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The determining factors for the fixation of uncemented screws in bone are the bone-implant interface and the peri-implant bone. The goal of this work was to explore the role of the peri-implant bone architecture on the mechanics of the bone-implant system. In particular, the specific aims of the study were to investigate: (i) the impact of the different architectural parameters, (ii) the effects of disorder, and (iii) the deformations in the peri-implant region. A three-dimensional beam lattice model to describe trabecular bone was developed. Various microstructural features of the lattice were varied in a systematic way. Implant pull-out tests were simulated, and the stiffness and strength of the bone-implant system were computed. The results indicated that the strongest decrease in pull-out strength was obtained by trabecular thinning, whereas pull-out stiffness was mostly affected by trabecular removal. These findings could be explained by investigating the peri-implant deformation field. For small implant displacements, a large amount of trabeculae in the peri-implant region were involved in the load transfer from implant to bone. Therefore, trabecular removal in this region had a strong negative effect on pull-out stiffness. Conversely, at higher displacements, deformations mainly localized in the trabeculae in contact with the implant; hence, thinning those trabeculae produced the strongest decrease in the strength of the system. Although idealized, the current approach is helpful for a mechanical understanding of the role played by peri-implant bone. [ABSTRACT FROM AUTHOR]

On a sub-millimeter length scale, bone is a very heterogeneous material with varying mineral content. This heterogeneity can be measured by quantitative backscattered electron imaging (qBEI) and quantified by a probability distribution called the bone mineralization density distribution (BMDD). The stochastic nature of the backscattering of electrons during the measurement makes the results dependent on the acquisition time. In this work the influence of the measurement conditions was quantified and was corrected for using Tikhonov regularization. Deconvolution reduces the width of the BMDD and allows a more precise definition of a reference BMDD for healthy adults. The corrected information was used as input for a mathematical model that predicts the time evolution of the BMDD. Simulations of osteoporosis treatment reveal a double peak in the BMDD that is not observed in experiments due to limited acquisition time. Our method allows determining the necessary acquisition time to resolve such double peaks. [ABSTRACT FROM AUTHOR]