Li Xiaogai
Division of Neuronic Engineering, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
Front Bioeng Biotechnol. 2021 Oct 18;9:706566. doi: 10.3389/fbioe.2021.706566. eCollection 2021.
Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.
有限元（FE）头部模型已成为神经科学许多领域的强大工具，特别是在研究创伤性脑损伤（TBI）的生物力学方面。为了获得更可靠的预测结果，需要考虑个体间几何差异的个体化头部模型。然而，生成适用于研究TBI的此类模型仍然是一项重大挑战，并且一直是阻碍个性化模拟的瓶颈。本研究提出了一个个性化框架，通过对基线模型进行变形，生成跨越生命周期以及具有显著解剖结构变化的病理性大脑的个体化模型。该框架由分层多特征和多模态成像配准、网格变形和网格分组组成，在包括新生儿、1岁、2岁、成人、92岁以及脑积水大脑的异构数据集上显示出高效性。生成的六个受试者的模型显示出具有竞争力的个性化精度，证明了该框架生成具有显著解剖差异的个体化模型的能力。生成的头部模型家族有助于研究年龄依赖性和群体特异性的脑损伤机制。高效生成个体化FE头部模型的框架有助于促进神经科学许多领域的个性化模拟。
