ABSTRACT
Objective To study the relationship between material mechanics and bone material parameters of rat skulls and their correlation with age by examination of the parameters. Methods Forty-eight healthy male SD rats were divided into 2, 4, 6, 8, 17, 26, 52 and 104 week groups according to their age. Each group had six rats. The right cranium was compressed by KD Ⅱ-0.2 microcomputer controlled electronic universal testing machine, and material mechanics parameters （ultimate load, compression strength and compression modulus） were measured, then the skull slices were cut off and scanned by Micro-CT system to detect bone material parameters （skull thickness, bone mineral density, bone volume, and trabecular thickness）. Results The differences in ultimate load, compression strength and compression modulus among all groups had statistical significance （P<0.05）, and were positively correlated with age within 26 weeks （P<0.05）. The differences in skull thickness, bone mineral density, bone volume and trabecular thickness among all groups had statistical significance （P<0.05）, and were positively correlated with age within 52 weeks （P<0.05）. All material mechanics parameters were positively correlated with bone material parameters （P<0.05）. Conclusion There is a positive correlation between bone material parameters （skull thickness, bone mineral density, bone volume, trabecular thickness）, material mechanics parameter （skull ultimate load, compression strength, compression modulus） and age in a certain range, which can be used to infer age.
Subject(s)
Animals , Male , Rats , Biomechanical Phenomena , Bone Density , Rats, Sprague-Dawley , Skull/diagnostic imagingABSTRACT
Objective To obtain an optimized method of providing hyperelastic parameters of soft tissue, and to promote the simulation accuracy in explicit solution of finite element analysis (FEA) on soft tissue impact test. Methods Compressive properties of soft tissue from six fresh planta were measured. The experimental data were used to calculate the FEA material properties, which were then optimized by Poisson’s ratio. With the same loading and boundary conditions as the experiment, the FEA model was conducted for simulation. The simulation results were verified by both the experimental data and literature data. Results The force-displacement curve of soft tissue presented an exponential growth trend in the in vitro biomechanical experiment. When the compression ratio was under 45%, the FEA simulation result was consistent with the experimental data. When the compression ratio was above 45%, the closer the Poisson’s ratio up to 0.5, the higher the accuracy of FEA simulation result. However, there was a strong linear correlation between the FEA simulation results and experimental data (R2=0.9923) when the Poisson’s ratio was 0.497. Conclusions The simulation result of material parameters in FEA model is preferable in this study. With a lower compression ratio, the simulation results from FEA model are in consistency with the experimental data. Increasing the Poisson’s ratio can promote the simulation accuracy of the FEA model when the compression ratio is high.