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牛股骨中段皮质内硬度:基于陷窝-小管的均匀化数值解与显微硬度测量。

Intracortical stiffness of mid-diaphysis femur bovine bone: lacunar-canalicular based homogenization numerical solutions and microhardness measurements.

机构信息

Department of Mechanical Engineering, Notre Dame University-Louaize, Zouk Mosbeh, P.O.Box: 72, Zouk Mikael, Lebanon.

Department of Mechanical Engineering, American University of Beirut, Riad El-Solh, Beirut, 1107 2020, Lebanon.

出版信息

J Mater Sci Mater Med. 2017 Sep;28(9):135. doi: 10.1007/s10856-017-5924-5. Epub 2017 Jul 31.

DOI:10.1007/s10856-017-5924-5
PMID:28762142
Abstract

Microscale lacunar-canalicular (L-C) porosity is a major contributor to intracortical bone stiffness variability. In this work, such variability is investigated experimentally using micro hardness indentation tests and numerically using a homogenization scheme. Cross sectional rings of cortical bones are cut from the middle tubular part of bovine femur long bone at mid-diaphysis. A series of light microscopy images are taken along a line emanating from the cross-section center starting from the ring's interior (endosteum) ring surface toward the ring's exterior (periosteum) ring surface. For each image in the line, computer vision analysis of porosity is conducted employing an image segmentation methodology based on pulse coupled neural networks (PCNN) recently developed by the authors. Determined are size and shape of each of the lacunar-canalicular (L-C) cortical micro constituents: lacunae, canaliculi, and Haversian canals. Consequently, it was possible to segment and quantify the geometrical attributes of all individual segmented pores leading to accurate determination of derived geometrical measures such as L-C cortical pores' total porosity (pore volume fraction), (elliptical) aspect ratio, orientation, location, and number of pores in secondary and primary osteons. Porosity was found to be unevenly (but linearly) distributed along the interior and exterior regions of the intracortical bone. The segmented L-C porosity data is passed to a numerical microscale-based homogenization scheme, also recently developed by the authors, that analyses a composite made up of lamella matrix punctuated by multi-inclusions and returns corresponding values for longitudinal and transverse Young's modulus (matrix stiffness) for these micro-sized spatial locations. Hence, intracortical stiffness variability is numerically quantified using a combination of computer vision program and numerical homogenization code. These numerically found stiffness values of the homogenization solution are corroborated experimentally using microhardness indentation measurements taken at the same points that the digital images were taken along a radial distance emanating from the interior (endosteum) surface toward the bone's exterior (periosteum) surface. Good agreement was found between numerically calculated and indentation measured stiffness of Intracortical lamellae. Both indentation measurements and numerical solutions of matrix stiffness showed increasing linear trend of compressive longitudinal modulus (E11) values vs. radial position for both interior and exterior regions. In the interior (exterior) region of cortical bone, stiffness modulus values were found to range from 18.5 to 23.4 GPa (23 to 26.0 GPa) with the aggregate stiffness of the cortical lamella in the exterior region being 12% stiffer than that in the interior region. In order to further validate these findings, experimental and FEM simulation of a mid-diaphysis bone ring under compression is employed. The FEM numerical deflections employed nine concentric regions across the thickness with graded stiffness values based on the digital segmentation and homogenization scheme. Bone ring deflections are found to agree well with measured deformations of the compression bone ring.

摘要

微尺度管腔-小管(L-C)孔隙率是皮质骨内刚度变化的主要贡献者。在这项工作中,使用微硬度压痕试验进行了实验研究,并使用均匀化方案进行了数值研究。从牛股骨中间管状部分的中间管切开皮质骨的横截面环。从横截面中心沿一条线拍摄一系列光镜图像,该线从环的内部(骨内膜)表面开始,朝向环的外部(骨外膜)表面。对于线上的每幅图像,都采用作者最近开发的基于脉冲耦合神经网络(PCNN)的图像分割方法对孔隙率进行计算机视觉分析。确定了每个腔隙-小管(L-C)皮质微成分的大小和形状:腔隙、小管和哈弗斯管。因此,可以分割和量化所有单个分割孔的几何属性,从而准确确定衍生的几何度量,例如 L-C 皮质孔的总孔隙率(孔隙体积分数)、(椭圆形)纵横比、取向、位置和二次和初级骨单位中的孔数。发现孔隙率沿皮质内和外区域不均匀(但呈线性)分布。将分割的 L-C 孔隙率数据传递给作者最近开发的基于微尺度的数值均匀化方案,该方案分析由基质中多夹杂组成的复合材料,并返回这些微空间位置的纵向和横向杨氏模量(基质刚度)的相应值。因此,使用计算机视觉程序和数值均匀化代码对皮质内刚度变化进行了数值量化。使用在相同点处从内部(骨内膜)表面到骨骼外部(骨外膜)表面沿径向距离采集的数字图像的微硬度压痕测量值,实验验证了均匀化解的这些数值发现的刚度值。数值计算得到的和压痕测量得到的皮质板的刚度之间存在很好的一致性。在内部(外部)区域,压痕测量和基质刚度的数值解均显示出压缩纵向模量(E11)值随径向位置的线性增加趋势。在皮质骨的内部(外部)区域,发现刚度模量值在 18.5 到 23.4 GPa(23 到 26.0 GPa)之间,而外部区域皮质板的总刚度比内部区域硬 12%。为了进一步验证这些发现,采用压缩中轴骨环的实验和有限元模拟。有限元数值挠度采用沿厚度的九个同心区域,基于数字分割和均匀化方案具有分级刚度值。发现骨环的挠度与压缩骨环的测量变形吻合良好。

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本文引用的文献

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Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks.基于几何属性的优化神经网络对皮质骨切片进行分割
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一种用于测量皮质骨弹性特性的替代超声方法。
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