Digital Child Project
Goal
Development of Age-Dependent Pediatric Head and Neck Computational Models
Introduction
Traumatic brain and neck injuries, especially resulting from traffic accidents, are major causes of morbidity and mortality in the United States and elsewhere. As such, precise prediction of injury criteria is crucial to the proper design of safety devices intended to reduce the risk of these injuries. Head and neck injury mechanisms are difficult to study experimentally due not only to a variety of impact scenarios but also to important ethical issues involving use of human cadavers and animals. Therefore, computational simulation has contributed greatly not only to virtual impact tests but also to examination of human injury mechanisms.
To date, common practice for modeling and simulation of pediatric head and neck injuries has been to scale adult data, due to limited availability of child donors. The scaling rules are not well validated due to lack of understanding of unique structures and material properties in the child anatomy. The goal of the proposed research is to construct anatomically accurate, high-fidelity, age-dependent pediatric head and neck computational models.
Specific Aims
The overall aim of the proposed project is to create computational models of the pediatric head and neck that will be used for the investigation of injury mechanics due to impact and whiplash. Age-dependent computational models will be developed across age groups from 6 months to 10 years. The following specific aims will be accomplished in collaboration with SCIB researchers from the University of Pennsylvania, the University of Virginia, Wayne State University and Duke University:
Specific Aim #1
To develop a repository of anatomically accurate, high-fidelity digital geometry and meshes of the head and neck involving children across an age group from 6 months to 10 years. The head and neck geometry will be generated based on medical images from CT/MRI data.
Specific Aim #2
To develop an optimal material properties database needed to perform high-fidelity numerical simulations in the study of impact-related injuries. A material identification method will be applied to estimate realistic nonlinear material properties.
Specific Aim #3
To use the optimized computational models in the study realistic pediatric injury scenarios, such as impact and whiplash. The computational models will be validated with experimental data.
Approaches
We have developed an octree-based unstructured hexahedral mesh generation method to create reasonable-quality meshes automatically from triangulated surface models. The starting point of the mesh generation can be either original medical image data or triangulated surface models extracted from the medical image data. Since the latter approach is more flexible to control noises in the medical image data, we use the triangulated surface models as input.