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Geometrical accuracy evaluation of an affordable 3D printing technology for spine physical models

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OBJECTIVE The aim of the study is to develop a workflow to establish geometrical quality criteria for 3D printed anatomical models as a guidance for selecting the most suitable 3D… Click to show full abstract

OBJECTIVE The aim of the study is to develop a workflow to establish geometrical quality criteria for 3D printed anatomical models as a guidance for selecting the most suitable 3D printing technologies available in a clinical environment. METHODS We defined the 3D geometry of a 25-year-old male patient's L4 vertebra and the geometry was then printed using two technologies, which differ in printing resolution and affordability: Fused Deposition Modelling (FDM) and Digital Light Processing (DLP). In order to measure geometrical accuracy, the 3D scans of two physical models were compared to the virtual input model. To compare surface qualities of these printing technologies we determined surface roughness for two regions of interest. Finally, we present our experience in the clinical application of a physical model in a congenital deformity case. RESULTS The analysis of the distribution of the modified Hausdorff distance values along the vertebral surface meshes (99% of values <1 mm) of the 3D printed models provides evidence for high printing accuracy in both printing techniques. Our results demonstrate that the surface qualities, measured by roughness are adequate (~99% of values <0.1 mm) for both physical models. Finally, we implemented the FDM physical model for surgical planning. CONCLUSION We present a workflow capable of determining the quality of 3D printed models and the application of a high quality and affordable 3D printed spine physical model in the pre operative planning. As a result of the visual guidance provided by the physical model, we were able to define the optimal trajectory of the screw insertion during surgery.

Keywords: physical models; geometrical accuracy; printing; model; geometry

Journal Title: Journal of Clinical Neuroscience
Year Published: 2020

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