1 Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts.
2 Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts.
J Neurotrauma. 2019 Jan 15;36(2):250-263. doi: 10.1089/neu.2018.5634. Epub 2018 Aug 10.
Advanced neuroimaging provides new opportunities to enhance head injury models, including the incorporation of white matter (WM) structural anisotropy. Information from high-resolution neuroimaging, however, usually has to be "down-sampled" to match a typically coarse brain mesh. To understand how this mesh-image resolution mismatch affects impact simulation and subsequent response sampling, we compared three competing anisotropy implementations (using either voxels, tractography, or a multiscale submodeling) and two response sampling strategies (element-wise or tractography-based, using brain mesh or neuroimaging for region segmentation, respectively). Using the combination of high resolution options as a baseline, we studied how the choice in each individual category affected the resulting injury metrics. By simulating a recorded loss of consciousness head impact, we found that injury metrics including peak strain and injury susceptibility in the deep WM regions based on fiber strain, but not on maximum principal strain, were sensitive to the anisotropy implementation, response sampling, and region segmentation. Overall, it was recommended to use tractography for anisotropy implementation and response sampling, and to employ neuroimaging for region segmentation, because they led to more accurate injury metrics. Further refining mesh locally via submodeling was unnecessary. Brain strain responses were also parametrically found to be closer to that from minimum fiber reinforcement, consistent with the fact that the majority of WM had a rather high degree of fiber dispersion. Finally, the upgraded Worcester Head Injury Model incorporating WM anisotropy was successfully re-validated against cadaveric impacts and an in vivo head rotation ("good" to "excellent" validation with an average Correlation Analysis score of 0.437 and 0.509, respectively). These investigations may facilitate further continual development of head injury models to better study traumatic brain injury.
高级神经影像学为增强头部损伤模型提供了新的机会,包括白质(WM)结构各向异性的纳入。然而,来自高分辨率神经影像学的信息通常必须“下采样”以匹配典型的粗脑网格。为了了解这种网格-图像分辨率不匹配如何影响冲击模拟和随后的响应采样,我们比较了三种竞争的各向异性实现方法(分别使用体素、追踪或多尺度子建模)和两种响应采样策略(元素或基于追踪的,分别使用脑网格或神经影像学进行区域分割)。使用高分辨率选项的组合作为基线,我们研究了在每个单独类别中进行选择如何影响最终的损伤指标。通过模拟记录的昏迷头部撞击,我们发现基于纤维应变的深部 WM 区域的峰值应变和损伤易感性等损伤指标,但不是最大主应变,对各向异性实现、响应采样和区域分割敏感。总体而言,建议使用追踪进行各向异性实现和响应采样,并使用神经影像学进行区域分割,因为它们可以导致更准确的损伤指标。进一步通过子建模局部细化网格是不必要的。还发现脑应变响应在参数上更接近最小纤维增强的应变响应,这与 WM 中大多数纤维具有相当高的分散度的事实一致。最后,成功地将包含 WM 各向异性的升级后的 Worcester 头部损伤模型重新验证了尸体冲击和体内头部旋转(分别为平均相关分析得分为 0.437 和 0.509 的“良好”到“优秀”验证)。这些研究可能有助于进一步持续开发头部损伤模型,以更好地研究创伤性脑损伤。