Bioengineering and Computing Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
Materialise N.V., Heverlee, Belgium; Multiscale in Mechanical and Biological Engineering (M2BE), University of Zaragoza, Zaragoza, Spain; Biomechanics Section, KU Leuven, Leuven, Belgium.
Bone. 2020 Jul;136:115348. doi: 10.1016/j.bone.2020.115348. Epub 2020 Mar 31.
Despite evidence of the biomechanical role of cortical bone, current state of the art finite element models of the proximal femur built from clinical CT data lack a subject-specific representation of the bone cortex. Our main research hypothesis is that the subject-specific modelling of cortical bone layer from CT images, through a deconvolution procedure known as Cortical Bone Mapping (CBM, validated for cortical thickness and density estimates) can improve the accuracy of CT-based FE models of the proximal femur, currently limited by partial volume artefacts. Our secondary hypothesis is that a careful choice of cortical-specific density-elasticity relationship may improve model accuracy. We therefore: (i) implemented a procedure to include subject-specific CBM estimates of both cortical thickness and density in CT-based FE models. (ii) defined alternative models that included CBM estimates and featured a cortical-specific or an independently optimised density-elasticity relationship. (iii) tested our hypotheses in terms of elastic strain estimates and failure load and location prediction, by comparing with a published cohort of 14 femurs, where strain and strength in stance and fall loading configuration were experimentally measured, and estimated through reference FE models that did not explicitly model the cortical compartment. Our findings support the main hypothesis: an explicit modelling of the proximal femur cortical bone layer including CBM estimates of cortical bone thickness and density increased the FE strains prediction, mostly by reducing peak errors (average error reduced by 30%, maximum error and 95th percentile of error distribution halved) and especially when focusing on the femoral neck locations (all error metrics at least halved). We instead rejected the secondary hypothesis: changes in cortical density-elasticity relationship could not improve validation performances. From these improved baseline strain estimates, further work is needed to achieve accurate strength predictions, as models incorporating cortical thickness and density produced worse estimates of failure load and equivalent estimates of failure location when compared to reference models. In summary, we recommend including local estimates of cortical thickness and density in FE models to estimate bone strains in physiological conditions, and especially when designing exercise studies to promote bone strength.
尽管有皮质骨生物力学作用的证据，但目前基于临床 CT 数据构建的股骨近端有限元模型缺乏对皮质骨的特定个体的表示。我们的主要研究假设是，通过称为皮质骨映射（Cortical Bone Mapping，CBM）的去卷积过程对 CT 图像中的皮质骨层进行特定个体建模，该过程已通过皮质厚度和密度估计验证，可以提高基于 CT 的股骨近端有限元模型的准确性，目前该模型受部分容积伪影限制。我们的次要假设是，仔细选择皮质特定的密度-弹性关系可以提高模型的准确性。因此，我们：（i）实施了一种程序，将基于 CT 的有限元模型中包含特定个体的 CBM 皮质厚度和密度估计值。（ii）定义了替代模型，这些模型包括 CBM 估计值，并具有皮质特定或独立优化的密度-弹性关系。（iii）通过与发表的 14 个股骨的队列进行比较，根据弹性应变估计和失效载荷和位置预测来检验我们的假设，在该队列中，站立和跌倒加载配置中的应变和强度通过实验测量，并通过未明确模拟皮质室的参考有限元模型进行估计。我们的发现支持主要假设：对股骨近端皮质骨层进行明确建模，包括 CBM 对皮质骨厚度和密度的估计，可以提高 FE 应变预测，主要是通过减少峰值误差（平均误差减少 30％，最大误差和误差分布的第 95 百分位数减半），尤其是在关注股骨颈位置时（所有误差指标至少减半）。我们拒绝了次要假设：皮质密度-弹性关系的变化不能提高验证性能。从这些改进的基础应变估计中，需要进一步的工作来实现准确的强度预测，因为与参考模型相比，纳入皮质厚度和密度的模型对失效载荷的估计更差，对失效位置的估计则相同。总之，我们建议在 FE 模型中纳入皮质厚度和密度的局部估计值，以在生理条件下估计骨应变，特别是在设计促进骨强度的运动研究时。
