Abstract
Introduction
Patient-specific biomechanical modeling using Finite Element Analysis (FEA) is pivotal for understanding the structural health of bones, optimizing surgical procedures, assessing outcomes, and validating medical devices, aligning with guidance issued by standards and regulatory bodies. Accurate mapping of image-to-mesh-material is crucial given bone's heterogeneous composition. This study aims to rigorously assess mesh convergence and evaluate the sensitivity of material grouping strategies in quantifying bone strength.
Method
Subject-specific geometry and nonlinear material properties were derived from computed tomography (CT) scan data of one cadaveric human vertebral body. Linear tetrahedral elements with varying edge lengths between 2mm and 0.9mm were then generated to study the mesh convergence. To compare the effectiveness of different grouping strategies, three approaches were used: Modulus Gaping (a user-defined absolute threshold of Young's modulus ranging from 500 MPa to 1 MPa), Percentual Thresholding (relative parameter thresholds ranging from 50% to 1%), and Adaptive clustering (unsupervised k-means-based clustering ranging from 10 to 200 clusters). Adaptive clustering enables a constant number of unique material properties in cross-specimen studies, improving the validity of results.
Result
Mesh convergence was evaluated via fracture load and reached at a 1mm mesh size across grouping strategies. All strategies exhibit minimal deviation (within 5%) from individually assigned material parameters, except Modulus Gaping, with a 500 MPa threshold (32% difference). Computational efficiency, measured by runtime, significantly improved with grouping strategies, reducing computational cost by 82 to 94% and unique material count by up to 99%.
Conclusion
Different grouping strategies offer comparable mesh convergence, highlighting their potential to reduce computational complexity while maintaining accuracy in the biomechanical modeling of bones and suggesting a more efficient approach than individual element materials. The higher efficiency of FEA may increase its applicability in clinical settings with limited computational resources. Further studies are needed to refine grouping parameters and assess their suitability across different subjects.
