Soni Aakash, Kumar Sachin, Kumar Navin
Department of Mechanical Engineering, Indian Institute of Technology Ropar, Punjab, India, 140001.
Department of Mechanical Engineering, Indian Institute of Technology Ropar, Punjab, India, 140001.
Comput Methods Programs Biomed. 2022 Jun;220:106820. doi: 10.1016/j.cmpb.2022.106820. Epub 2022 Apr 17.
Medical imaging-based finite element methods are more accurate tools for fracture risk prediction than the traditional aBMD based methods. However, these methods have drawbacks like geometric errors, high computational cost, mesh-dependent results, etc. In this article, the authors have proposed an isogeometric analysis-based nonlocal gradient-enhanced damage model to overcome some of these issues. Moreover, there are uncertainties in the values of input parameters for such analysis due to various measurement errors. Hence, stochastic analysis is performed to quantify the effect of these parametric uncertainties on the fracture behavior of the proximal femur.
Computed Tomography images of a patient are used to create a 2D proximal femur model with a heterogeneous description of material properties. A numerical model based on gradient-enhanced nonlocal continuum damage mechanics is used for fracture analysis of proximal femur to overcome the issues related to mesh dependency in traditional continuum damage mechanics models. Further, a multipatch isogeometric solver is developed to solve the governing equations. Monte Carlo simulations are used to understand the effect of parametric uncertainties on the fracture behavior of the proximal femur.
The developed numerical framework is used to solve the fracture problem of proximal femur under single leg stance loading conditions. The obtained results are validated by comparing the load-displacement response and the crack path with that given in the literature. Stochastic analysis is performed by considering a ±5% variation in the elastic modulus, damage initiation strain, and the neck-shaft angle values.
The proposed numerical framework can correctly predict the damage initiation and propagation in a proximal femur. The results reveal that the heterogeneous nature of material properties of bone plays a significant role in determining the fracture characteristics of the proximal femur. Further, the results of the stochastic analysis reveal that the parametric uncertainties in the neck-shaft angle have a much more significant influence on the results of the analysis than the parametric uncertainties in the elastic modulus and damage initiation strain.
基于医学成像的有限元方法是比传统基于骨密度（aBMD）的方法更精确的骨折风险预测工具。然而，这些方法存在诸如几何误差、计算成本高、结果依赖网格等缺点。在本文中，作者提出了一种基于等几何分析的非局部梯度增强损伤模型来克服其中一些问题。此外，由于各种测量误差，此类分析的输入参数值存在不确定性。因此，进行随机分析以量化这些参数不确定性对近端股骨骨折行为的影响。
使用一名患者的计算机断层扫描图像创建具有材料属性非均匀描述的二维近端股骨模型。基于梯度增强非局部连续损伤力学的数值模型用于近端股骨的骨折分析，以克服传统连续损伤力学模型中与网格依赖性相关的问题。此外，开发了一种多面片等几何求解器来求解控制方程。蒙特卡罗模拟用于了解参数不确定性对近端股骨骨折行为的影响。
所开发的数值框架用于解决单腿站立加载条件下近端股骨的骨折问题。通过将载荷 - 位移响应和裂纹路径与文献中给出的结果进行比较，对所得结果进行了验证。通过考虑弹性模量、损伤起始应变和颈干角值的±5%变化进行随机分析。
所提出的数值框架能够正确预测近端股骨中的损伤起始和扩展。结果表明，骨材料属性的非均匀性质在确定近端股骨的骨折特征方面起着重要作用。此外，随机分析结果表明，颈干角的参数不确定性对分析结果的影响比弹性模量和损伤起始应变的参数不确定性大得多。
