Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Magn Reson Med. 2019 Feb;81(2):1247-1264. doi: 10.1002/mrm.27463. Epub 2018 Sep 5.
Time-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the human spinal cord, as its axonal structure is specific and different from the brain.
We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi-shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm ; b = 2855 s mm ), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers.
Both intra-/extra-axonal perpendicular diffusivities and kurtosis excess show time-dependence in our synthetic spinal cord model. This time-dependence is reflected mostly in the intra-axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time-dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time-dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two-compartment SMT metrics. Accounting for large axon calibers improves fitting of multi-compartment models to a minor extent.
Time-dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non-invasive estimation of microstructural properties. The time-dependence of the perpendicular DW signal may feature strong intra-axonal contributions due to large spinal axon caliber. Hence, a popular model known as "stick" (zero-radius cylinder) may be sub-optimal to describe signals from the largest spinal axons.
扩散加权（DW）信号的时间依赖性是其关键特征之一，了解这一点有助于生物物理建模。在这里，我们研究了人类脊髓的时间依赖性，因为其轴突结构是特定的，与大脑不同。
我们使用脊髓白质（WM）（大轴突）和大脑 WM（较小轴突）的合成模型进行蒙特卡罗模拟。此外，我们还研究了来自三名志愿者的颈椎脊髓临床可行的多壳 DW 扫描（b=0；b=711 s mm；b=2855 s mm），使用三个扩散时间（Δ为 29、52 和 76 ms）。
我们的合成脊髓模型中的内/外轴突垂直扩散系数和峰度过剩均表现出时间依赖性。这种时间依赖性主要反映在轴内垂直 DW 信号中，与我们的大脑模型不同，它也表现出强烈的衰减。在存在噪声的情况下，我们的合成脊髓模型中的总 DW 信号的时间依赖性似乎可以检测到，但在大脑中则不行。在 WM 中，我们观察到宏观和微观扩散系数以及扩散峰度、NODDI 和双室 SMT 指标随时间变化。考虑到大轴突的口径，可以在一定程度上改善多室模型的拟合。
在脊髓 WM 中，可以在体内检测到临床上可行的 DW MRI 指标的时间依赖性，从而为非侵入性估计微观结构特性提供了新的机会。垂直 DW 信号的时间依赖性可能由于大的脊髓轴突口径而具有强烈的轴内贡献。因此，一种流行的称为“stick”（零半径圆柱）的模型可能不太适合描述来自最大脊髓轴突的信号。
