Parametric study of head impact in the infant.
Computer finite element model (FEM) simulations are often used as a substitute for human experimental head injury studies to enhance our understanding of injury mechanisms and develop prevention strategies. While numerous adult FEM of the head have been developed, there are relatively few pediatric FEM due to the paucity of material property data for children. Using radiological serial images of infants (<6 wks old) and recent published material property data of infant skull and suture, we developed a FEM of the infant head to study skull fracture from occipital impacts. Here we determined the relative importance of brain material properties and anatomical variations in infant suture and scalp tissue on principal stress (sigma(p)) estimates in the skull of the model using parametric simulations of occipital impacts from 0.3m falls onto concrete. Decreasing the brain stiffness of pediatric brain tissue by a factor of two to simulate the softer adult brain properties we reported previously did not affect sigma(p). Using adult brain stiffness reported by others (4 times higher than our pediatric values) increased sigma(p) in skull by 38%. Interestingly, the precision used to model compressibility of the brain (0.49-0.4999) significantly varied sigma(p) 30-77%, underscoring the influence of the brain properties in models of fracture in the highly deformable infant skullcase. Suture thickness, small anatomical variations in suture width and the exclusion of scalp did not affect sigma(p) of the skull; however, unusually large sutures (10 mm) in young infants significantly lowered sigma(p). Validation of this model against published infant cadaver drop studies found good agreement with the prediction of fracture for falls onto hard surfaces. More biomechanical data from impacts onto softer surfaces is needed before skull fracture predictions can be made in these scenarios. In summary, the pediatric FEM response is not sensitive to small variations in anatomy or brain modulus, large deviations will significantly influence principal stress estimates and the prediction of skull fracture.
Coats
Brittany
B
University of Pennsylvania, Department of Bioengineering, 240 Skirkanich Hall, University of Pennsylvania, Philadelphia, PA 19104-6321, USA.
Margulies
Susan S
SS
Ji
Songbai
S
D001921:Brain / Q000033:anatomy & histology*; D001930:Brain Injuries / Q000503:physiopathology*; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D008297:Male; D008954:Models, Biological; D012887:Skull Fractures / Q000503:physiopathology*