No abstract

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Reliable computer models are needed for a better understanding of the physical mechanisms of skull fracture in accidental hits, falls, bicycle-motor vehicle & car accidents and assaults. The performance and biofidelity of these models depend on the correct anatomical representation and material description of these structures. In literature, a strain energy criterion has been proposed to predict skull fractures. However, a broad range of values for this criterion has been reported. This study investigates if the impactor orientation, scalp thickness and material model of the skull could provide us with insight in the influencing factors of this criterion. 18 skull fracture experiments previously performed in our research group were reproduced in finite element simulations. Subject-specific skull geometries were derived from medical images and used to create high-quality finite element meshes. Based on local Hounsfield units, a subject-specific isotropic material model was assigned. The subject-specific models were able to predict fractures who matched visually with the corresponding experimental fracture patterns and provided detailed fracture patterns. The sensitivity study showed that small variations in impactor positioning as well as variations of the local geometry (frontal-Preprint submitted to Elsevier May 18, 2019 temporal-occipital) strongly influenced the skull strain energy. Subject-specific modelling leads to a more accurate prediction of the force-displacement curve. The average error of the peak fracture force for all the 18 cases is 0.4190 for the subject-specific and 0.4538 for the homogeneous material model, for the displacement; 0.3368 versus 0.3844. But it should be carefully interpreted as small variations in the computational model significantly influence the outcome.

No abstract

Classification and evaluation of acetabular defects remain challenging and are primarily based on qualitative classification methods. That is because quantitative techniques describing variations of acetabular defects and accompanying bone loss volume are not available. This study introduces a new method based on statistical shape models (SSMs) to quantitively describe acetabular defects. This method is then applied to 87 acetabular defects to objectively describe the variations in acetabular defects typically encountered during revision total hip arthroplasty. The absolute bone loss volume, relative bone loss volume, and relative bone loss surface area with respect to the SSM-based pre-diseased anatomy were used to quantify the acetabular bone defects in different segments of the acetabular surface. The absolute bone loss volume of the average defect shape was equal to 37.0 cm 3 . The first three principal modes, accounting for 62% of the total shape variation, were found to represent variations in acetabular defect morphology. The first, second, and third principal modes described, respectively, the size of the bone defects, the difference between superomedially and superolaterally migrated defects, and the degree of involvement of the posterior or anterior column. The developed SSM and the introduced approach could be used to create automated and unbiased classification methods based on quantitative data. Moreover, the proposed model and the underlying data provide the basis for a quantitative design approach where the shape and size of new acetabular implants are determined according to clinical variation present in acetabular defects.