MRI-based parallel mechanisms to model subject-specific joint kinematics

Subject-specific musculoskeletal (MSK) computer models can estimate muscle and joint articular forces, enabling the identification of mechanical factors causing joint injury or disease. MSK models include models of skeletal anatomy and joint kinematics that can be created in OpenSim [1]. These models are generic and undergo simple linear scaling to a subject using markers from motion analysis. However, generic scaled MSK models produce less accurate estimates of measured knee articular forces compared to those that are subject-specific [2]. Thus, methods are needed to readily create subject-specific models. Passive tibiofemoral (TFJ), patellofemoral (PFJ) and talocrural (TAJ) kinematics measured in cadavers are well predicted using 3D parallel mechanisms [3,4]. These models integrate the cadaver’s measured bone, ligament and tendon geometries to constrain the joints’ degrees of freedom (DOFs). Using TFJ flexion angle as input, TFJ and PFJ models estimate the tibia’s and patella’s remaining Flex-Extension (FE), Abd-Adduction (AA), Int-External (IE) rotations and Ant-Posterior (AP), Prox-Distal (PD) and Med-Lateral (ML) translations. Similarly, TAJ models use talus flexion angle to estimate kinematics from the other TAJ DOFs. However, these models have only been used in cadavers where the kinematics were accurately measured and used to tune the models geometrical parameters. We aimed to use MRI images of in vivo lower limb bones, cartilages and ligaments to create models’ geometrical parameters. Using previously described mechanisms [3,4] we estimated subject-specific kinematics for use in OpenSim. However, without measured kinematics to tune the model, we created specialized algorithms to solve the mechanisms and then compared the results with those from cadaveric studies.