%0 Journal Article
%T A computational modelling tool for prediction of head reshaping following endoscopic strip craniectomy and helmet therapy for the treatment of scaphocephaly.
%A Deliege L
%A Carriero A
%A Ong J
%A James G
%A Jeelani O
%A Dunaway D
%A Stoltz P
%A Hersh D
%A Martin J
%A Carroll K
%A Chamis M
%A Schievano S
%A Bookland M
%A Borghi A
%J Comput Biol Med
%V 177
%N 0
%D 2024 Jul 23
%M 38805810
%F 6.698
%R 10.1016/j.compbiomed.2024.108633
%X <B>BACKGROUND: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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 3 mm. The cranial index was also accurately predicted (r = 0.91).<BR><B>CONCLUSIONS: </B>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.