Quantification and statistical analysis on the cranial vault morphology for Chinese children 3–10 years old. (March 2022)

• Record Type: Journal Article
• Authors: Li, Zhigang; Pang, Ziqiang; Qiu, Jinlong; Zhang, Zhenhao; Liu, Xiaochuan; Bai, Chunyu; Wang, Yafeng; Guo, Yazhou
• Is Part Of: Computer methods and programs in biomedicine. Volume 215(2022)
• Publication Date: 2022-03
• Subjects: Child cranial vault -- Morphology -- Growth -- Statistical model; 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.2021.106591
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Three-dimensional cranial vault morphologies of 42 child subjects aged 3–10 years were automatically quantified. A statistical regression cranial vault morphological model as a function of age and head circumference was developed. Multiple child cranial vault morphologies at typical ages were predicted in statistical sense. Variation law of the child cranial vault morphology with age was characterized. Abstract: Background and Objective: Head injury is the leading cause of fatalities and disabilities in children. Characterizing the variation in cranial size/shape and thickness during growth is important for developing finite element models of child heads and evaluating head injury risk at different ages. However, the quantitative morphological features of the cranial vault (size/shape and non-uniform thickness distribution) have not been accounted for in children aged between 3 and 10 years old (YO). Methods: Geometrically equivalent discrete points were identified on 42 head CT scans of 3–10 YO children by separation, curve dividing, and point fitting. Based on discrete points, the principal component analysis and regression (PCA&R) method was used to develop a statistical model of the cranial vault as a function of age and head circumference. Results: The ontogeny of three-dimensional cranial morphology and non-uniform thickness from 3 to 10 years of age was quantified and cranial vault morphologies for 3–10 YO children were generated in 1 year intervals. Conclusions: The automatic method, the procedure of identifying discrete points from CT scans, and the developed quantitative cranial vault model are reliable and accurate.