PSI - Issue 43

Apolena Šustková et al. / Procedia Structural Integrity 43 (2023) 276–281 Author name / Structural Integrity Procedia 00 (2022) 000 – 000

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1. Introduction Computational modeling using the finite element method (FEM) is increasingly widespread in addressing the issue of mechanical interaction of dental implants with bone tissue. In this case, the computational model of the bone tissue of the mandible or maxilla must include the geometry of the trabecular micro architecture, which consists of rod and plate structures. The study and mechanical analysis of these bone structures is made possible by micro FEM models. For this purpose, it is necessary to use data obtained on a micro- computer tomography (μ -CT) device. The output of this device is high-resolution image data, which must be transformed into a computational model using image processing. Image segmentation is used for this purpose. Specifically, for micro CT data, the most often method used is thresholding (van Eijnatten et al. 2017). This process is crucial and will most affect the subsequent 3D reconstruction of the model of geometry of the computational model and, as a result, also the related deformation and stress states. Threshold determines the bone and soft tissue interface – pixels with values greater than the threshold value are included in the computational model and pixels with values under the threshold are considered unimportant (soft tissue, fat, etc.). Moreover, the threshold value for automatic and manual segmentation may differ and the variant used depends, among other things, on the software. All of this can have an impact on the final geometry model of bone tissues and thus on the computational model (which subsequently also affects stress-strain stages). The aim of this study is to determine and analyze apparent mechanical behavior of mandibular trabecular bone structure based on image processing using different threshold values and assessment of its influence on local strain intensity in chosen rod. 2. Materials and methods The mandibular bone segment (20 x 15 x 15 mm) was acquired from the Department of Anatomy, Faculty of Medicine, Masaryk University Brno, Czech Republic. This sample was scanned using a micro-computed tomography scanner (GE phoenix v|tome|x L240, GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany) with a voxel size of 15 μm. The bone density calibration was performed using a hydroxyapatite phantom (MicroCT-HA D20, © QRM GmbH, Moehrendorf, Germany) included during scanning. This phantom caliber consists of 5 cylinders with different densities – 0 g/cm 3 , 0.05 g/cm 3 , 0.20 g/cm 3 , 0.80 g/cm 3 , 1.20 g/cm 3 (see Fig. 1a)). Micro CT images of phantom were then used to determine bone density according to a standard procedure based on a linear relationship between hydroxyapatite density and the corresponding image – pixel gray values (see Fig. 1b)).

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Figure 1: Phantom caliber: a) Transversal view of the hydroxyapatite phantom block (g/cm 3 ); b) Linear relationship between bone density of hydroxyapatite samples and pixel gray value.

2.1. Image processing and models of geometry

For subsequent analysis, a region of interest (ROI) in the area of the alveolar bone in the size of 4 x 4 x 4 mm was selected (see Fig. 2a)). This ROI was chosen to represent a typical location for dental implant insertion. Automatic segmentation was performed in the ImageJ software (Schneider et al. 2012) for different threshold values (pixel gray values) corresponding to bone density from 0.25 to 0.65 g/cm 3 (see Fig. 2b)) with the increment of 0.05 g/cm 3 (nine