UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
Neuroimage. 2021 Nov 15;242:118445. doi: 10.1016/j.neuroimage.2021.118445. Epub 2021 Aug 8.
Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter.
使用扩散加权 MRI 和多维扩散编码进行微观扩散各向异性成像，是量化神经组织临床和科学相关微观结构特性的一种很有前途的方法。已经提出了几种估计微观各向异性分数（µFA）的方法，µFA 是微观扩散各向异性的归一化度量，但迄今为止，这些方法之间的差异还没有得到太多关注。在这项研究中，使用成像实验和模拟评估了使用 q 空间轨迹编码和不同信号模型估计 µFA 的准确性和精度。对三名健康志愿者和一个微纤维模型进行了成像，使用了五个非零 b 值和梯度波形来编码线性和球形 b 张量。由于在成像实验中未知µFA 的真实值，因此使用模拟真实值已知的类神经纤维的蒙特卡罗随机游走模拟进行了模拟。此外，通过使用具有相似功率谱的调谐波形以及不同于 q 空间轨迹编码的三重扩散编码重复模拟，量化了由于扩散随时间变化而导致的参数偏差，三重扩散编码与 q 空间轨迹编码不同，它不基于扩散随时间不变的假设。粉末平均信号的截断累积展开、伽马分布扩散系数假设和 q 空间轨迹成像（将截断累积展开推广到单个信号）被用于估计µFA。伽马分布扩散系数假设导致的µFA 值始终大于二阶累积展开，在整个大脑上的平均值高出 0.1。在模拟中，广义累积展开提供了最准确的估计。重要的是，尽管所有研究的方法都因扩散随时间变化而导致µFA 产生显著高估，但模拟表明，µFA 的偏差小于 0.1。
