title: A statistical skull geometry model for children 0-3 years old.
abstract: Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.
authors.affiliation: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China; University of Michigan Transportation Research Institute, Ann Arbor, Michigan, United States of America; State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China.
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Li
authors.affiliation: University of Michigan Transportation Research Institute, Ann Arbor, Michigan, United States of America.
authors.forename: Byoung-Keon
authors.initials: BK
authors.lastname: Park
authors.affiliation: Zhejiang Key Laboratory of Automobile Safety Technology, Hangzhou, China.
authors.forename: Weiguo
authors.initials: W
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China.
authors.forename: Jinhuan
authors.initials: J
authors.lastname: Zhang
authors.affiliation: University of Michigan Transportation Research Institute, Ann Arbor, Michigan, United States of America; Center for Ergonomics, Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Reed
authors.affiliation: University of Michigan Transportation Research Institute, Ann Arbor, Michigan, United States of America; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America; University of Michigan Medical School, Department of Emergency Medicine, Ann Arbor, Michigan, United States of America.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Rupp
authors.affiliation: University of Michigan Medical School, Department of Radiology, Ann Arbor, Michigan, United States of America.
authors.forename: Carrie N
authors.initials: CN
authors.lastname: Hoff
authors.affiliation: University of Michigan Transportation Research Institute, Ann Arbor, Michigan, United States of America; Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Jingwen
authors.initials: J
authors.lastname: Hu
mesh_terms: D002508:Cephalometry / Q000379:methods; D002675:Child, Preschool; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007223:Infant; D007231:Infant, Newborn; D015233:Models, Statistical; D008962:Models, Theoretical; D012307:Risk Factors; D012886:Skull / Q000033:anatomy & histology* / Q000254:growth & development*; D014057:Tomography, X-Ray Computed / Q000379:methods