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Spring assisted cranioplasty: A patient specific computational model.

Alessandro Borghi1, Naiara Rodriguez-Florez1, Will Rodgers1

  • 1UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH.

Medical Engineering & Physics
|January 24, 2018
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Summary

A new computational model accurately predicts final head shape after spring assisted cranioplasty (SAC) for sagittal craniosynostosis, improving treatment outcomes.

Keywords:
CraniosynostosisFinite element modelingSpring cranioplasty

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Area of Science:

  • Biomedical Engineering
  • Computational Modeling
  • Craniofacial Surgery

Background:

  • Sagittal craniosynostosis treatment using spring distractors shows promise but has unpredictable outcomes.
  • Understanding the skull-distractor interaction is crucial for optimizing results.
  • Novel computational models are needed to predict individual head shape post-surgery.

Purpose of the Study:

  • To develop a patient-specific computational model for spring assisted cranioplasty (SAC).
  • To predict the final head shape in infants undergoing SAC.
  • To enhance pre-operative planning and distractor design.

Main Methods:

  • Created a 3D skull model from patient CT scans.
  • Simulated spring implantation using mechanical data and literature-based viscoelastic bone properties.
  • Tuned model parameters with surgical and follow-up X-ray data.
  • Validated model predictions against post-operative 3D head scans.

Main Results:

  • The model accurately predicted spring expansion during surgery (within 9%) and follow-ups (within 8%).
  • Numerical results closely matched post-operative 3D head scans, validating predictive capability.
  • Demonstrated successful prediction of the overall final head shape for the patient.

Conclusions:

  • Computational modeling shows potential for studying SAC and improving surgical planning.
  • Patient-specific models can aid in predicting individual outcomes for craniosynostosis treatment.
  • This approach can guide the design of improved spring distractor devices.