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Predicting calvarial morphology in sagittal craniosynostosis

Early fusion of the sagittal suture is a clinical condition called, sagittal craniosynostosis. Calvarial reconstruction is the most common treatment option for this condition with a range of techniques being developed by different groups. Computer simulations have a huge potential to predict the cal...

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Autores principales: Malde, Oyvind, Cross, Connor, Lim, Chien L., Marghoub, Arsalan, Cunningham, Michael L., Hopper, Richard A., Moazen, Mehran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949270/
https://www.ncbi.nlm.nih.gov/pubmed/31913294
http://dx.doi.org/10.1038/s41598-019-55224-5
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author Malde, Oyvind
Cross, Connor
Lim, Chien L.
Marghoub, Arsalan
Cunningham, Michael L.
Hopper, Richard A.
Moazen, Mehran
author_facet Malde, Oyvind
Cross, Connor
Lim, Chien L.
Marghoub, Arsalan
Cunningham, Michael L.
Hopper, Richard A.
Moazen, Mehran
author_sort Malde, Oyvind
collection PubMed
description Early fusion of the sagittal suture is a clinical condition called, sagittal craniosynostosis. Calvarial reconstruction is the most common treatment option for this condition with a range of techniques being developed by different groups. Computer simulations have a huge potential to predict the calvarial growth and optimise the management of this condition. However, these models need to be validated. The aim of this study was to develop a validated patient-specific finite element model of a sagittal craniosynostosis. Here, the finite element method was used to predict the calvarial morphology of a patient based on its preoperative morphology and the planned surgical techniques. A series of sensitivity tests and hypothetical models were carried out and developed to understand the effect of various input parameters on the result. Sensitivity tests highlighted that the models are sensitive to the choice of input parameter. The hypothetical models highlighted the potential of the approach in testing different reconstruction techniques. The patient-specific model highlighted that a comparable pattern of calvarial morphology to the follow up CT data could be obtained. This study forms the foundation for further studies to use the approach described here to optimise the management of sagittal craniosynostosis.
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spelling pubmed-69492702020-01-13 Predicting calvarial morphology in sagittal craniosynostosis Malde, Oyvind Cross, Connor Lim, Chien L. Marghoub, Arsalan Cunningham, Michael L. Hopper, Richard A. Moazen, Mehran Sci Rep Article Early fusion of the sagittal suture is a clinical condition called, sagittal craniosynostosis. Calvarial reconstruction is the most common treatment option for this condition with a range of techniques being developed by different groups. Computer simulations have a huge potential to predict the calvarial growth and optimise the management of this condition. However, these models need to be validated. The aim of this study was to develop a validated patient-specific finite element model of a sagittal craniosynostosis. Here, the finite element method was used to predict the calvarial morphology of a patient based on its preoperative morphology and the planned surgical techniques. A series of sensitivity tests and hypothetical models were carried out and developed to understand the effect of various input parameters on the result. Sensitivity tests highlighted that the models are sensitive to the choice of input parameter. The hypothetical models highlighted the potential of the approach in testing different reconstruction techniques. The patient-specific model highlighted that a comparable pattern of calvarial morphology to the follow up CT data could be obtained. This study forms the foundation for further studies to use the approach described here to optimise the management of sagittal craniosynostosis. Nature Publishing Group UK 2020-01-08 /pmc/articles/PMC6949270/ /pubmed/31913294 http://dx.doi.org/10.1038/s41598-019-55224-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Malde, Oyvind
Cross, Connor
Lim, Chien L.
Marghoub, Arsalan
Cunningham, Michael L.
Hopper, Richard A.
Moazen, Mehran
Predicting calvarial morphology in sagittal craniosynostosis
title Predicting calvarial morphology in sagittal craniosynostosis
title_full Predicting calvarial morphology in sagittal craniosynostosis
title_fullStr Predicting calvarial morphology in sagittal craniosynostosis
title_full_unstemmed Predicting calvarial morphology in sagittal craniosynostosis
title_short Predicting calvarial morphology in sagittal craniosynostosis
title_sort predicting calvarial morphology in sagittal craniosynostosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949270/
https://www.ncbi.nlm.nih.gov/pubmed/31913294
http://dx.doi.org/10.1038/s41598-019-55224-5
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