Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis
Craniosynostosis is the premature fusion of one or more sutures across the calvaria, resulting in morphological and health complications that require invasive corrective surgery. Finite element (FE) method is a powerful tool that can aid with preoperative planning and post-operative predictions of c...
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doaj-e5af4e19d5ec443898c96ab9ad7f7bdd2021-05-26T06:38:28ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2021-05-01910.3389/fcell.2021.621249621249Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal CraniosynostosisConnor Cross0Roman H. Khonsari1Leila Galiay2Giovanna Patermoster3David Johnson4Yiannis Ventikos5Mehran Moazen6Department of Mechanical Engineering, University College London, London, United KingdomService de Chirurgie Maxillo-Faciale et Plastique, Assistance Publique des Hôpitaux de Paris, Paris, FranceService de Chirurgie Maxillo-Faciale et Plastique, Assistance Publique des Hôpitaux de Paris, Paris, FranceDepartment of Neurosurgery, Craniofacial 16 Surgery Unit, Necker–Enfants Malades University Hospital, Assistance Publique–Hôpitaux de 17 Paris, Université de Paris, Paris, FranceOxford Craniofacial Unit, Oxford University Hospital, NHS Foundation Trust, Oxford, United KingdomDepartment of Mechanical Engineering, University College London, London, United KingdomDepartment of Mechanical Engineering, University College London, London, United KingdomCraniosynostosis is the premature fusion of one or more sutures across the calvaria, resulting in morphological and health complications that require invasive corrective surgery. Finite element (FE) method is a powerful tool that can aid with preoperative planning and post-operative predictions of craniosynostosis outcomes. However, input factors can influence the prediction of skull growth and the pressure on the growing brain using this approach. Therefore, the aim of this study was to carry out a series of sensitivity studies to understand the effect of various input parameters on predicting the skull morphology of a sagittal synostosis patient post-operatively. Preoperative CT images of a 4-month old patient were used to develop a 3D model of the skull, in which calvarial bones, sutures, cerebrospinal fluid (CSF), and brain were segmented. Calvarial reconstructive surgery was virtually modeled and two intracranial content scenarios labeled “CSF present” and “CSF absent,” were then developed. FE method was used to predict the calvarial morphology up to 76 months of age with intracranial volume-bone contact parameters being established across the models. Sensitivity tests with regards to the choice of material properties, methods of simulating bone formation and the rate of bone formation across the sutures were undertaken. Results were compared to the in vivo data from the same patient. Sensitivity tests to the choice of various material properties highlighted that the defined elastic modulus for the craniotomies appears to have the greatest influence on the predicted overall skull morphology. The bone formation modeling approach across the sutures/craniotomies had a considerable impact on the level of contact pressure across the brain with minimum impact on the overall predicated morphology of the skull. Including the effect of CSF (based on the approach adopted here) displayed only a slight reduction in brain pressure outcomes. The sensitivity tests performed in this study set the foundation for future comparative studies using FE method to compare outcomes of different reconstruction techniques for the management of craniosynostosis.https://www.frontiersin.org/articles/10.3389/fcell.2021.621249/fullcraniosynostosiscerebrospinal fluidfinite elementcalvarial growthsagittal synostosisbiomechanics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Connor Cross Roman H. Khonsari Leila Galiay Giovanna Patermoster David Johnson Yiannis Ventikos Mehran Moazen |
spellingShingle |
Connor Cross Roman H. Khonsari Leila Galiay Giovanna Patermoster David Johnson Yiannis Ventikos Mehran Moazen Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis Frontiers in Cell and Developmental Biology craniosynostosis cerebrospinal fluid finite element calvarial growth sagittal synostosis biomechanics |
author_facet |
Connor Cross Roman H. Khonsari Leila Galiay Giovanna Patermoster David Johnson Yiannis Ventikos Mehran Moazen |
author_sort |
Connor Cross |
title |
Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis |
title_short |
Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis |
title_full |
Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis |
title_fullStr |
Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis |
title_full_unstemmed |
Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis |
title_sort |
using sensitivity analysis to develop a validated computational model of post-operative calvarial growth in sagittal craniosynostosis |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Cell and Developmental Biology |
issn |
2296-634X |
publishDate |
2021-05-01 |
description |
Craniosynostosis is the premature fusion of one or more sutures across the calvaria, resulting in morphological and health complications that require invasive corrective surgery. Finite element (FE) method is a powerful tool that can aid with preoperative planning and post-operative predictions of craniosynostosis outcomes. However, input factors can influence the prediction of skull growth and the pressure on the growing brain using this approach. Therefore, the aim of this study was to carry out a series of sensitivity studies to understand the effect of various input parameters on predicting the skull morphology of a sagittal synostosis patient post-operatively. Preoperative CT images of a 4-month old patient were used to develop a 3D model of the skull, in which calvarial bones, sutures, cerebrospinal fluid (CSF), and brain were segmented. Calvarial reconstructive surgery was virtually modeled and two intracranial content scenarios labeled “CSF present” and “CSF absent,” were then developed. FE method was used to predict the calvarial morphology up to 76 months of age with intracranial volume-bone contact parameters being established across the models. Sensitivity tests with regards to the choice of material properties, methods of simulating bone formation and the rate of bone formation across the sutures were undertaken. Results were compared to the in vivo data from the same patient. Sensitivity tests to the choice of various material properties highlighted that the defined elastic modulus for the craniotomies appears to have the greatest influence on the predicted overall skull morphology. The bone formation modeling approach across the sutures/craniotomies had a considerable impact on the level of contact pressure across the brain with minimum impact on the overall predicated morphology of the skull. Including the effect of CSF (based on the approach adopted here) displayed only a slight reduction in brain pressure outcomes. The sensitivity tests performed in this study set the foundation for future comparative studies using FE method to compare outcomes of different reconstruction techniques for the management of craniosynostosis. |
topic |
craniosynostosis cerebrospinal fluid finite element calvarial growth sagittal synostosis biomechanics |
url |
https://www.frontiersin.org/articles/10.3389/fcell.2021.621249/full |
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