Method for determining musculotendon parameters in subject-specific musculoskeletal models of children developed from MRI data

Abstract

Accurate knowledge of muscle-tendon parameters in biomechanical models is critical for accurate simulation and analyses of human movement. An excellent example of this is the creation of subject-specific models from magnetic resonance imaging (MRI). When Hill-type muscle models are used to calculate muscle forces, the determination of muscle attachment points, optimal fiber length, tendon slack length and maximum isometric force all have a significant influence on the joint moment-angle behavior of the model. In the present study a method was developed for customizing the values of muscle-tendon parameters in a generic model to create subject-specific biomechanical models from MRI. The method was applied by generating musculoskeletal models for the biomechanical simulation platform OpenSim, but the workflow is equally well applicable to other simulation platforms. New computational algorithms are described for identifying joint centers and for reconstructing the centroids of the muscle bellies from MRI. A process is also described for the extraction of the muscle paths and for identifying the positions of 'via-points' used to model muscles wrapping over bones. Finally, a new algorithm is described for adjusting the values of optimal fiber length, tendon slack length and maximum isometric force based on a comparison of the model results with experiment. We tested our computational algorithms by developing subject-specific biomechanical models of five typically developed children (age 9.5±1.7 years) from MRI. The joint moment-angle relationships calculated for the subject-specific models were similar to those determined for corresponding scaled generic models. The results indicate that the proposed methodology is suitable for developing subject-specific models of healthy children. Future studies should investigate how abnormalities of the musculoskeletal system, such as tibial torsion and muscle spasticity, can be integrated into the modeling process. © 2011 The Author(s).