Abstract：
BACKGROUND: Endoscopic strip craniectomy followed by helmet therapy (ESCH) is a minimally invasive approach for correcting sagittal craniosynostosis. The treatment involves a patient-specific helmet designed to facilitate lateral growth while constraining sagittal expansion. In this study, finite element modelling was used to predict post-treatment head reshaping, improving our comprehension of the necessary helmet therapy duration.
METHODS: Six patients (aged 11 weeks to 9 months) who underwent ESCH at Connecticut Children\'s Hospital were enrolled in this study. Day-1 post-operative 3D scans were used to create skin, skull, and intracranial volume models. Patient-specific helmet models, incorporating areas for growth, were designed based on post-operative imaging. Brain growth was simulated through thermal expansion, and treatments were modelled according to post-operative Imaging available. Mechanical testing and finite element modelling were combined to determine patient-specific mechanical properties from bone samples collected from surgery. Validation compared simulated end-of-treatment skin surfaces with optical scans in terms of shape matching and cranial index estimation.
RESULTS: Comparison between the simulated post-treatment head shape and optical scans showed that on average 97.3 ± 2.1 % of surface data points were within a distance range of -3 to 3 mm. The cranial index was also accurately predicted (r = 0.91).
CONCLUSIONS: In conclusion, finite element models effectively predicted the ESCH cranial remodeling outcomes up to 8 months postoperatively. This computational tool offers valuable insights to guide and refine helmet treatment duration. This study also incorporated patient-specific material properties, enhancing the accuracy of the modeling approach.
METHODS: Six patients (aged 11 weeks to 9 months) who underwent ESCH at Connecticut Children\'s Hospital were enrolled in this study. Day-1 post-operative 3D scans were used to create skin, skull, and intracranial volume models. Patient-specific helmet models, incorporating areas for growth, were designed based on post-operative imaging. Brain growth was simulated through thermal expansion, and treatments were modelled according to post-operative Imaging available. Mechanical testing and finite element modelling were combined to determine patient-specific mechanical properties from bone samples collected from surgery. Validation compared simulated end-of-treatment skin surfaces with optical scans in terms of shape matching and cranial index estimation.
RESULTS: Comparison between the simulated post-treatment head shape and optical scans showed that on average 97.3 ± 2.1 % of surface data points were within a distance range of -3 to 3 mm. The cranial index was also accurately predicted (r = 0.91).
CONCLUSIONS: In conclusion, finite element models effectively predicted the ESCH cranial remodeling outcomes up to 8 months postoperatively. This computational tool offers valuable insights to guide and refine helmet treatment duration. This study also incorporated patient-specific material properties, enhancing the accuracy of the modeling approach.
摘要：
背景：内镜下带状颅骨切除术后再进行头盔治疗（ESCH）是一种用于矫正矢状位颅骨融合的微创方法。治疗涉及患者特异性头盔，旨在促进横向生长，同时限制矢状扩张。在这项研究中,有限元模型用于预测治疗后的头部重塑，提高我们对必要的头盔治疗持续时间的理解。
方法：在康涅狄格州儿童医院接受ESCH治疗的6名患者（年龄11周至9个月）被纳入本研究。术后第一天3D扫描用于创建皮肤，头骨,和颅内容量模型。针对患者的头盔模型，纳入增长领域，是基于术后影像学设计的。通过热膨胀模拟大脑生长，根据现有的术后影像学对治疗进行建模。机械测试和有限元建模相结合，以确定从手术收集的骨骼样本中患者特定的机械性能。验证在形状匹配和颅骨指数估计方面将模拟的治疗结束皮肤表面与光学扫描进行了比较。
结果：模拟的后处理头部形状和光学扫描之间的比较表明，平均97.3±2.1％的表面数据点在-3至3mm的距离范围内。颅骨指数也被准确预测(r=0.91)。
结论：结论：有限元模型可有效预测术后8个月ESCH颅骨重塑结果。这个计算工具提供了有价值的见解，以指导和完善头盔治疗的持续时间。这项研究还纳入了患者特定的材料特性，提高建模方法的准确性。
方法：在康涅狄格州儿童医院接受ESCH治疗的6名患者（年龄11周至9个月）被纳入本研究。术后第一天3D扫描用于创建皮肤，头骨,和颅内容量模型。针对患者的头盔模型，纳入增长领域，是基于术后影像学设计的。通过热膨胀模拟大脑生长，根据现有的术后影像学对治疗进行建模。机械测试和有限元建模相结合，以确定从手术收集的骨骼样本中患者特定的机械性能。验证在形状匹配和颅骨指数估计方面将模拟的治疗结束皮肤表面与光学扫描进行了比较。
结果：模拟的后处理头部形状和光学扫描之间的比较表明，平均97.3±2.1％的表面数据点在-3至3mm的距离范围内。颅骨指数也被准确预测(r=0.91)。
结论：结论：有限元模型可有效预测术后8个月ESCH颅骨重塑结果。这个计算工具提供了有价值的见解，以指导和完善头盔治疗的持续时间。这项研究还纳入了患者特定的材料特性，提高建模方法的准确性。
