Automated creation and tuning of personalised muscle paths for OpenSim musculoskeletal models of the knee joint

Computational modelling is an invaluable tool for investigating features of human locomotion and motor control which cannot be measured except through invasive techniques. Recent research has focussed on creating personalised musculoskeletal models using population-based morphing or directly from medical imaging. Although progress has been made, robust definition of two critical model parameters remains challenging: (1) complete tibiofemoral (TF) and patellofemoral (PF) joint motions, and (2) muscle tendon unit (MTU) pathways and kinematics (i.e. lengths and moment arms). The aim of this study was to develop an automated framework, using population-based morphing approaches to create personalised musculoskeletal models, consisting of personalised bone geometries, TF and PF joint mechanisms, and MTU pathways and kinematics. Informed from medical imaging, personalised rigid body TF and PF joint mechanisms were created. Using atlas- and optimisation-based methods, personalised MTU pathways and kinematics were created with the aim of preventing MTU penetration into bones and achieving smooth MTU kinematics that follow patterns from existing literature. This framework was integrated into the Musculoskeletal Atlas Project Client software package to create and optimise models for 6 participants with incrementally increasing levels of personalisation with the aim of improving MTU kinematics and pathways. Three comparisons were made: (1) non-optimised (Model 1) and optimised models (Model 3) with generic joint mechanisms; (2) non-optimised (Model 2) and optimised models (Model 4) with personalised joint mechanisms; and (3) both optimised models (Model 3 and 4). Following optimisation, improvements were consistently shown in pattern similarity to cadaveric data in comparison (1) and (2). For comparison (3), a number of comparisons showed no significant difference between the two compared models. Importantly, optimisation did not produce statistically significantly worse results in any case.

[1]  M. Andersen.  How sensitive are predicted muscle and knee contact forces to normalization factors and polynomial order in the muscle recruitment criterion formulation? , 2018, International Biomechanics.

[2]  D G Lloyd,et al.  Machine learning methods to support personalized neuromusculoskeletal modelling , 2020, Biomechanics and Modeling in Mechanobiology.

[3]  Jeffrey A. Reinbolt,et al.  Are Patient-Specific Joint and Inertial Parameters Necessary for Accurate Inverse Dynamics Analyses of Gait? , 2007, IEEE Transactions on Biomedical Engineering.

[4]  Luca Modenese,et al.  Automated Generation of Three-Dimensional Complex Muscle Geometries for Use in Personalised Musculoskeletal Models , 2020, Annals of Biomedical Engineering.

[5]  Marcus G Pandy,et al.  Muscle and joint‐contact loading at the glenohumeral joint after reverse total shoulder arthroplasty , 2011, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[6]  J. Langenderfer,et al.  Probabilistic Modeling of Knee Muscle Moment Arms: Effects of Methods, Origin–Insertion, and Kinematic Variability , 2007, Annals of Biomedical Engineering.

[7]  Daniel Nolte,et al.  Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling , 2020, Gait & posture.

[8]  Nicola Sancisi,et al.  Feasibility of using MRIs to create subject-specific parallel-mechanism joint models. , 2017, Journal of biomechanics.

[9]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[10]  M. Bobbert,et al.  Length and moment arm of human leg muscles as a function of knee and hip-joint angles , 2004, European Journal of Applied Physiology and Occupational Physiology.

[11]  Ayman Habib,et al.  OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement , 2018, PLoS Comput. Biol..

[12]  M. Pandy,et al.  The Obstacle-Set Method for Representing Muscle Paths in Musculoskeletal Models , 2000, Computer methods in biomechanics and biomedical engineering.

[13]  Matthew S. DeMers,et al.  Changes in tibiofemoral forces due to variations in muscle activity during walking , 2014, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[14]  David G Lloyd,et al.  Minimal medical imaging can accurately reconstruct geometric bone models for musculoskeletal models , 2018, bioRxiv.

[15]  Fulvia Taddei,et al.  nmsBuilder: Freeware to create subject-specific musculoskeletal models for OpenSim , 2017, Comput. Methods Programs Biomed..

[16]  Ziyun Ding,et al.  Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models , 2016, Journal of biomechanics.

[17]  Paul Suetens,et al.  Atlas-based non-rigid image registration to automatically define line-of-action muscle models: a validation study. , 2009, Journal of biomechanics.

[18]  Anthony M. J. Bull,et al.  An Optimization-Based Simultaneous Approach to the Determination of Muscular, Ligamentous, and Joint Contact Forces Provides Insight into Musculoligamentous Interaction , 2011, Annals of Biomedical Engineering.

[19]  Novacheck,et al.  The biomechanics of running. , 1998, Gait & posture.

[20]  Christopher P Carty,et al.  Increasing level of neuromusculoskeletal model personalisation to investigate joint contact forces in cerebral palsy: A twin case study. , 2019, Clinical biomechanics.

[21]  Massimo Sartori,et al.  Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces. , 2013, Journal of biomechanics.

