Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Bone. 2020 Jun;135:115314. doi: 10.1016/j.bone.2020.115314. Epub 2020 Mar 8.
One of the characteristics of osteoporotic bone is the deterioration of trabecular microarchitecture. Previous studies have shown microarchitecture alone can vary the apparent modulus of trabecular bone significantly independent of bone volume fraction (BV/TV) from morphological and topological perspectives. However, modulus is a mechanical quantity and there is a lack of mechanical explanatory parameters. This study aims to propose a novel mechanical parameter to quantify the microarchitecture effect on the apparent modulus of trabecular bone.
Fourteen human female cadaveric vertebrae were scanned with a dual-energy X-ray (DXA) equipment followed by a micro-CT (μCT) system at 18 μm isotropic resolution. Four trabecular bone specimens (3.46 × 3.46 × 3.46 mm) were obtained from each vertebral body and converted to voxel-based micro finite element (μFE) models. The apparent modulus (E) of the μFE model was computed using a linear micro finite element analysis (μFEA). The normalized apparent modulus (E*) was computed as E divided by BV/TV. The relationship between E and BV/TV was analyzed by linear, power-law and exponential regressions. Linear regression was performed between E* and BV/TV. Ineffective bone mass (InBM) was defined as the bone mass with a negligible contribution to the load-resistance and represented by elements with von Mises stress less than a certain stress threshold. InBM was quantified as the low von Mises stress ratio (LSR), which is the ratio of the number of InBM elements to the total number of elements in the μFE model. An incremental search technique with coarse and fine search intervals of 10 and 1 MPa, respectively, was adopted to determine the stress threshold for calculating LSR of the μFE model. Correlation between E* and LSR was analyzed using linear and power-law models for each stress threshold. The threshold producing the highest coefficient of determination (R) in the correlation between E* and LSR was taken as the optimal stress threshold for calculating LSR. Linear regression was performed between E and LSR. Multiple linear regression of E against both BV/TV and LSR was further analyzed.
E significantly (p < .001) correlates to BV/TV whereas E* has no significant (p = .75) correlation with BV/TV. Incremental search suggests 59 MPa to be the optimal stress threshold for calculating LSR. BV/TV alone can explain 59% of the variation in E using power-law regression model (E = 2254.64BV/TV, R = 0.59, p < .001). LSR alone can explain 48% of the variation in E using linear regression model (E = 1696.4-1647.1LSR, R = 0.48, p < .001). With these two predictors taken into consideration, 95% of the variation in E can be explained in a multiple linear regression model (E = 1364.89 + 2184.37BV/TV - 1605.38LSR, adjusted R = 0.95, p < .001).
LSR can be adopted as the mechanical parameter to quantify the microarchitecture effect on the apparent modulus of trabecular bone.
骨质疏松症骨骼的特征之一是小梁微结构的恶化。以前的研究表明,从形态学和拓扑学的角度来看,微结构本身可以显著改变小梁骨的表观弹性模量,而与骨体积分数(BV/TV)无关。然而,模量是一个力学量,缺乏力学解释参数。本研究旨在提出一种新的力学参数来量化微结构对小梁骨表观弹性模量的影响。
对 14 具女性成人尸体的椎体进行双能 X 射线(DXA)设备扫描,然后在 18μm 各向同性分辨率下使用微计算机断层扫描(μCT)系统。从每个椎体中获得 4 个小梁骨标本(3.46×3.46×3.46mm),并转换为基于体素的微有限元(μFE)模型。使用线性微有限元分析(μFEA)计算 μFE 模型的表观弹性模量(E)。将归一化表观弹性模量(E*)定义为 E 除以 BV/TV。分析 E 与 BV/TV 之间的关系采用线性、幂律和指数回归。E与 BV/TV 之间进行线性回归。无效骨量(InBM)被定义为对负荷阻力贡献微不足道的骨量,用 von Mises 应力小于一定应力阈值的元素表示。InBM 被量化为低 von Mises 应力比(LSR),即 InBM 元素数与 μFE 模型中总元素数的比值。采用粗搜索间隔和细搜索间隔分别为 10 和 1MPa 的增量搜索技术来确定计算μFE 模型 LSR 的应力阈值。对于每个应力阈值,使用线性和幂律模型分析 E与 LSR 之间的相关性。在 E*和 LSR 之间的相关性中,采用产生最高决定系数(R)的阈值作为计算 LSR 的最佳应力阈值。E 与 LSR 之间进行线性回归。进一步对 E 与 BV/TV 和 LSR 进行多元线性回归分析。
E 与 BV/TV 显著相关(p<0.001),而 E*与 BV/TV 无显著相关性(p=0.75)。增量搜索表明 59MPa 是计算 LSR 的最佳应力阈值。BV/TV 单独使用幂律回归模型可解释 E 变化的 59%(E=2254.64BV/TV,R=0.59,p<0.001)。LSR 单独使用线性回归模型可解释 E 变化的 48%(E=1696.4-1647.1LSR,R=0.48,p<0.001)。考虑这两个预测因子,E 的 95%变化可以在多元线性回归模型中得到解释(E=1364.89+2184.37BV/TV-1605.38LSR,调整后的 R=0.95,p<0.001)。
LSR 可作为量化微结构对小梁骨表观弹性模量影响的力学参数。