A computational modelling tool for prediction of head reshaping following endoscopic strip craniectomy and helmet therapy for the treatment of scaphocephaly

Deliege, Lara; Carriero, Alessandra; Ong, Juling; James, Greg; Jeelani, Owase; Dunaway, David; Stoltz, Petronella; Hersh, David; Martin, Jonathan; Carroll, Kathleeen; Chamis, Megan; Schievano, Silvia; Bookland, Markus; Borghi, Alessandro

Abstract

Endoscopic strip craniectomy followed by helmet therapy (ESCH) is a minimally invasive approach for correcting sagittal craniosynostosis. The treatment involves a patient-specific helmet designed to facilitate lateral growth while constraining sagittal expansion. In this study, finite element modelling was used to predict post-treatment head reshaping, improving our comprehension of the necessary helmet therapy duration. Six patients (aged 11 weeks to 9 months) who underwent ESCH at Connecticut Children's Hospital were enrolled in this study. Day-1 post-operative 3D scans were used to create skin, skull, and intracranial volume models. Patient-specific helmet models, incorporating areas for growth, were designed based on post-operative imaging. Brain growth was simulated through thermal expansion, and treatments were modelled according to post-operative Imaging available. Mechanical testing and finite element modelling were combined to determine patient-specific mechanical properties from bone samples collected from surgery. Validation compared simulated end-of-treatment skin surfaces with optical scans in terms of shape matching and cranial index estimation. Comparison between the simulated post-treatment head shape and optical scans showed that on average 97.3±2.1% of surface data points were within a distance range of -3 to 3mm. The cranial index was also accurately predicted (r=0.91). In conclusion, finite element models effectively predicted the ESCH cranial remodeling outcomes up to 8 months postoperatively. This computational tool offers valuable insights to guide and refine helmet treatment duration. This study also incorporated patient-specific material properties, enhancing the accuracy of the modeling approach. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.]

Citation

Deliege, L., Carriero, A., Ong, J., James, G., Jeelani, O., Dunaway, D., …Borghi, A. (2024). A computational modelling tool for prediction of head reshaping following endoscopic strip craniectomy and helmet therapy for the treatment of scaphocephaly. Computers in Biology and Medicine, 177, Article 108633. https://doi.org/10.1016/j.compbiomed.2024.108633

• Journal Article Type: Article
• Acceptance Date: May 18, 2024
• Online Publication Date: May 23, 2024
• Publication Date: 2024-07
• Deposit Date: May 24, 2024
• Publicly Available Date: Jun 7, 2024
• Journal: Computers in Biology and Medicine
• Print ISSN: 0010-4825
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 177
• Article Number: 108633
• DOI: https://doi.org/10.1016/j.compbiomed.2024.108633
• Keywords: Helmet therapy, Endoscopic strip craniectomy, Finite element modelling, Craniosynostosis, Pre-operative planning
• Public URL: https://durham-repository.worktribe.com/output/2453753