Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich, Germany.
European Laboratory for Non-Linear Spectroscopy, University of Florence, Florence, Italy.
Sci Rep. 2022 Mar 14;12(1):4328. doi: 10.1038/s41598-022-08140-0.
The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclinations is more challenging because they are derived from the amplitude of the birefringence signals, which depends e.g. on the amount of nerve fibres. One possibility to improve the accuracy is to consider the average transmitted light intensity (transmittance weighting). The current procedure requires effortful manual adjustment of parameters and anatomical knowledge. Here, we introduce an automated, optimised computation of the fibre inclinations, allowing for a much faster, reproducible determination of fibre orientations in 3D-PLI. Depending on the degree of myelination, the algorithm uses different models (transmittance-weighted, unweighted, or a linear combination), allowing to account for regionally specific behaviour. As the algorithm is parallelised and GPU optimised, it can be applied to large data sets. Moreover, it only uses images from standard 3D-PLI measurements without tilting, and can therefore be applied to existing data sets from previous measurements. The functionality is demonstrated on unstained coronal and sagittal histological sections of vervet monkey and rat brains.
3D 偏光成像（3D-PLI）方法测量组织学脑切片的双折射，以确定神经纤维（髓鞘轴突）的空间走向。虽然可以高精度地确定平面内纤维方向，但计算平面外纤维倾斜度更具挑战性，因为它们是从双折射信号的幅度导出的，而信号幅度取决于神经纤维的数量等因素。提高准确性的一种可能性是考虑平均透射光强度（透射权重）。当前的过程需要费力地手动调整参数和解剖学知识。在这里，我们引入了一种自动优化的纤维倾斜计算方法，允许更快、更可重复地确定 3D-PLI 中的纤维方向。根据髓鞘化程度，算法使用不同的模型（透射加权、未加权或线性组合），可以考虑特定区域的行为。由于算法是并行化和 GPU 优化的，因此可以应用于大型数据集。此外，它仅使用来自标准 3D-PLI 测量的无倾斜图像，因此可以应用于以前测量的现有数据集。该功能在未染色的恒河猴和大鼠脑冠状和矢状组织学切片上得到了验证。
