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

Area of Science:

• Biomedical Engineering
• Computational Biology
• Orthopedics

Background:

• Accurate trabecular bone microstructure analysis is vital for assessing bone health and mechanical properties.
• Low-resolution computed tomography (CT) images limit the depiction of fine trabecular architecture.
• Existing methods struggle to capture the inherent mechanical and biological variability in bone adaptation.

Purpose of the Study:

• To develop a computational framework for subject-specific trabecular microstructure reconstruction.
• To enhance accuracy and stability by integrating mechanical and biological variability.
• To enable detailed bone analysis from low-resolution imaging.

Main Methods:

• A topology optimization framework was employed to predict trabecular morphology from low-resolution CT scans.
• Uncertainty in loading and biological response during bone remodeling was incorporated.
• A superposition strategy was used to estimate local mechanical stimuli, enhancing robustness to boundary force variations.
• Validation was performed using high-resolution rabbit bone images and application to human lower-limb bone data.

Main Results:

• Reconstructed trabecular regions closely matched high-resolution images, accurately capturing branching and connectivity.
• Predicted human bone morphology aligned with established statistical distributions of trabecular parameters.
• The framework demonstrated computational precision and stability, yielding anisotropic mechanical properties consistent with physiological loading.

Conclusions:

• The developed computational approach allows for patient-specific trabecular microstructure reconstruction from low-resolution imaging.
• This method offers improved robustness and reduced computational cost compared to existing techniques.
• The framework holds potential for clinical assessment and multi-scale bone mechanics research.