Cross, Connor Charles James; (2022) Predicting and optimising the postoperative outcomes of sagittal craniosynostosis correction. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

The neonate skull consists of several flat bones, connected by fibrous joints called sutures. Sutures regulate the bone formation along their adjoining edges, while providing mailability to assist with the early phases of rapid brain growth and passing through the birth canal with minimal restriction. By adolescents, these sutures fuse into solid bone, protecting the brain from impacts. The premature fusion of one or more of these sutures is a medical condition known as craniosynostosis, with its most common form being sagittal craniosynostosis (fusion of the midline suture). The condition results in compensatory overgrowth perpendicular to the fused suture, leading to calvarial deformation and possible neurofunctional defects. Surgeons have developed several surgical techniques to restore the normative shape. This has led to debates as to which surgical option provides the most beneficial long term outcome. The overall aim of this thesis was to develop a computational approach using the finite element (FE) method capable of predicting and optimising the long term outcomes for treating sagittal craniosynostosis. A generic 3D pre-operative FE model was developed using patient specific CT data. The FE model was parameterised to predict the long term calvarial growth, the pattern of suture and bone formation, the pattern of bone healing across the replicated surgical techniques, and the changes in contact pressure levels across the modelled brain. All techniques underwent simulated growth up to the maximum age of 76 months. Morphological results were compared against the patient specific CT data at the same age. Where absent, technique specific follow up CT data were used instead. Results highlighted a good morphological agreement between the predicted models and their comparative CT data. The FE model was highly sensitive to the choice of input parameters. Based on the findings of this thesis, the *** approach proved the most optimal across the predicted outcomes. The novel methodology and platform developed here has huge potential to better inform surgeons of the impact various techniques could have on long term outcomes and continue to improve the quality of care for patients undergoing corrective surgery.

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