Jérôme Schmid, Anders Sandholm, François Chung, Daniel Thalmann, Hervé Delingette, Nadia Magnenat-Thalmann; Musculoskeletal simulation model generation from MRI datasets and motion capture data; In: Nadia Magnenat-Thalmann, Jian Zhang, David Feng (Ed.): Recent Advances in the 3D Physiological Human (3DPH), pp. 3-19, Springer-Verlag, London, 2009.
Today, computer models and computer simulations of the musculoskeletal system are widely used to study the mechanisms behind human gait and its disorders. The common way of creating musculoskeletal models is to use a generic musculoskeletal model based on data derived from anatomical and biomechanical studies of cadaverous specimens. To adapt this generic model to a specific subject, the usual approach is to scale it. This scaling has been reported to introduce several errors because it does not always account for subject-specific anatomical differences. As a result, a novel semi-automatic workflow is proposed that creates subject-specific musculoskeletal models from Magnetic Resonance Imaging (MRI) datasets and motion capture data. Based on subject-specific medical data and a model-based automatic segmentation approach, an accurate modeling of the anatomy can be produced while avoiding the scaling operation. This anatomical model coupled with motion capture data, joint kinematics information and muscle-tendons actuators is finally used to create a subject-specific musculoskeletal model.