A robust topology optimization based biomechanical computational framework for patient-specific trabecular bone...

Accurate reconstruction of trabecular bone microstructure is essential for understanding bone health and mechanical competence. Low-resolution computed tomography images, however, lack the detailed information that is needed to depict fine trabecular architecture. This study aims to develop a computational framework that reconstructs subject-specific trabecular microstructure with improved accuracy and stability by incorporating mechanical and biological variability inherent in bone adaptation.

A robust topology optimization framework was developed to predict trabecular morphology from low-resolution images. The method incorporates uncertainty in loading and biological response during bone remodeling. To reduce sensitivity to variations in boundary forces, a superposition strategy was used to estimate local mechanical stimuli within each volume of interest. The predicted microstructure was compared against high-resolution images of rabbit bone for validation, and subsequently applied to human lower-limb bone images. Quantitative assessments included geometric similarity and evaluation of mechanical anisotropy.

The reconstructed trabecular regions showed close agreement with high-resolution microstructural images in the animal validation study, capturing fine branching and connectivity patterns. In human bone, the predicted morphology was consistent with expected statistical distributions of trabecular thickness, spacing, and orientation. The framework demonstrated high computational precision and stability, producing anisotropic mechanical properties aligned with physiological loading patterns.

This computational approach enables patient-specific reconstruction of trabecular microstructure from low-resolution imaging with improved robustness and reduced computational cost. The framework shows potential for supporting clinical assessment and for advancing multi-scale investigations of bone mechanics.