The temporomandibular joint (TMJ) is one of the most important and complex joints of the body and its pathologies affect a great percentage of the human population. The simulation of the TMJ behavior during opening, closing and chewing movements can be very useful to the understanding of this articulation by physicians, helping them to prevent or fix problems due to accidents or diseases. This work proposes a model to simulate the human TMJ behavior based on the concept of two interdependent joints. The model was conceived using multimodal information acquired from CT and MRI images of a live person, as well as motion data acquired from this same person with a magnetic motion capture device. Simulation of movement of other TMJs, based on different morphology of bones and teeth, is obtained by adapting the regular captured motion data through collision detection and treatment methods. The proposed model was evaluated through image registration techniques by comparing our simulated results with real, captured motion data. We also validate the model showing how it can be used to predict TMJ behavior in the presence of different – normal or abnormal – bones and teeth morphologies.
