E J Bos1, T Scholten2, Y Song2, J C Verlinden2, J Wolff3, T Forouzanfar3, M N Helder4, P van Zuijlen5. 1. Dept. of Plastic & Reconstructive Surgery, VUMC, The Netherlands; Burn Centre Beverwijk, The Netherlands. Electronic address: ej.bos@vumc.nl. 2. Faculty of Industrial Design Engineering, TU Delft, The Netherlands. 3. Dept. of Oral & Maxillofacial Surgery, VUMC, The Netherlands. 4. Dept. of Orthopedics, VUMC, The Netherlands. 5. Dept. of Plastic & Reconstructive Surgery, VUMC, The Netherlands; Burn Centre Beverwijk, The Netherlands.
Abstract
INTRODUCTION: Ear reconstruction is a tedious and demanding surgical procedure and the implant framework used is essential for the esthetic result. The outcome of a reconstructed ear, however, is not necessarily limited to the implant shape but rather to the available options of transplantable tissue for coverage. Apart from the visual aesthetics, ear reconstruction subsequently also requires implant dimensions to be adapted to the surgical possibilities. In this article, we have brought different disciplines together to develop a customizable ear model for 3D printing of ear implants. MATERIAL AND METHODS: Computed tomography (CT) scans were made of 4 human cadaver ears before and after soft tissue dissection using a Discovery 750 High Definition Freedom Edition scanner (GE, Milwaukee, WI, USA) and subsequently converted into an STL data set using Mimics Software (Materialise, Leuven, Belgium). These scans were then used to develop a fully adjustable parametric model based on the essential ear anatomy using Rhinoceros and Grasshopper software. RESULTS: To determine the quality of the developed models, directed Hausdorff distance (DHD) was applied as the basis for measuring the similarity between the parametric model and the ear cartilage scanning data. Two methods were used. The mean directed Haussdorff distance (MDHD) was calculated based on the distribution of point sets showing an average similarity of 0.8 mm (±0.05 mm). The mean similarity coefficient (SC) of the model and scan surfaces was 94% with a 2-mm threshold. CONCLUSION: This study shows that a parametric standard model could be used as a feasible method to generate custom implants based on existing ear images.
INTRODUCTION: Ear reconstruction is a tedious and demanding surgical procedure and the implant framework used is essential for the esthetic result. The outcome of a reconstructed ear, however, is not necessarily limited to the implant shape but rather to the available options of transplantable tissue for coverage. Apart from the visual aesthetics, ear reconstruction subsequently also requires implant dimensions to be adapted to the surgical possibilities. In this article, we have brought different disciplines together to develop a customizable ear model for 3D printing of ear implants. MATERIAL AND METHODS: Computed tomography (CT) scans were made of 4 human cadaver ears before and after soft tissue dissection using a Discovery 750 High Definition Freedom Edition scanner (GE, Milwaukee, WI, USA) and subsequently converted into an STL data set using Mimics Software (Materialise, Leuven, Belgium). These scans were then used to develop a fully adjustable parametric model based on the essential ear anatomy using Rhinoceros and Grasshopper software. RESULTS: To determine the quality of the developed models, directed Hausdorff distance (DHD) was applied as the basis for measuring the similarity between the parametric model and the ear cartilage scanning data. Two methods were used. The mean directed Haussdorff distance (MDHD) was calculated based on the distribution of point sets showing an average similarity of 0.8 mm (±0.05 mm). The mean similarity coefficient (SC) of the model and scan surfaces was 94% with a 2-mm threshold. CONCLUSION: This study shows that a parametric standard model could be used as a feasible method to generate custom implants based on existing ear images.
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Authors: D O Visscher; A Gleadall; J K Buskermolen; F Burla; J Segal; G H Koenderink; M N Helder; P P M van Zuijlen Journal: J Biomed Mater Res B Appl Biomater Date: 2018-11-01 Impact factor: 3.368