Department of Medical Imaging, Urmia Medical Sciences University, Urmia, Iran.
Phys Med. 2010 Apr;26(2):88-97. doi: 10.1016/j.ejmp.2009.08.002. Epub 2009 Sep 24.
Noninvasive prediction of vertebral body strength under compressive loading condition is a valuable tool for the assessment of clinical fractures. This paper presents an effective specimen-specific approach for noninvasive prediction of human vertebral strength using a nonlinear finite element (FE) model and an image based parameter based on the quantitative computed tomography (QCT). Nine thoracolumbar vertebrae excised from three cadavers with an average age of 42 years old were used as the samples. The samples were scanned using the QCT. Then, a segmentation technique was performed on each QCT sectional image. The segmented images were then converted into three-dimensional FE models for linear and nonlinear analyses. A new material model was implemented in our nonlinear model being more compatible with real mechanical behavior of trabecular bone. A new image based MOS (Mechanic of Solids) parameter named minimum sectional strength ((sigma(u)A)(min)) was used for the ultimate compressive strength prediction. Subsequently, the samples were destructively tested under uniaxial compression and their experimental ultimate compressive strengths were obtained. Results indicated that our new implemented FE model can predict ultimate compressive strength of human vertebra with a correlation coefficient (R(2)=0.94) better than usual linear and nonlinear FE models (R(2)=0.83 and 0.85 respectively). The image based parameter introduced in this study ((sigma(u)A)(min)) was also correlated well with the experimental results (R(2)=0.86). Although nonlinear FE method with new implemented material model predicts compressive strength better than the (sigma(u)A)(min), this parameter is clinically more feasible due to its simplicity and lower computational costs. This can make future applications of the (sigma(u)A)(min) more justified for human vertebral body compressive strength prediction.
非侵入性预测椎体在压缩载荷下的强度是评估临床骨折的一种有价值的工具。本文提出了一种有效的基于样本的方法，使用非线性有限元（FE）模型和基于图像的参数，通过定量计算机断层扫描（QCT）来预测人体椎体的强度。9 个胸腰椎取自 3 具平均年龄为 42 岁的尸体作为样本。使用 QCT 对样本进行扫描。然后，对每个 QCT 切片图像执行分割技术。分割后的图像然后转换为三维 FE 模型进行线性和非线性分析。在我们的非线性模型中实现了一种新的材料模型，使其更符合小梁骨的实际力学行为。引入了一种新的基于图像的 MOS（固体力学）参数，称为最小截面强度((sigma(u)A)(min))，用于预测极限抗压强度。随后，对样本进行单轴压缩破坏性测试，获得其实验极限抗压强度。结果表明，我们新实现的 FE 模型可以预测人体椎体的极限抗压强度，相关系数（R(2)=0.94）优于通常的线性和非线性 FE 模型（R(2)=0.83 和 0.85）。本研究中引入的基于图像的参数((sigma(u)A)(min))也与实验结果具有良好的相关性（R(2)=0.86）。尽管具有新实现的材料模型的非线性 FE 方法预测抗压强度比(sigma(u)A)(min)更好，但由于其简单性和较低的计算成本，该参数在临床上更可行。这使得(sigma(u)A)(min)在预测人体椎体抗压强度方面的未来应用更有依据。
