Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, 660 First Avenue, New York, NY, 10016, USA.
Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Avenue, New York, NY, 10016, USA.
Brain Struct Funct. 2019 May;224(4):1469-1488. doi: 10.1007/s00429-019-01844-6. Epub 2019 Feb 21.
Tissue microstructure modeling of diffusion MRI signal is an active research area striving to bridge the gap between macroscopic MRI resolution and cellular-level tissue architecture. Such modeling in neuronal tissue relies on a number of assumptions about the microstructural features of axonal fiber bundles, such as the axonal shape (e.g., perfect cylinders) and the fiber orientation dispersion. However, these assumptions have not yet been validated by sufficiently high-resolution 3-dimensional histology. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a random-walker (RaW)-based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed by a segmentation based on human annotations initiated with conventional machine-learning-based carving, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated MRI-relevant estimates of size-related parameters (inner axonal diameter, its distribution, along-axon variation, and myelin g-ratio), and orientation-related parameters (fiber orientation distribution and its rotational invariants; dispersion angle). The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, while the reported diameter exceeds those in other mouse brain studies. Furthermore, we calculated how these quantities would evolve in actual diffusion MRI experiments as a function of diffusion time, thereby providing a coarse-graining window on the microstructure, and showed that the orientation-related metrics have negligible diffusion time-dependence over clinical and pre-clinical diffusion time ranges. However, the MRI-measured inner axonal diameters, dominated by the widest cross sections, effectively decrease with diffusion time by ~ 17% due to the coarse-graining over axonal caliber variations. Furthermore, our 3d measurement showed that there is significant variation of the diameter along the axon. Hence, fiber orientation dispersion estimated from MRI should be relatively stable, while the "apparent" inner axonal diameters are sensitive to experimental settings, and cannot be modeled by perfectly cylindrical axons.
扩散磁共振成像信号的组织微观结构建模是一个活跃的研究领域,旨在弥合宏观磁共振分辨率和细胞水平组织结构之间的差距。在神经元组织中,这种建模依赖于对轴突纤维束微观结构特征的许多假设,例如轴突形状(例如,完美的圆柱体)和纤维方向离散度。然而,这些假设尚未通过足够高分辨率的三维组织学得到验证。在这里,我们重建了小鼠大脑胼胝体的连续扫描电子显微镜图像,并引入了基于随机游走(RaW)的算法来快速分割单个轴内空间和有髓轴突的髓鞘。通过基于常规基于机器学习的雕刻的人类注释启动的分割来验证,我们的半自动算法是可靠且耗时更少的。基于分割,我们计算了与 MRI 相关的大小相关参数(内轴直径、其分布、沿轴变化和髓鞘 g 比)和方向相关参数(纤维方向分布及其旋转不变量;离散角)的估计值。报告的离散角与以前的二维组织学研究和扩散磁共振测量一致,而报告的直径超过了其他小鼠脑研究中的直径。此外,我们计算了这些数量在实际扩散 MRI 实验中如何随扩散时间变化而演变,从而为微观结构提供了一个粗粒化窗口,并表明在临床和临床前扩散时间范围内,与方向相关的指标对扩散时间的依赖性可以忽略不计。然而,由于对轴突口径变化的粗粒化,MRI 测量的内轴直径(主要由最宽的横截面决定)实际上随扩散时间有效减少了约 17%。此外,我们的 3d 测量显示,直径在轴突上有显著变化。因此,从 MRI 估计的纤维方向离散度应该相对稳定,而“表观”内轴直径对实验设置敏感,不能用完美的圆柱体轴突来建模。