Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01506, USA.
Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Ann Biomed Eng. 2017 Oct;45(10):2437-2450. doi: 10.1007/s10439-017-1888-3. Epub 2017 Jul 14.
A pre-computed brain response atlas (pcBRA) may have the potential to accelerate the investigation of the biomechanical mechanisms of traumatic brain injury on a large-scale. In this study, we further enhance the technique and evaluate its performance using six degree-of-freedom angular velocity profiles from dummy head impacts. Using the pcBRA to simplify profiles into acceleration-only shapes, sufficiently accurate strain estimates were obtained for impacts with a major dominating velocity peak. However, they were largely under-estimated when substantial deceleration occurred that reversed the direction of the angular velocity. For these impacts, estimation accuracy was substantially improved with a biphasic profile simplification (average correlation coefficient and linear regression slope of 0.92 ± 0.03 and 0.95 ± 0.07 for biphasic shapes, respectively, vs. 0.80 ± 0.06 and 0.80 ± 0.08 for acceleration-only shapes). Peak maximum principal strain (ɛ ) and cumulative strain damage measure (CSDM) from the estimated strains consistently correlated stronger than kinematic metrics with respect to the baseline ɛ and CSDM from the directly simulated responses, regardless of the brain region, and by a large margin (e.g., correlation of 0.93 vs. 0.75 compared to Brain Injury Criterion (BrIC) for ɛ in the whole-brain, and 0.91 vs. 0.47 compared to BrIC for CSDM in the corpus callosum). These findings further support the pre-computation technique for accurate, real-time strain estimation, which could be important to accelerate model-based brain injury studies in the future.
预先计算的大脑反应图谱(pcBRA)有可能加速对创伤性脑损伤的生物力学机制的大规模研究。在这项研究中,我们进一步增强了该技术,并使用假人头颅冲击的六自由度角速度曲线来评估其性能。使用 pcBRA 将曲线简化为仅加速度形状,对于主要主导速度峰值的冲击,可获得足够准确的应变估计。然而,当发生大幅度减速并使角速度方向反转时,这些估计值会大大低估。对于这些冲击,采用双相曲线简化(双相形状的平均相关系数和线性回归斜率分别为 0.92±0.03 和 0.95±0.07,而仅加速度形状分别为 0.80±0.06 和 0.80±0.08),可以显著提高估计精度。从估计的应变中得出的最大主应变峰值(ɛ)和累积应变损伤度量(CSDM)与从直接模拟响应得出的基线ɛ和 CSDM 的相关性始终比运动学指标更强,无论大脑区域如何,相关性都很大(例如,整个大脑中ɛ的相关性为 0.93 与脑损伤准则(BrIC)相比为 0.75,胼胝体中 CSDM 的相关性为 0.91 与 BrIC 相比为 0.47)。这些发现进一步支持了用于准确实时应变估计的预计算技术,这对于未来加速基于模型的脑损伤研究可能很重要。