A Parametric Thoracic Spine Model Accounting for Geometric Variations by Age, Sex, Stature, and Body Mass Index

Abstract

In this study, a parametric thoracic spine (T-spine) model was developed to account for morphological variations among the adult population. A total of 84 CT scans were collected, and the subjects were evenly distributed among age groups and both sexes. CT segmentation, landmarking, and mesh morphing were performed to map a template mesh onto the T-spine vertebrae for each sampled subject. Generalized procrustes analysis (GPA), principal component analysis (PCA), and linear regression analysis were then performed to investigate the morphological variations and develop prediction models. A total of 13 statistical models, including 12 T-spine vertebrae and a spinal curvature model, were combined to predict a full T-spine 3D geometry with any combination of age, sex, stature, and body mass index (BMI). A leave-one-out root mean square error (RMSE) analysis was conducted for each node of the mesh predicted by the statistical model for every T-spine vertebra. Most of the RMSEs were less than 2 mm across the 12 vertebral levels, indicating good accuracy. The presented parametric T-spine model can serve as a geometry basis for parametric human modeling or future crash test dummy designs to better assess T-spine injuries accounting for human diversity.