An Overview of Modelling Craniosynostosis Using the Finite Element Method.

Craniosynostosis is a medical condition caused by the early fusion of the cranial joint. The finite element method (FEM) is a computational technique that can answer a variety of "what if" questions in relation to the biomechanics of this condition. The aim of this study was to review the current literature that has used FEM to investigate the biomechanics of any aspect of craniosynostosis, being its development or its reconstruction. This review highlights that a relatively small number of studies (n = 10) has used FEM to investigate the biomechanics of craniosynostosis. Current studies set a good foundation for the future to take advantage of this method and optimize reconstruction of various forms of craniosynostosis.

Characterizing and Modeling Bone Formation during Mouse Calvarial Development.

The newborn mammalian cranial vault consists of five flat bones that are joined together along their edges by soft fibrous tissues called sutures. Early fusion of these sutures leads to a medical condition known as craniosynostosis. The mechanobiology of normal and craniosynostotic skull growth is not well understood. In a series of previous studies, we characterized and modeled radial expansion of normal and craniosynostotic (Crouzon) mice. Here, we describe a new modeling algorithm to simulate bone formation at the sutures in normal and craniosynostotic mice. Our results demonstrate that our modeling approach is capable of predicting the observed ex vivo pattern of bone formation at the sutures in the aforementioned mice. The same approach can be used to model different calvarial reconstruction in children with craniosynostosis to assist in the management of this complex condition.

Predicting calvarial growth in normal and craniosynostotic mice using a computational approach.

During postnatal calvarial growth the brain grows gradually and the overlying bones and sutures accommodate that growth until the later juvenile stages. The whole process is coordinated through a complex series of biological, chemical and perhaps mechanical signals between various elements of the craniofacial system. The aim of this study was to investigate to what extent a computational model can accurately predict the calvarial growth in wild-type (WT) and mutant type (MT) Fgfr2C342Y/+ mice displaying bicoronal suture fusion. A series of morphological studies were carried out to quantify the calvarial growth at P3, P10 and P20 in both mouse types. MicroCT images of a P3 specimen were used to develop a finite element model of skull growth to predict the calvarial shape of WT and MT mice at P10. Sensitivity tests were performed and the results compared with ex vivo P10 data. Although the models were sensitive to the choice of input parameters, they predicted the overall skull growth in the WT and MT mice. The models also captured the difference between the ex vivoWT and MT mice. This modelling approach has the potential to be translated to human skull growth and to enhance our understanding of the different reconstruction methods used to manage clinically the different forms of craniosynostosis, and in the long term possibly reduce the number of re-operations in children displaying this condition and thereby enhance their quality of life.

Modelling human skull growth: a validated computational model.

During the first year of life, the brain grows rapidly and the neurocranium increases to about 65% of its adult size. Our understanding of the relationship between the biomechanical forces, especially from the growing brain, the craniofacial soft tissue structures and the individual bone plates of the skull vault is still limited. This basic knowledge could help in the future planning of craniofacial surgical operations. The aim of this study was to develop a validated computational model of skull growth, based on the finite-element (FE) method, to help understand the biomechanics of skull growth. To do this, a two-step validation study was carried out. First, an in vitro physical three-dimensional printed model and an in silico FE model were created from the same micro-CT scan of an infant skull and loaded with forces from the growing brain from zero to two months of age. The results from the in vitro model validated the FE model before it was further developed to expand from 0 to 12 months of age. This second FE model was compared directly with in vivo clinical CT scans of infants without craniofacial conditions (n = 56). The various models were compared in terms of predicted skull width, length and circumference, while the overall shape was quantified using three-dimensional distance plots. Statistical analysis yielded no significant differences between the male skull models. All size measurements from the FE model versus the in vitro physical model were within 5%, with one exception showing a 7.6% difference. The FE model and in vivo data also correlated well, with the largest percentage difference in size being 8.3%. Overall, the FE model results matched well with both the in vitro and in vivo data. With further development and model refinement, this modelling method could be used to assist in preoperative planning of craniofacial surgery procedures and could help to reduce reoperation rates.