Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Centre for Medical Image Computing, University College London, London, UK.
Center of Functionally Integrative Neuroscience (CFIN), Clinical Institute, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
Neuroimage. 2018 Dec;183:934-949. doi: 10.1016/j.neuroimage.2018.08.034. Epub 2018 Aug 23.
Microscopic diffusion anisotropy (μA) has been recently gaining increasing attention for its ability to decouple the average compartment anisotropy from orientation dispersion. Advanced diffusion MRI sequences, such as double diffusion encoding (DDE) and double oscillating diffusion encoding (DODE) have been used for mapping μA, usually using measurements from a single b shell. However, the accuracy of μA estimation vis-à-vis different b-values was not assessed. Moreover, the time-dependence of this metric, which could offer additional insights into tissue microstructure, has not been studied so far. Here, we investigate both these concepts using theory, simulation, and experiments performed at 16.4T in the mouse brain, ex-vivo. In the first part, simulations and experimental results show that the conventional estimation of microscopic anisotropy from the difference of D(O)DE sequences with parallel and orthogonal gradient directions yields values that highly depend on the choice of b-value. To mitigate this undesirable bias, we propose a multi-shell approach that harnesses a polynomial fit of the signal difference up to third order terms in b-value. In simulations, this approach yields more accurate μA metrics, which are similar to the ground-truth values. The second part of this work uses the proposed multi-shell method to estimate the time/frequency dependence of μA. The data shows either an increase or no change in μA with frequency depending on the region of interest, both in white and gray matter. When comparing the experimental results with simulations, it emerges that simple geometric models such as infinite cylinders with either negligible or finite radii cannot replicate the measured trend, and more complex models, which, for example, incorporate structure along the fibre direction are required. Thus, measuring the time dependence of microscopic anisotropy can provide valuable information for characterizing tissue microstructure.
微观扩散各向异性（μA）因其能够将平均隔室各向异性与方向分散解耦而受到越来越多的关注。先进的扩散 MRI 序列，如双扩散编码（DDE）和双振荡扩散编码（DODE），已被用于映射 μA，通常使用单个 b 壳的测量值。然而，不同 b 值下 μA 估计的准确性尚未得到评估。此外，到目前为止，还没有研究这种度量的时间依赖性，它可以提供对组织微观结构的额外见解。在这里，我们使用理论、模拟和在小鼠大脑中的 16.4T 进行的实验来研究这两个概念，这些实验都是离体进行的。在第一部分中，模拟和实验结果表明，从平行和正交梯度方向的 D(O)DE 序列的差异中常规估计微观各向异性的方法得出的值高度依赖于 b 值的选择。为了减轻这种不理想的偏差，我们提出了一种多壳方法，该方法利用 b 值的三次多项式拟合信号差异。在模拟中，该方法产生了更准确的 μA 度量值，与真实值相似。这项工作的第二部分使用所提出的多壳方法来估计 μA 的时间/频率依赖性。数据显示，在白质和灰质中，μA 随频率增加或不增加，这取决于感兴趣的区域。当将实验结果与模拟结果进行比较时，结果表明，简单的几何模型，如具有可忽略或有限半径的无限圆柱，无法复制所测量的趋势，需要更复杂的模型，例如，纳入纤维方向的结构。因此，测量微观各向异性的时间依赖性可以为组织微观结构的特征提供有价值的信息。
