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Mandible Morphing Through Principal Components Analysis

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Design Tools and Methods in Industrial Engineering (ADM 2019)

Abstract

The aim of this research is to develop patient-specific 3D mandible models, based on a limited number of measurements taken on the patient. Twenty Computed Tomography scans were used to build the respective 3D cad models of the mandible. Fifteen of these models were given as an input to a Principal Component Analysis software, and eight ‘principal’ mandible morphologies were produced. The following step was to identify the most efficient landmarks to ‘weight’ these morphologies when building a patient-specific model. Two further mandible computed tomography scans (a ‘normal’ mandible and a ‘severely resorbed’ one) were used to test the full procedure and to assess its accuracy.

The accuracy of the 3D morphed surface resulted to range between 0.025 and 3.235 mm for the ‘normal’ mandible and between 0.012 and 1.149 mm for the ‘severely resorbed’ one having used eight landmarks to morph a ‘standard’ mandible.

This work demonstrates how patient-specific models can be obtained registering the position of a limited number of points (on panoramic x-ray or on the physical model), reaching a good accuracy. This allows performing patient-specific planning and numerical simulations even for those cases where a computed tomography scan would not be available. In fact, this procedure can be interfaced with mesh morphing algorithms to automatically build finite element models. The accuracy of the procedure can be further improved, widening the mandibles computed tomography scans database and optimizing landmarks position.

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Correspondence to Michele Calì .

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Pascoletti, G., Calì, M., Bignardi, C., Conti, P., Zanetti, E.M. (2020). Mandible Morphing Through Principal Components Analysis. In: Rizzi, C., Andrisano, A.O., Leali, F., Gherardini, F., Pini, F., Vergnano, A. (eds) Design Tools and Methods in Industrial Engineering. ADM 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-31154-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-31154-4_2

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