Spring assisted cranioplasty: A patient specific computational model. (March 2018)

• Record Type: Journal Article
• Authors: Borghi, Alessandro; Rodriguez-Florez, Naiara; Rodgers, Will; James, Gregory; Hayward, Richard; Dunaway, David; Jeelani, Owase; Schievano, Silvia
• Is Part Of: Medical engineering & physics. Volume 53(2018)
• Page Start: 58
• Page End: 65
• Publication Date: 2018-03
• Subjects: Craniosynostosis -- Spring cranioplasty -- Finite element modeling; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2018.01.001
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: A patient specific sagittal craniosynostosis cranioplasty model is proposed. The model captures spring expansion due to skull viscoelastic adaptation. The finite element model is validated using post-operative 3D surface scanning. Abstract: Implantation of spring-like distractors in the treatment of sagittal craniosynostosis is a novel technique that has proven functionally and aesthetically effective in correcting skull deformities; however, final shape outcomes remain moderately unpredictable due to an incomplete understanding of the skull-distractor interaction. The aim of this study was to create a patient specific computational model of spring assisted cranioplasty (SAC) that can help predict the individual overall final head shape. Pre-operative computed tomography images of a SAC patient were processed to extract a 3D model of the infant skull anatomy and simulate spring implantation. The distractors were modeled based on mechanical experimental data. Viscoelastic bone properties from the literature were tuned using the specific patient procedural information recorded during surgery and from x-ray measurements at follow-up. The model accurately captured spring expansion on-table (within 9% of the measured values), as well as at first and second follow-ups (within 8% of the measured values). Comparison between immediate post-operative 3D head scanning and numerical results for this patient proved that the model could successfully predict the final overall head shape. This preliminary work showed the potential application of computational modeling to study SAC, to support pre-operative planning and guide novel distractor design.