Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal...

Area of Science:

• Biomechanical Engineering
• Craniofacial Surgery
• Computational Modeling

Background:

• Craniosynostosis, premature suture fusion, causes skull deformities and health issues requiring surgery.
• Finite element (FE) method is a valuable tool for predicting surgical outcomes in craniosynostosis.
• Input parameters in FE models can significantly influence predictions of skull growth and intracranial pressure.

Purpose of the Study:

• To conduct sensitivity studies on FE models for craniosynostosis.
• To assess the impact of various input parameters on predicting post-operative skull morphology.
• To understand the influence of material properties, bone formation simulation, and bone formation rate on FE predictions.

Main Methods:

• A 3D FE model of a sagittal synostosis patient's skull was created from CT images.
• Virtual reconstructive surgery was performed with two intracranial content scenarios (CSF present/absent).
• Sensitivity analyses were conducted on material properties, bone formation methods, and bone formation rates, comparing results to in vivo data.

Main Results:

• The elastic modulus of craniotomies most significantly influenced overall predicted skull morphology.
• Bone formation modeling approaches impacted brain contact pressure but had minimal effect on skull morphology.
• Including cerebrospinal fluid (CSF) slightly reduced predicted brain pressure.

Conclusions:

• FE model sensitivity to material properties is crucial for accurate craniosynostosis outcome prediction.
• Bone formation simulation methods are key for assessing brain pressure, while material properties are vital for morphology.
• These findings establish a basis for future FE-based comparative studies on craniosynostosis reconstruction techniques.