ENHANCED CRANIOPLASTY IMPLANT DESIGN VIA SURFACE REGISTRATION

Van-Giang Nguyen; Mau Uyen Nguyen; Duc Tang Tran

Van-Giang Nguyen Institute of Information and Communication Technology Le Quy Don Technical University
Mau Uyen Nguyen Institute of Information and Communication Technology Le Quy Don Technical University
Duc Tang Tran Vietnam-Japan International Cooperation Center for Science and Technology, Le Quy Don Technical University

Keywords: adaptive mirroring technique, cranial implant, cranioplasty, personalized implant design

Abstract

Cranioplasty requires precise, patient-specific implants to restore skull geometry while satisfying clinical and surgical constraints. Traditional implant design methods, particularly symmetry-based mirroring, leverage the skull’s inherent symmetry to create individualized implants but are limited by assumptions of perfect symmetry with midsagittal plane, restricting their applicability to non-symmetrical skulls. Meanwhile, data-driven deep learning approaches, despite their promise, often produce lowerresolution implants requiring extensive post-processing and may lack personalization for unique anatomical features. This study proposes an enhanced mirroring technique integrated with surface registration to relax the strict symmetry requirement, enabling high-resolution implant design for imperfectly symmetrical skulls. The proposed implant generation workflow involves several key steps: initial Computed Tomography (CT) image acquisition, skull image segmentation, and a preliminary symmetric mirroring of the skull along the x-axis. The core innovation lies in applying surface registration to accurately align the original and mirrored images, determining a precise transformation matrix. This transformation is then used to create the implant by subtracting the original image from the transformed one, followed by final adjustments based on specific medical requirements. Experimental results demonstrate that the generated implants are highly individualized, exhibit high accuracy, meet clinical demands, and offer improved resolution compared to those produced by artificial intelligence (AI)-based methods. By addressing the limitations of both classical and AI-driven approaches, this work expands the utility of mirroring techniques, offering a practical, high-fidelity solution for personalized cranioplasty implants.

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