Gender differences in L1 vertebral strength in adults 50+ using automated CT-based finite element analysis

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.