Effect of implementing magnetic resonance imaging for patient-specific OpenSim models on lower-body kinematics and knee ligament lengths |
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| Institution: | 1. Faculty of Health Sciences, University of Ottawa, Canada;2. Faculty of Industrial Engineering, University of Bologna, Italy;3. Section for Sports Traumatology M51, Bispebjerg-Frederiksberg Hospital, Denmark;4. Department of Physical and Occupational Therapy, Copenhagen University Hospital, Denmark;5. Department of Biomedical Sciences, University of Copenhagen, Denmark;1. Menzies Health Institute Queensland, Griffith University, Australia;2. Department of Mechanical Engineering, University of Sheffield, UK;3. INSIGNEO institute for in silico medicine, University of Sheffield, UK;4. Department of Industrial Engineering, Health Sciences and Technologies ICIR, University of Bologna, Italy;1. Biedermann Lab for Orthopaedic Research, Department of Orthopaedic Surgery, The University of Pennsylvania, Philadelphia, PA 19104, USA;2. Human Motion Lab, Department of Orthopaedic Surgery, The University of Pennsylvania, Philadelphia, PA 19104, USA;1. Department of Mechanical Engineering, University of Ottawa, Ottawa, Ontario, Canada;2. School of Rehabilitation Sciences, University of Ottawa, Ottawa, Ontario, Canada;3. School of Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada;1. School of Engineering, Faculty of Science & Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK;2. School of Healthcare Science, Faculty of Science & Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK;3. School of Mechanical, Aerospace & Civil Engineering, University of Manchester, Manchester M13 9PL, UK;1. School of Human Kinetics, University of Ottawa, Canada;2. Department of Engineering, University of Ottawa, Canada;3. School of Rehabilitation Sciences, University of Ottawa, Canada;1. Gold Coast Centre for Orthopedic Research, Engineering and Education, Menzies Health Institute Queensland and School of Allied Health Sciences, Griffith University, Gold Coast, Australia;2. Department of Civil and Environmental Engineering, Imperial College London, United Kingdom;3. Queensland Children’s Motion Analysis Service, Queensland Pediatric Rehabilitation Service, Children’s Health Queensland Hospital and Health Service, Brisbane, Australia;4. Department of Orthopedics, Children’s Health Queensland Hospital and Health Service, Brisbane, Australia;5. Department of Medical Imaging and Nuclear Medicine, Children’s Health Queensland, Lady Cilento Children’s Hospital, Brisbane, Australia;6. Department of Industrial Engineering, Università degli Studi di Bologna, Italy;7. School of Information and Communication Technology, Griffith University, Nathan, Australia |
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| Abstract: | BackgroundOpenSim models are typically based on cadaver findings that are generalized to represent a wide range of populations, which curbs their validity. Patient-specific modelling through incorporating magnetic resonance imaging (MRI) improves the model’s biofidelity with respect to joint alignment and articulations, muscle wrapping, and ligament insertions. The purpose of this study was to determine if the inclusion of an MRI-based knee model would elicit differences in lower limb kinematics and resulting knee ligament lengths during a side cut task.MethodsEleven participants were analyzed with the popular Rajagopal OpenSim model, two variations of the same model to include three and six degrees of freedom knee (DOF), and a fourth version featuring a four DOF MRI-based knee model. These four models were used in an inverse kinematics analysis of a side cut task and the resulting lower limb kinematics and knee ligament lengths were analyzed.ResultsThe MRI-based model was more responsive to the movement task than the original Rajagopal model while less susceptible to soft tissue artifact than the unconstrained six DOF model. Ligament isometry was greatest in the original Rajagopal model and smallest in the six DOF model.ConclusionsWhen using musculoskeletal modelling software, one must acutely consider the model choice as the resulting kinematics and ligament lengths are dependent on this decision. The MRI-based knee model is responsive to the kinematics and ligament lengths of highly dynamic tasks and may prove to be the most valid option for continuing with late-stage modelling operations such as static optimization. |
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| Keywords: | Biomechanics Computer simulation Knee injuries Musculoskeletal model |
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