UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A population-specific material model for sagittal craniosynostosis to predict surgical shape outcomes

Borghi, A; Rodriguez Florez, N; Ruggiero, F; James, G; O'Hara, J; Ong, J; Jeelani, O; ... Schievano, S; + view all (2019) A population-specific material model for sagittal craniosynostosis to predict surgical shape outcomes. Biomechanics and Modeling in Mechanobiology 10.1007/s10237-019-01229-y. (In press). Green open access

[thumbnail of Borghi2019_Article_APopulation-specificMaterialMo.pdf]
Preview
Text
Borghi2019_Article_APopulation-specificMaterialMo.pdf - Published Version

Download (2MB) | Preview

Abstract

Sagittal craniosynostosis consists of premature fusion (ossification) of the sagittal suture during infancy, resulting in head deformity and brain growth restriction. Spring-assisted cranioplasty (SAC) entails skull incisions to free the fused suture and insertion of two springs (metallic distractors) to promote cranial reshaping. Although safe and effective, SAC outcomes remain uncertain. We aimed hereby to obtain and validate a skull material model for SAC outcome prediction. Computed tomography data relative to 18 patients were processed to simulate surgical cuts and spring location. A rescaling model for age matching was created using retrospective data and validated. Design of experiments was used to assess the effect of different material property parameters on the model output. Subsequent material optimization-using retrospective clinical spring measurements-was performed for nine patients. A population-derived material model was obtained and applied to the whole population. Results showed that bone Young's modulus and relaxation modulus had the largest effect on the model predictions: the use of the population-derived material model had a negligible effect on improving the prediction of on-table opening while significantly improved the prediction of spring kinematics at follow-up. The model was validated using on-table 3D scans for nine patients: the predicted head shape approximated within 2 mm the 3D scan model in 80% of the surface points, in 8 out of 9 patients. The accuracy and reliability of the developed computational model of SAC were increased using population data: this tool is now ready for prospective clinical application.

Type: Article
Title: A population-specific material model for sagittal craniosynostosis to predict surgical shape outcomes
Location: Germany
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10237-019-01229-y
Publisher version: https://doi.org/10.1007/s10237-019-01229-y
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Craniofacial surgery, Design of experiments, Finite element modelling, Scaphocephaly, Spring cranioplasty
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Childrens Cardiovascular Disease
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Biology and Cancer Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10083017
Downloads since deposit
99Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item