The influence of the specimen shape and loading conditions on the parameter identification of a viscoelastic brain model

Statistical modeling of human liver incorporating the variations in shape, size, and material properties

The liver is one of the most frequently injured abdominal organs during motor vehicle crashes. Realistic numerical assessments of liver injury risk for the entire occupant population require incorporating inter-subject variations into numerical models. The main objective of this study was to quantify the shape variations of human liver in a seated posture and the statistical distributions of its material properties. Statistical shape analysis was applied to construct shape models of the livers of 15 adult human subjects, recorded in a typical seated (occupant) posture. The principal component analysis was then utilized to obtain the modes of variation, the mean model, and 95% statistical boundary shape models. In addition, a total of 52 tensile tests were performed on the parenchyma of three fresh human livers at four loading rates (0.01, 0.1, 1, and 10 s^-1) to characterize the rate-dependent and failure properties of the human liver. A FE-based optimization approach was employed to identify the material parameters of an Ogden material model for each specimen. The mean material parameters were then determined for each loading rate from the characteristic averages of the stress-strain curves, and a stochastic optimization approach was utilized to determine the standard deviations of the material parameters. Results showed that the first five modes of the human liver shape models account for more than 60% of the overall anatomical variations. The distributions of the material parameters combined with the mean and statistical boundary shape models could be used to develop probabilistic finite element (FE) models, which may help to better understand the variability in biomechanical responses and injuries to the abdominal organs under impact loading.