Chen Hsiang-Ho, Wu Chieh-Wei, Cheng Yen, Su Mao-Chieh, Chen Yu-Jhen, Lai Po-Liang
Department of Biomedical Engineering, College of Engineering, Chang Gung University, Taoyuan, 33302, Taiwan.
Bone and Joint Research Center, Department of Orthopedic Surgery, Linkou Chang-Gung Memorial Hospital, Taoyuan, 33305, Taiwan.
Sci Rep. 2025 Mar 28;15(1):10667. doi: 10.1038/s41598-025-94557-2.
Osteoporosis is usually diagnosed using a Bone Mineral Density test using dual-energy X-ray Absorptiometry. However, it is limited by low testing rates and the inability to directly measure bone strength. Finite Element Analysis allows for a more detailed assessment of bone strength. However, its modeling complexity and high computational time requirements pose challenges. This study aims to develop customized MATLAB programs to automate the creation of heterogeneous bone models, streamlining preprocessing to reduce time, computational costs, and minimize variability from manual processes. The focus is on establishing a prediction model for the structural strength of the L1 vertebral body using patient-specific CT data, thereby aiding in the prediction of vertebral fracture risk. The CT images are stacked into a 3D array, and the pixel values are converted by Hounsfield units based on CT image. The bone segment and elasticity values are established based on the Hounsfield units. After modeling, strain and stress analysis were performed through the solver LS-DYNA. The compression force was distributed vertically on the upper endplate of the vertebral body. All nodes in the subvertebral plane were fully constrained. For comparison, vertebral models were automatically established and analyzed from recruited subjects. This study collected spine CT imaging datasets from 52 subjects, comprising 28 males and 24 females aged between 50 and 95 years. Preprocessing and mechanical analysis for each subject took an average of approximately 579.6 seconds. Analysis of the results indicated that women over 50 years of age exhibited higher strain and stress values in their vertebral models compared to men under the same applied force, highlighting gender-specific differences in biomechanical characteristics. This study effectively employed a practical approach to identify and select specific spinal segments from CT images, facilitating the automated creation of 3D models for subsequent finite element analysis. The predictive model generated results consistent with previous studies involving mechanical testing on actual human bones. Notably, the implementation of our predictive model substantially decreased processing time for Finite Element Analysis, rendering it more suitable for clinical use and easier to extend for future application.
骨质疏松症通常通过使用双能X线吸收法的骨密度测试来诊断。然而，它受到低检测率以及无法直接测量骨强度的限制。有限元分析能够对骨强度进行更详细的评估。然而，其建模复杂性和高计算时间要求带来了挑战。本研究旨在开发定制的MATLAB程序，以自动创建异质骨模型，简化预处理以减少时间、计算成本，并最小化人工流程带来的变异性。重点是使用患者特异性CT数据建立L1椎体结构强度的预测模型，从而有助于预测椎体骨折风险。CT图像被堆叠成一个三维数组，并且基于CT图像，像素值通过亨氏单位进行转换。基于亨氏单位确定骨段和弹性值。建模后，通过求解器LS-DYNA进行应变和应力分析。压缩力垂直分布在椎体的上端板上。椎体平面以下的所有节点均被完全约束。为了进行比较，从招募的受试者中自动建立并分析椎体模型。本研究收集了52名受试者的脊柱CT成像数据集，包括28名男性和24名女性，年龄在50至95岁之间。对每个受试者的预处理和力学分析平均耗时约579.6秒。结果分析表明，在相同作用力下，50岁以上女性的椎体模型中的应变和应力值高于男性，突出了生物力学特征中的性别差异。本研究有效地采用了一种实用方法，从CT图像中识别并选择特定的脊柱节段，便于自动创建三维模型以用于后续的有限元分析。所生成的预测模型的结果与先前涉及对实际人体骨骼进行力学测试的研究一致。值得注意的是，我们的预测模型的实施大幅减少了有限元分析的处理时间，使其更适合临床应用且更易于扩展以供未来使用。