[22]  James M. Wakeling,et al.  Why are Antagonist Muscles Co-activated in My Simulation? A Musculoskeletal Model for Analysing Human Locomotor Tasks , 2017, Annals of Biomedical Engineering.

[23]  Luca Modenese,et al.  Tibiofemoral contact forces during walking, running and sidestepping. , 2016, Gait & posture.

[24]  Hans Kainz,et al.  Effects of hip joint centre mislocation on gait kinematics of children with cerebral palsy calculated using patient-specific direct and inverse kinematic models. , 2017, Gait & posture.

[25]  Ilse Jonkers,et al.  A musculoskeletal model customized for squatting task , 2018, Computer methods in biomechanics and biomedical engineering.

[26]  Nicola Sancisi,et al.  A novel 3D parallel mechanism for the passive motion simulation of the patella-femur-tibia complex , 2011 .

[27]  Thor F Besier,et al.  Lower limb estimation from sparse landmarks using an articulated shape model. , 2016, Journal of biomechanics.

[28]  C. Spoor,et al.  Knee muscle moment arms from MRI and from tendon travel. , 1992, Journal of biomechanics.

[29]  Duncan Bakke,et al.  Shape model constrained scaling improves repeatability of gait data. , 2020, Journal of biomechanics.

[30]  W. Buford,et al.  Muscle balance at the knee--moment arms for the normal knee and the ACL-minus knee. , 1997, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[31]  T P Andriacchi,et al.  Interaction between intrinsic knee mechanics and the knee extensor mechanism , 1987, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[32]  T. B. Kirk,et al.  Muscle and external load contribution to knee joint contact loads during normal gait. , 2009, Journal of biomechanics.

[33]  Allison L. Kinney,et al.  Influence of musculoskeletal model parameter values on prediction of accurate knee contact forces during walking. , 2020, Medical engineering & physics.

[34]  Luca Modenese,et al.  Automatic Generation of Personalised Skeletal Models of the Lower Limb from Three-Dimensional Bone Geometries , 2020, bioRxiv.

[35]  Mohammad Kia,et al.  Concurrent prediction of muscle and tibiofemoral contact forces during treadmill gait. , 2014, Journal of Biomechanical Engineering.

[36]  Frances T Sheehan,et al.  Dynamic in vivo 3-dimensional moment arms of the individual quadriceps components. , 2009, Journal of biomechanics.

[37]  M Günther,et al.  Tailoring anatomical muscle paths: a sheath-like solution for muscle routing in musculoskeletal computer models. , 2019, Mathematical biosciences.

[38]  Christopher P Carty,et al.  Best methods and data to reconstruct paediatric lower limb bones for musculoskeletal modelling , 2019, Biomechanics and Modeling in Mechanobiology.

[39]  Mark Taylor,et al.  The MAP Client: User-Friendly Musculoskeletal Modelling Workflows , 2014, ISBMS.

[40]  Scott L. Delp,et al.  Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait , 2016, IEEE Transactions on Biomedical Engineering.

[41]  Nicola Sancisi,et al.  An Anatomical-Based Subject-Specific Model of In-Vivo Knee Joint 3D Kinematics From Medical Imaging , 2020, Applied Sciences.

[42]  Scott L. Delp,et al.  A Model of the Lower Limb for Analysis of Human Movement , 2010, Annals of Biomedical Engineering.

[43]  Kevin B. Shelburne,et al.  Dependence of Muscle Moment Arms on In Vivo Three-Dimensional Kinematics of the Knee , 2017, Annals of Biomedical Engineering.

[44]  Jason M. Konrath,et al.  Muscle contributions to medial tibiofemoral compartment contact loading following ACL reconstruction using semitendinosus and gracilis tendon grafts , 2017, PloS one.

[45]  Nicola Sancisi,et al.  Effect of implementing magnetic resonance imaging for patient-specific OpenSim models on lower-body kinematics and knee ligament lengths. , 2019, Journal of biomechanics.

[46]  Nicola Sancisi,et al.  A New Kinematic Model of the Passive Motion of the Knee Inclusive of the Patella , 2011 .

[47]  Mark Taylor,et al.  Statistical shape modelling versus linear scaling: Effects on predictions of hip joint centre location and muscle moment arms in people with hip osteoarthritis. , 2019, Journal of biomechanics.

[48]  Nicola Sancisi,et al.  Development and validation of subject-specific pediatric multibody knee kinematic models with ligamentous constraints. , 2019, Journal of biomechanics.

[49]  Ilse Jonkers,et al.  Subject-specific musculoskeletal modelling in patients before and after total hip arthroplasty* , 2016, Computer methods in biomechanics and biomedical engineering.

[50]  M S Andersen,et al.  Development and validation of a subject-specific moving-axis tibiofemoral joint model using MRI and EOS imaging during a quasi-static lunge. , 2018, Journal of biomechanics.

[51]  Stefan Wesarg,et al.  Investigation of the dependence of joint contact forces on musculotendon parameters using a codified workflow for image-based modelling. , 2018, Journal of biomechanics.

[52]  Matthew S. DeMers,et al.  How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces. , 2015, Journal of biomechanics.