A comparison of porosity, fabric and fractal dimension as predictors of the Young’s modulus of equine cancellous bone.

This study analyses the effectiveness of fabric and fractal dimension as predictors for the Young’s modulus of equine cancellous bone, and demonstrates that combining porosity and fractal dimension tends to provide a more accurate prediction.

Understanding the Research

• The focus of this study is to ascertain how well the structural parameters of fabric (the spatial arrangement and orientation of different components within cancellous bone) and fractal dimension (a statistical quantity that shows how a fractal pattern’s scale changes with the scale factor) predict the Young’s modulus of equine cancellous bone.
• The Young’s modulus is a measure of stiffness of an elastic material, and in this context, it’s used to represent the stiffness of the cancellous bone. Cancellous bone is one of the two types of osseous tissues forming bones, which is characterized by a sponge-like structure, highly vascularized, and filled with red bone marrow.

Methodology

• To gather data, the researchers extracted 15mm cubes of cancellous bone from three equine third metacarpal bones. The Young’s modulus, fabric, and fractal dimension were calculated for each of the six exposed faces of these cubes. These data were then compared to determine how effectively these metrics could predict the Young’s modulus of the bone.

Findings

• The data suggested that the combination of fractal dimension and porosity (the measure of the void spaces in a material) offered greater explanatory power for the Young’s modulus (78.7% accuracy), as compared to the combination of fabric and porosity (69.2% accuracy).
• Fractal dimension was also found to be significantly correlated with fabric. This suggests that the fractal dimension and fabric are related, and changes to one might influence the other.

Implications and Conclusions

• By indicating that fractal dimension plus porosity gives a higher accuracy in predicting Young’s modulus, this study suggests that these parameters could be useful in predicting aspects of bone health and strength in equine subjects.
• The authors acknowledge that while this method of combining fractal dimension data into a directionally dependent predictor of Young’s modulus has demonstrated potential, it will still require further validations. This is particularly necessary across a wider range of porosities and varying cancellous bone tissues.