A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis

Abstract

The neonate skull consists of several bony plates, connected by fibrous soft tissue called sutures. Premature fusion of sutures is a medical condition known as craniosynostosis. Sagittal synostosis, caused by premature fusion of the sagittal suture, is the most common form of this condition. The optimum management of this condition is an ongoing debate in the craniofacial community while aspects of the biomechanics and mechanobiology are not well understood. Here, we describe a computational framework that enables us to predict and compare the calvarial growth following different reconstruction techniques for the management of sagittal synostosis. Our results demonstrate how different reconstruction techniques interact with the increasing intracranial volume. The framework proposed here can be used to inform optimum management of different forms of craniosynostosis, minimising the risk of functional consequences and secondary surgery.

Keywords: biomechanics; calvarial bones; finite element method; sagittal synostosis; skull growth; sutures.

FIGURE 1

Using CT -scan data at an initial preoperative age of 4 months (A), a 3D finite element model was developed (B). The chosen surgical technique at the appropriate age was performed (C). The intracranial volume was then expanded to a specified volume in 6 load steps (D). Elements across the sutures and craniotomies were then selected based on the level of hydrostatic strain and/or radii process from the bone lining, following the algorithm described in the flow chart. This process was repeated while updating the material properties and geometry of the model at each load step until the final load step has been reached (E). The intracranial volume at the final load step was equivalent to 76 months of age.

FIGURE 2

Predicted bone formation across all replicated techniques at the postoperative age of 76 months. The material properties across the newly and previously selected elements were updated at each load step.

FIGURE 3

Cephalometric measurements across all replicated approaches. The predicted length (A) and width (B) were used to calculate the cephalic index (C). The circumference (D) was measured in the transverse plane as shown within the diagram.

FIGURE 4

The predicted contact pressure captured across the brain surface for all replicated techniques at 76 months (A). Contact pressure was quantified across different regions of the ICV for each replicated technique with standard deviations corresponding to the nodal distribution across the highlighted regions (B). All results were recorded at 76 months of age.