A new fracture assessment approach coupling HR-pQCT imaging and fracture mechanics-based finite element modeling

A new fracture assessment approach that combines HR-pQCT imaging with fracture mechanics-based finite element modeling was developed to evaluate distal radius fracture load. Twenty distal radius images obtained from postmenopausal women (fracture, n=10; nonfracture, n=10) were processed to obtain a cortical and a whole bone model for each subject. The geometrical properties of each model were evaluated and the corresponding fracture load was determined under realistic fall conditions using cohesive finite element modeling. The results showed that the whole bone fracture load can be estimated based on the cortical fracture load for nonfracture (R2=0.58, p=0.01) and pooled data (R2=0.48, p<0.001) but not for the fracture group. The portion of the whole bone fracture load carried by the cortical bone increased with increasing cortical fracture load (R2≥0.5, p<0.05) indicating that a more robust cortical bone carries a larger percentage of whole bone fracture load. Cortical thickness was found to be the best predictor of both cortical and whole bone fracture load for all groups (R2 range: 0.49-0.96, p<0.02) with the exception of fracture group whole bone fracture load showing the predictive capability of cortical geometrical properties in determining whole bone fracture load. Fracture group whole bone fracture load was correlated with trabecular thickness (R2=0.4, p<0.05) whereas the nonfracture and the pooled group did not show any correlation with the trabecular parameters. In summary, this study introduced a new modeling approach that coupled HR-pQCT imaging with fracture mechanics-based finite element simulations, incorporated fracture toughness and realistic fall loading conditions in the models, and showed the significant contribution of the cortical compartment to the overall fracture load of bone. Our results provide more insight into the fracture process in bone and may lead to improved fracture load predictions.