Automatic hole repairing for cranioplasty using Bézier surface approximation

Area of Science:

• Biomedical Engineering
• Computer-Aided Surgery
• Medical Imaging

Background:

• Cranioplasty is essential for repairing skull defects caused by trauma, infection, or surgery.
• Current methods for creating cranial implants can be time-consuming and may lack precision.
• Accurate reconstruction of the skull defect is crucial for protecting the brain and achieving optimal aesthetic and functional results.

Purpose of the Study:

• To develop and present a novel hole-repairing algorithm for generating artificial cranial implants.
• To automate the process of filling irregular defects in biomodels for cranioplasty.
• To ensure the generated implant meshes accurately interpolate the shape and density of the surrounding native bone.

Main Methods:

• A hole-repairing algorithm was developed to fill defects in biomodels represented by unstructured triangular surface meshes or in stereo-lithography (STL) format.
• The algorithm incorporates hole identification, triangulation using Genetic Algorithm optimization, and an advancing-front meshing technique.
• Surface approximations based on Quartic Bézier patches/surfaces were utilized for precise mesh generation.

Main Results:

• The developed algorithm successfully fills cranial defects by generating accurate patching meshes.
• The resulting meshes interpolate the shape and density of the surrounding cranial bone, ensuring a good fit for the implant.
• The method provides a robust approach for creating patient-specific cranial implants.

Conclusions:

• The presented hole-repairing algorithm offers an effective solution for automating the creation of cranial implants in cranioplasty.
• This computational approach can enhance the precision and efficiency of surgical planning and implant design.
• The technique holds potential for improving patient outcomes in reconstructive neurosurgery.