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PubMedDate not supplied

A new method for the automatic mesh generation of bone segments from CT data.

A new procedure for the automatic generation of finite element meshes of bone segments from computed tomography (CT) data sets is described. The new method allows a direct automatic generation from the CT data and produces a very accurate unstructured hexahedral mesh. The accuracy of the method was established using the CT images of an artificial femur showing range of attenuation values comparable to those of a human femur. To establish the optimal values for the parameters controlling the mesh a sensitivity analysis was carried out using mesh-conditioning indicators. Some of the best meshes, with increasing levels of refinement, were used to analyse the stresses induced in the proximal femur by single leg stance posture. The accuracy of the meshes was evaluated using an implicit a posteriori residual-based error estimates. The number of elements with stress residuals larger than 10% of the peak stress was 7.8% using the coarsest mesh and only 1.8% with the finest mesh. The proposed method has been proved able to conjugate full automation with high-quality finite element meshes. The stress predictions obtained using these hexahedral-only meshes have been more accurate than those obtained by any other automatic mesh generation algorithm. Once properly integrated in an easy-to-use application, the described method could finally make feasible many clinical applications of finite element analysis.

Bone and Bones↗

PubMedDate not supplied

Fracture prediction for the femoral neck using finite element models.

Fracture of the femoral neck is an important clinical, social and economic topic. Prediction in subjects who are at risk for this type of fracture has been the object of numerous studies. Nonetheless, the methods of classification based on densitometric indicators alone have shown poor accuracy. It is the purpose of this study to propose a method to obtain an estimate of the resistance of the proximal femur, and to improve accuracy in prediction in subjects who are at risk for fracture. Based on the densitometric dataset alone, a bidimensional finite element model was developed that takes into account the distribution of density together with the femoral anatomy and the typical conditions of trauma. The model was applied to a group of preliminary osteoporotic patients. The statistical classification showed an increase in accuracy by 13%, as compared to a classification based on densitometric indicators alone.

Adult↗