A computational modelling tool for prediction of head reshaping following endoscopic strip craniectomy and helmet therapy for the treatment of scaphocephaly.

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. METHOD: 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.

Spring-assisted posterior vault expansion: a parametric study to improve the intracranial volume increase prediction.

Spring-assisted posterior vault expansion has been adopted at the London Great Ormond Street Hospital for Children to treat raised intracranial pressure in patients affected by syndromic craniosynostosis, a congenital calvarial anomaly causing the premature fusion of skull sutures. This procedure involves elastic distractors used to dynamically reshape the skull and increase the intracranial volume (ICV). In this study, we developed and validated a patient-specific model able to predict the ICV increase and carried out a parametric study to investigate the effect of surgical parameters on that final volume. Pre- and post-operative computed tomography data relative to 18 patients were processed to extract simplified patient-specific skull shape, replicate surgical cuts, and simulate spring expansion. A parametric study was performed to quantify each parameter's impact on the surgical outcome: for each patient, the osteotomy location was varied in a pre-defined range; local sensitivity of the predicted ICV to each parameter was analysed and compared. Results showed that the finite element model performed well in terms of post-operative ICV prediction and allowed for parametric optimization of surgical cuts. The study indicates how to optimize the ICV increase according to the type of procedure and provides indication on the most robust surgical strategy.

Validation of an in-silico modelling platform for outcome prediction in spring assisted posterior vault expansion.

BACKGROUND: Spring-Assisted Posterior Vault Expansion has been adopted at Great Ormond Street Hospital for Children, London, UK to treat raised intracranial pressure in patients affected by syndromic craniosynostosis, a congenital calvarial anomaly which causes premature fusion of skull sutures. This procedure aims at normalising head shape and augmenting intracranial volume by means of metallic springs which expand the back portion of the skull. The aim of this study is to create and validate a 3D numerical model able to predict the outcome of spring cranioplasty in patients affected by syndromic craniosynostosis, suitable for clinical adoption for preoperative surgical planning. METHODS: Retrospective spring expansion measurements retrieved from x-ray images of 50 patients were used to tune the skull viscoelastic properties for syndromic cases. Pre-operative computed tomography (CT) data relative to 14 patients were processed to extract patient-specific skull shape, replicate surgical cuts and simulate spring insertion. For each patient, the predicted finite element post-operative skull shape model was compared with the respective post-operative 3D CT data. FINDINGS: The comparison of the sagittal and transverse cross-sections of the simulated end-of-expansion calvaria and the post-operative skull shapes extracted from CT images showed a good shape matching for the whole population. The finite element model compared well in terms of post-operative intracranial volume prediction (R2 = 0.92, p < 0.0001). INTERPRETATION: These preliminary results show that Finite Element Modelling has great potential for outcome prediction of spring assisted posterior vault expansion. Further optimisation will make it suitable for clinical deployment.