Deliège, Lara;
(2024)
Translating numerical predictive tools for the correction of craniosynostosis into clinical practice.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
In recent years, the field of craniofacial surgery has seen significant advancements with the introduction of computer aided modelling, personalised implant solutions and surgical cutting guides, leading to notable improvements in procedural outcomes. Despite the proven clinical benefits, minimally invasive craniofacial procedures, such as spring- assisted cranioplasty or cranial moulding treatments, continue to yield less predictable results compared to traditional open surgeries, with success often depending on the experience of the operating centre. Although computational modelling holds the potential to enhance these outcomes, to date it has predominantly remained confined to research settings. The overall aim of this project was to develop, validate and implement modelling methodologies to facilitate surgical planning for minimally invasive craniofacial surgeries, and integrate them into clinical practice. Finite element models to predict post-treatment intracranial volume and/or skull shape outcomes for craniosynostosis corrections were developed. Population-specific material properties of syndromic and non-syndromic bone were retrieved via experimental testing and included in the modelling. This methodology was tested and validated for three distinct craniofacial surgeries routinely performed at Great Ormond Street Hospital for Children (London) and Connecticut Children’s Hospital (Hartford, CT), including classic and vertical vector posterior vault expansion as well as endoscopic strip craniectomy followed by helmet therapy. Additionally, for classic spring-assisted posterior vault expansion, we designed as a proof of concept an augmented reality application that demonstrates the feasibility of bringing this predictive modelling tool directly into the operating theatre. This thesis demonstrates that combining clinical imaging and finite element modelling tools can provide a powerful platform to predict several surgeries outcomes. Results suggests a possible improvement of surgical planning in terms of postoperative head shape and intracranial volume as well as a better understanding of population-specific cranium properties, thereby promoting translation of advanced computational analysis techniques into clinical practice.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Translating numerical predictive tools for the correction of craniosynostosis into clinical practice |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194502 |
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