Abstract
Pathological conditions of the pediatric craniofacial skeleton commonly occur from traumatic injuries and congenital birth defects. Corrective surgeries are often necessary to treat these conditions. Due to the capacity for rapid growth of the developing pediatric skull, these surgeries involve removing large portions of the pathological skull tissue, and relying on the regrowth of the skull tissue. There are a number of challenges associated with these corrective surgeries stemming primarily from a lack of information and understanding of the properties and growth of the pediatric skull. These challenges include an elevated risk of skull fracture from surgical defects, an incompatibility of surgical hardware originally designed for adult skull tissue and scaled in size for children, and an unknown regrowth pattern of the skull following the surgery. Improving our understanding of the pediatric skull, both in terms of mechanical property information and the development patterns of the pediatric skull with age, will ultimately help reduce the challenges with pediatric craniofacial surgery. Therefore, the goal of this research is to improve our understanding of the pediatric skull by using a two-phased approach. The first phase involved experimental testing of pediatric cranial bone to identify its microstructure and mechanical properties. The second phase involved developing an analytical model of pediatric skull tissue growth, applying this model to a computational framework that simulates the growth and development of a skull from 6 months to 2 years in age, and then investigating how parameters in the tissue growth model influence the prediction of the pediatric skull shape, mechanical properties, and skull thickness.
In the first phase, eight fresh, never frozen, pediatric skull tissue specimens were collected in the operating room during pediatric craniosynostosis surgery. The normally discarded tissue was obtained from patients ranging in age from 4 to 10 months. Up to 12 individual samples were harvested and prepared from each specimen for mechanical four-point bending testing to failure. The microstructure of each sample was analyzed using micro-computed tomography before and after each mechanical test. From this analysis, effective geometric and mechanical properties were determined for each sample (n = 68). Test results demonstrated that the pediatric skull is 2.0 mm (+/- 0.4) thick, with a porosity of approximately 80%, The effective Young’s modulus of the pediatric skull tissue, determined using Euler beam theory, was 4.2 GPa (+/- 2.1), which was approximately three times less stiff than adult skull tissue reported in the literature. Furthermore, the pediatric skull was able to bend up to tensile failure strains of 6.7% (+/- 2.0), which was approximately five times larger than failure strains measured in adult skull. The disparity between the measured mechanical properties of pediatric skull tissue and adult skull tissue highlights the need to reevaluate the design of surgical hardware used in pediatric cranial surgery to be more compatible with the pediatric tissue.
Using the mechanical data collected in this study, the second phase of the research involved the development of a computational model of skull growth to investigate how the natural growth and expansion of the pediatric brain influence the shape and structural growth patterns of the pediatric skull. Skull tissue growth was modeled by simulating bone remodeling (resorption and growth) using the biomechanical loading (tissue stresses and strains) distributed throughout the pediatric skull during brain growth. This tissue growth model was implemented into a finite element (FE) model of a 6-month old pediatric skull that was developed using published skull morphology data. An iterative growth process was applied to the FE model, with each iteration corresponding to discrete week in age. For each iteration, the biomechanical responses of the skull model were calculated through simulation of an increment of brain volume corresponding to natural growth rates. The resulting loading distribution calculated throughout the skull model was used to update the skull tissue geometry, modulus, and thickness based on a skull tissue growth model. Simulations of growth were performed up to 2 years of age, and the final skull shape and stiffness was validated against limited available literature. Furthermore, a parametric analysis was performed to investigate the factors contributing to the underlying growth patterns of the skull. This pediatric skull growth model was the first of its kind in the field, and with the wider availability of pediatric data, it serves as a platform for future development and capability as a tool to predict healthy and post-operative pediatric skull growth.
Overall, the insight from this thesis will help to address the underlying challenges associated with pediatric craniofacial surgeries. By facilitating development of surgical hardware that is specific to the unique characteristics of the pediatric skull and enabling understanding of the growth patterns of the pediatric skull, the work of this thesis will comprehensively maximize the well-being of pediatric surgical patients moving forward.
