Abstract:
This technical paper describes the development and application of a probabilistic finite element analysis (FEA) model to investigate the biomechanical response of baboon femurs. A primary challenge in computational biomechanics is accurately representing the complex morphology and inherent variability of biological structures in models. This study addressed this challenge by developing a statistical shape model from micro-CT scans of three baboon femurs. The methodology involved extracting bone geometry and apparent density distributions from the scans. A Principal Components Analysis (PCA) was used to capture the variation in geometry and density, revealing that 99.94% of the variation could be explained by a single eigenvalue. This dominant mode of variation was then modeled as a random variable, along with a parameter representing the uncertainty in determining elastic modulus from apparent density. Using probabilistic software (NESSUS) and the Latin Hypercube analysis method, the study demonstrated how these input variabilities propagated through the FE model to predict a range of femur stiffness values. The results showed a 90% probability that femur stiffness would fall within the range of 2085 to 2866 N/mm. This approach provides a robust method for quantifying the effects of biological variability and uncertainty on biomechanical predictions.
