Mechanical Engineering, USA.
Electrical and Computer Engineering University of Texas at San Antonio, San Antonio, TX, 78249, USA.
J Mech Behav Biomed Mater. 2021 Dec;124:104834. doi: 10.1016/j.jmbbm.2021.104834. Epub 2021 Sep 15.
3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the recent success of deep learning (DL) techniques, we hypothesized that DL modeling techniques could be used to predict the apparent stiffness tensor of trabecular bone directly using dual-energy X-ray absorptiometry (DXA) images. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the apparent stiffness tensor of trabecular bone cubes using their DXA images. Trabecular bone cubes obtained from human cadaver proximal femurs were used to obtain simulated DXA images as input, and the apparent stiffness tensor of the trabecular cubes determined by using micro-CT based FE simulations was used as output (ground truth) to train the DL model. The prediction accuracy of the DL model was evaluated by comparing it with the micro-CT based FE models, histomorphometric parameter based multiple linear regression models, and BV/TV/fabric tensor based multiple linear regression models. The results showed that DXA image-based DL model achieved high fidelity in predicting the apparent stiffness tensor of trabecular bone cubes (R = 0.905-0.973), comparable to or better than the histomorphometric parameter based multiple linear regression and BV/TV/fabric tensor based multiple linear regression models, thus supporting the hypothesis of this study. The outcome of this study could be used to help develop DXA image-based DL techniques for clinical assessment of bone fracture risk.
基于三维图像的有限元（FE）和骨体积分数（BV/TV）/织构张量建模技术目前用于确定小梁骨的表观刚度张量，以评估其各向异性弹性行为。受深度学习（DL）技术最近成功的启发，我们假设 DL 建模技术可以直接使用双能 X 射线吸收法（DXA）图像来预测小梁骨的表观刚度张量。为了验证这一假设，我们训练并验证了一个卷积神经网络（CNN）模型，以使用其 DXA 图像预测小梁骨立方的表观刚度张量。使用从人体尸体股骨近端获得的小梁骨立方来获得模拟 DXA 图像作为输入，使用基于微 CT 的 FE 模拟确定的小梁立方的表观刚度张量作为输出（真实值）来训练 DL 模型。通过与基于微 CT 的 FE 模型、基于组织形态计量学参数的多元线性回归模型和基于 BV/TV/织构张量的多元线性回归模型进行比较，评估了 DL 模型的预测准确性。结果表明，基于 DXA 图像的 DL 模型在预测小梁骨立方的表观刚度张量方面具有很高的保真度（R=0.905-0.973），与基于组织形态计量学参数的多元线性回归和基于 BV/TV/织构张量的多元线性回归模型相当或更好，从而支持了本研究的假设。本研究的结果可用于帮助开发基于 DXA 图像的 DL 技术，用于临床评估骨折风险。
