移动的所有分子的信号。在平面滤波器之后，使用线性和各向同性加权测量剩余隔室中的扩散率。我们发现具体的平均轴突内扩散率$D_0 = 2.25\pm 0.03{\,\rm μm^2/ms}$的应用梯度的时间。外推到无限扩散时间得到$D_\infty \approx 2.0{\,\rm μm^2/ms}$。这一结果对扩散加权MRI的生物物理建模的参数选择施加了很大的限制。
英文摘要：
Biophysical modeling is the mediator of evaluating the cellular structure of biological tissues using diffusion-weighted MRI. It is however the bottleneck of microstructural MRI. Beyond the complexity of diffusion, the current development is hindered by the fact that biophysical models heavily rely on diffusion-specific properties of diverse cellular compartments that are still unknown and must be measured in vivo. Obtaining such parameters by straightforward fitting is hindered by the degenerated landscape of the likelihood functions, in particular, the signal obtained for multiple diffusion directions and moderate diffusion weighting strength is not enough to estimate these parameters: different parameter constellations explain the signal equally well. The aim of this study is to measure the central parameter of white matter models, namely the intra-axonal water diffusivity in the normal human brain. Proper estimation of this parameter is complicated due to (i) the presence of both intra- and extra-axonal water compartments and (ii) the orientation dispersion of axons. Our measurement involves an efficient suppression of 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 -- a strong diffusion weighting that suppresses signal from all molecules that are mobile in the plane transverse to the fiber bundle. Following the planar filter, the diffusivity in the remaining compartment is measured using linear and isotropic weighting. We find the specifically averaged intra-axonal diffusivity $D_0 = 2.25\pm 0.03{\,\rm μm^2/ms}$ for the timing of the applied gradients. Extrapolation to the infinite diffusion time gives $D_\infty \approx 2.0{\,\rm μm^2/ms}$. This result imposes a strong limitation on the parameter selection for biophysical modeling of diffusion-weighted MRI.
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