Bone remodelling-based numerical evaluation of personalized masticatory forces from CT-scans

[EN] In recent years, the patient specific medical scenario has gained increasing popularity in health and engineering research. Unlike the standard medical paradigm (generalized treatment), personalized health techniques are oriented towards suiting the specific demands of a given patient, resulting in solutions that increase the success of the medical procedure. The idea of computer oriented, or in silico, medical experimentation has proven to be an effective tool to permit the implementation of patient specific medicine. Bone remodelling (BR) simulation intends to predict bone distribution changes due to the specific living conditions of a certain bone under the patient¿s loading conditions. Unfortunately, it is usually difficult to obtain the simulation conditions affecting this process which are particular in each case. In this sense, the objective of this work is to present a procedure to adjust the patient-dependent biomechanical factors affecting bone growth and ingrowth, in order to reduce the computational uncertainty of the BR simulation. Assuming that the medical image represents a bone distribution in remodelling homeostasis, the optimization of parameters is driven by the search of a stationary remodelling state of the bone. The methodology is tested in a synthetic problem and also applied in the context of a human mandible in which mastication forces are obtained. The authors gratefully acknowledge the financial support of Generalitat Valenciana (FEDEGENT/2018/025 and Prometeo/2021/046), Ministerio de Economia, Industria y Competitividad (DPI2017-89816-R) and FEDER. Funding for open access charge: CRUE-Universitat Politecnica de Valencia. Gutiérrez-Gil, J.; Nadal, E.; Bouza, K.; Tur Valiente, M.; Atienza Vicente, CM.; Allix, O.; Ródenas, JJ. (2022). Bone remodelling-based numerical evaluation of personalized masticatory forces from CT-scans. Computer Methods in Applied Mechanics and Engineering. 401:1-20. https://doi.org/10.1016/j.cma.2022.115635