A parametric head geometry model accounting for variation among adolescent and young adult populations. (June 2022)

• Record Type: Journal Article
• Authors: Wei, Albert; Wang, Julie; Liu, Jiacheng; Jones, Monica L.H.; Hu, Jingwen
• Is Part Of: Computer methods and programs in biomedicine. Volume 220(2022)
• Publication Date: 2022-06
• Subjects: Head geometry -- Skull geometry -- Statistical model -- Human variation -- Traumatic brain injury; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106805
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Head CT scans of 101 subjects between 14 and 25 years of age were used to develop a statistical head geometry model. Skull and scalp statistical geometry models accounts for size and shape variations among the adolescent and young adult population were developed as functions of age, sex, stature, BMI, head circumference, and tragion-to-top distance. The statistical geometry models account for a high percentage of morphological variations in scalp, outer skull, inner skull, and skull thickness, and may serve as the geometric basis to develop individualized head finite element models for injury assessment and design of head-borne equipment. Abstract: Background and objective: Modeling the size and shape of human skull and scalp is essential for head injury assessment, design of helmets and head-borne equipment, and many other safety applications. Finite element (FE) head models are important tools to assess injury risks and design personal protective equipment. However, current FE head models are mainly developed based on the midsize male, failing to account for the significant morphological variation that exists in the skull and brain. The objective of this study was to develop a statistical head geometry model that accounts for size and shape variations among the adolescent and young adult population. Methods: To represent subject-specific geometry using a homologous mesh, threshold-based segmentation of head CT scans of 101 subjects between 14 and 25 years of age was performed, followed by landmarking, mesh morphing, and projection. Skull and scalp statistical geometry models were then developed as functions of age, sex, stature, BMI, head length, head breadth, and tragion-to-top of head using generalized Procrustes analysis (GPA), principal component analysis (PCA) and multivariate regression analysis. Results: The statistical geometry models account for a high percentage of morphological variations in scalp geometry (R 2 =0.63), outer skull geometry (R 2 =0.66), inner skull geometry (R 2 =0.55), and skull thickness (error < 1 mm) Conclusions: Skull and scalp statistical geometry models accounts for size and shape variations among the adolescent and young adult population were developed as functions of subject covariates. These models may serve as the geometric basis to develop individualized head FE models for injury assessment and design of head-borne equipment.