Including anisotropy in homogenised inverse remodelling algorithms for habitual load case estimation in bone

Area of Science:

• Biomechanics
• Computational modeling
• Bone biology

Background:

• Bone adapts its structure to withstand habitual loading.
• Finite element (FE) analysis estimates bone loading from imaging data.
• Homogenized FE models are computationally efficient but traditionally limited to density-based remodeling.

Purpose of the Study:

• To integrate fabric anisotropy into homogenized FE-based inverse bone remodeling algorithms.
• To estimate long-term habitual loading conditions in bone architecture.
• To analyze the influence of anisotropy, boundary effects, and load complexity on predicted loads.

Main Methods:

• Developed a novel theoretical framework for homogenized inverse bone remodeling including fabric anisotropy.
• Applied the framework to 24 human distal tibia samples using high-resolution computed tomography images.
• Analyzed the impact of fabric anisotropy, St. Venant effect, and load case complexity on predicted forces, moments, and optimization outcomes.

Main Results:

• Physiologically plausible forces and moments were predicted across all analysis types.
• Including additional forces and moments improved optimization quality (reduced objective function, p < 0.001).
• Incorporating microstructural orientation significantly increased predicted load magnitude (p < 0.001) and reduced objective function values by ~50%.

Conclusions:

• Considering both bone volume fraction and fabric anisotropy is crucial for accurate habitual load estimation in bone using homogenized FE models.
• The developed framework improves the agreement between predicted load cases and actual bone architecture.
• Microstructural orientation is a key factor in understanding bone's adaptation to mechanical loading.