RESUMEN
A circle/sphere populating method is proposed to generate 2D/3D stochastic microstructures. The proposed method uses circles/spheres as the basic elements and generates microstructure features through the populating process of the circles/spheres. In the populating process, the cores are first generated randomly and circles/spheres start to populate around the cores or the previous generation's circles/spheres. The populating process is controlled by the input parameters including the volume fraction, core number, circle/sphere size distribution, circle/sphere populating distance distribution, circle/sphere populating number, and populating direction constraint angle. The proposed method was compared with the QSGS method and random circle/sphere method in 2-dimensional (2D) and 3-dimensional (3D) cases. The proposed method shows advantages in generating microstructures with clear feature geometries and boundaries. Furthermore, parametric studies are conducted in 2D and 3D to investigate the effect of input parameters on the generated microstructures. With the consideration of circle/sphere spatial distributions, the proposed method can achieve different degrees of feature clustering and agglomerating. A wide range of microstructure morphologies can be achieved by adjusting the input parameters. A more accurate description of the features in the microstructures can be achieved without the involvement of the annealing-based optimization process. As a case study, the proposed method was used to generate sandstone microstructures with different grain size distributions and spatial distributions, and the permeability of generated sandstone was analyzed. Furthermore, the proposed method was applied to generate the microstructure model with a target radial distribution function to demonstrate its computational efficiency by comparing it with the random sphere method and simulated annealing based method.
RESUMEN
Although concussions can result in persistent neurological post-concussion symptoms, they are typically invisible on routine magnetic resonance imaging (MRI) scans. Our study aimed to investigate the use of ultra-high-field diffusion tensor imaging (UHF-DTI) in discerning severity-dependent microstructural changes in the mouse brain following a concussion. Twenty-three C57BL/6 mice were randomly allocated into three groups: the low concussive (LC, n = 9) injury group, the high concussive (HC, n = 6) injury group, and the sham control (SC, n = 7) group. Mice were perfused on day 2 post-injury, and the brains were scanned on a 16.4T MRI scanner with UHF-DTI and neurite orientation dispersion imaging (NODDI). Finite element analysis (FEA) was performed to determine the pattern and extent of the physical impact on the brain tissue. MRI findings were correlated with histopathological analysis in a subset of mice. In the LC group, increased fractional anisotropy (FA) and decreased orientation dispersion index (ODI) but limited neurite density index (NDI) changes were found in the gray matter, and minimal changes to white matter (WM) were observed. The HC group presented increased mean diffusivity (MD), decreased NDI, and decreased ODI in the WM and gray matter (GM); decreased FA was also found in a small area of the WM. WM changes were associated with WM degeneration and neuroinflammation. FEA showed varying region-dependent degrees of stress, in line with the different imaging findings. This study provides evidence that UHF-DTI combined with NODDI can detect concussions of variable intensities. This has significant implications for the diagnosis of concussion in humans.