Techniques to Reduce The Computational Cost of Finite Element Models of the Lower Limb Muscles

Abstract

Modeling the lower limb muscles using the Finite Element (FE) method is required for various applications including injury mechanisms or when stress/strain distribution in the muscle is of interest. When the muscles are represented with 3D FE models, the interaction between the muscles can be taken into account that has an effect on their force production. However, the computational cost of such a method is considerably high. Besides, in many cases, a major part of this computational cost is committed to gain unnecessary information. For instance, when having two FE muscles in contact, both muscles need to get finely meshed to conserve their surface details even if having the stress/strain distribution inside one of the two muscles is not required. As a result, the current study aims to explore a model reduction technique based on mesh embedding to decrease the computational cost of such models. A combination of Computerized Tomography and Magnetic Resonance Imaging (MRI) data obtained from a volunteer subject was used to generate a musculoskeletal model of the quadriceps muscle group. The modeling process was performed in ArtiSynth which is an opensource 3D modeling platform supporting combined simulation of multibody and finite element models. This platform allows the attachment of a passive embedded mesh to a FE body so that it deforms in accordance with the motion of the FE body. Considering that the external forces applied to the passive mesh can be propagated back to the FE body attached to it, contact can be defined between the embedded mesh and any other structure. A full and a reduced model are generated and are used to simulate a passive deep knee flexion to test the reliability of the method. The kinematic outcomes are compared against data points obtained from MRI at different flexion angles. The results show that the proposed methodology can be considered as a substitute to fully FE models without a substantial sacrifice on the outcomes despite having lower computational cost.

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