Borghi Alessandro, Rodriguez-Florez Naiara, Rodgers Will, James Gregory, Hayward Richard, Dunaway David, Jeelani Owase, Schievano Silvia
UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH.
UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH.
Med Eng Phys. 2018 Mar;53:58-65. doi: 10.1016/j.medengphy.2018.01.001. Epub 2018 Jan 19.
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.
植入弹簧式撑开器治疗矢状缝早闭是一种新技术，已证明在功能和美学上对矫正颅骨畸形有效；然而，由于对颅骨与撑开器相互作用的理解不完整，最终的形状结果仍存在一定程度的不可预测性。本研究的目的是创建一个患者特异性的弹簧辅助颅骨成形术（SAC）计算模型，该模型有助于预测个体的整体最终头部形状。对一名SAC患者的术前计算机断层扫描图像进行处理，以提取婴儿颅骨解剖结构的三维模型并模拟弹簧植入。撑开器基于力学实验数据进行建模。利用手术期间记录的特定患者手术信息以及随访时的x光测量结果，对文献中的粘弹性骨特性进行了调整。该模型准确地捕捉了台上弹簧扩张情况（在测量值的9%以内），以及第一次和第二次随访时的情况（在测量值的8%以内）。对该患者术后立即进行的三维头部扫描与数值结果的比较证明，该模型能够成功预测最终的整体头部形状。这项初步工作展示了计算建模在研究SAC中的潜在应用，以支持术前规划并指导新型撑开器的设计。
