Predicting and comparing three corrective techniques for sagittal craniosynostosis

Abstract

Sagittal synostosis is the most occurring form of craniosynostosis, resulting in calvarial deformation and possible long-term neurocognitive deficits. Several surgical techniques have been developed to correct these issues. Debates as to the most optimal approach are still ongoing. Finite element method is a computational tool that’s shown to assist with the management of craniosynostosis. The aim of this study was to compare and predict the outcomes of three reconstruction methods for sagittal craniosynostosis. Here, a generic finite element model was developed based on a patient at 4 months of age and was virtually reconstructed under all three different techniques. Calvarial growth was simulated to predict the skull morphology and the impact of different reconstruction techniques on the brain growth up to 60 months of age. Predicted morphology was then compared with in vivo and literature data. Our results show a promising resemblance to morphological outcomes at follow up. Morphological characteristics between considered techniques were also captured in our predictions. Pressure outcomes across the brain highlight the potential impact that different techniques have on growth. This study lays the foundation for further investigation into additional reconstructive techniques for sagittal synostosis with the long-term vision of optimizing the management of craniosynostosis.

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

“…To represent the differentiation of bone stiffness, these elements would have their elastic moduli updated, which varied based on the relative changes in age. See our previous studies for a full detailed description of this approach ( Cross et al, 2021a , b ).…”

“…To account for the contact conditions across the ICV, a Hertzian contact algorithm was implemented between the inner-calvarial table and ICV interfaces. The penalty-based behaviour with a normal contact stiffness of 50 N/mm, a penetration tolerance of 0.5 mm and a friction coefficient of 0.1 allowed for minimal levels of interpenetration between surfaces ( Malde et al, 2020 ; Cross et al, 2021a , b ). While initially in contact, normal/tangential separation was granted during simulated growth.…”

“…In a series of studies, we have previously developed a validated computational model based on the finite element method that enables us to predict the radial expansion of the calvaria as well as the bone formation at the cranial sutures in mice and humans ( Marghoub et al, 2018 ; Marghoub et al, 2019 ; Libby et al, 2017 ; Malde et al, 2020 ; Cross et al, 2021a , b ). In this current work, 1) we present a computational framework that can be used to advance the treatment of various forms of craniosynostosis; 2) we inform and optimise the clinical management of sagittal craniosynostosis.…”

A preliminary analysis of replicating the biomechanics of helmet therapy for sagittal craniosynostosis

“…CT imaging was used to segment the calvarial bones (A), sutures (B), and the ICV (C). All were incorporated to create the preoperative model at 4 months of age (D), adopted from Cross et al [17]. The centre-specific craniotomies (marked in white) were replicated across the parietal bone (E).…”

“…Using these detailed FE models, computational algorithms have been used to investigate the management of craniosynostosis. More advanced models have enabled us to accurately simulate the calvarial growth and bone formation under different types of surgical treatment [14][15][16][17][18][19]. Such models have the capability to investigate the biomechanics of craniosynostosis and to simulate the outcomes of various surgical parameters, such as postoperative helmet therapy.…”