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首页> 外文期刊>Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA >Automated 3D trabecular bone structure analysis of the proximal femur--prediction of biomechanical strength by CT and DXA.
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Automated 3D trabecular bone structure analysis of the proximal femur--prediction of biomechanical strength by CT and DXA.

机译:股骨近端的自动化3D小梁骨结构分析-通过CT和DXA预测生物力学强度。

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摘要

SUMMARY: The standard diagnostic technique for assessing osteoporosis is dual X-ray absorptiometry (DXA) measuring bone mass parameters. In this study, a combination of DXA and trabecular structure parameters (acquired by computed tomography [CT]) most accurately predicted the biomechanical strength of the proximal femur and allowed for a better prediction than DXA alone. INTRODUCTION: An automated 3D segmentation algorithm was applied to determine specific structure parameters of the trabecular bone in CT images of the proximal femur. This was done to evaluate the ability of these parameters for predicting biomechanical femoral bone strength in comparison with bone mineral content (BMC) and bone mineral density (BMD) acquired by DXA as standard diagnostic technique. METHODS: One hundred eighty-seven proximal femur specimens were harvested from formalin-fixed human cadavers. BMC and BMD were determined by DXA. Structure parameters of the trabecular bone (i.e., morphometry, fuzzy logic, Minkowski functionals, and the scaling index method [SIM]) were computed from CT images. Absolute femoral bone strength was assessed with a biomechanical side-impact test measuring failure load (FL). Adjusted FL parameters for appraisal of relative bone strength were calculated by dividing FL by influencing variables such as body height, weight, or femoral head diameter. RESULTS: The best single parameter predicting FL and adjusted FL parameters was apparent trabecular separation (morphometry) or DXA-derived BMC or BMD with correlations up to r = 0.802. In combination with DXA, structure parameters (most notably the SIM and morphometry) added in linear regression models significant information in predicting FL and all adjusted FL parameters (up to R(adj) = 0.872) and allowed for a significant better prediction than DXA alone. CONCLUSION: A combination of bone mass (DXA) and structure parameters of the trabecular bone (linear and nonlinear, global and local) most accurately predicted absolute and relative femoral bone strength.
机译:摘要:评估骨质疏松症的标准诊断技术是双X线骨密度仪(DXA)测量骨量参数。在这项研究中,DXA和骨小梁结构参数(通过计算机断层扫描[CT]获得)的组合最准确地预测了股骨近端的生物力学强度,并且比单独使用DXA更好地进行了预测。简介:采用自动3D分割算法来确定股骨近端CT图像中小梁骨的特定结构参数。与DXA作为标准诊断技术获得的骨矿物质含量(BMC)和骨矿物质密度(BMD)相比,评估这些参数预测生物力学股骨强度的能力。方法:从福尔马林固定的人尸体中收集了一百八十七个股骨近端标本。 BMC和BMD由DXA确定。从CT图像中计算出小梁骨的结构参数(即形态测量,模糊逻辑,Minkowski泛函和缩放指数方​​法[SIM])。通过测量失败负荷(FL)的生物力学侧面碰撞试验评估股骨的绝对骨强度。通过将FL除以影响变量(例如身高,体重或股骨头直径)来计算用于评估相对骨强度的调整后FL参数。结果:预测FL和调整FL参数的最佳单一参数是表观小梁分离(形态测定)或DXA衍生的BMC或BMD,相关系数最高为r = 0.802。与DXA结合使用时,在线性回归模型中添加的结构参数(最值得注意的是SIM和形态测量)可在预测FL和所有调整后的FL参数(高达R(adj)= 0.872)方面提供重要信息,并且比单独使用DXA显着更好。结论:结合骨质量(DXA)和小梁骨的结构参数(线性和非线性,整体和局部)可以最准确地预测股骨的绝对和相对强度。

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