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在模拟头部撞击中预计算脑反应图谱的性能评估。

Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts.

机构信息

Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01506, USA.

Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.

出版信息

Ann Biomed Eng. 2017 Oct;45(10):2437-2450. doi: 10.1007/s10439-017-1888-3. Epub 2017 Jul 14.

Abstract

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.

摘要

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。

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