title: Muscular stabilisation of the knee and development of automated and tuned subject-specific musculoskeletal models for gait simulations
Creators: name: Killen, Bryce A.
Publisher: name: Griffith University
Year: 2019
Subjects: subject: Muscular stabilisation
subject: Knee
subject: Human biomechanics
subject: Musculoskeletal system
subject: Gait simulations

description: Computational models of the human musculoskeletal system allow researchers to investigate human biomechanics without the need for invasive methods or expensive experiments. These models can be combined with standard motion capture technology to simulate individual’s movement patterns. With relatively little data processing, the model’s joint kinematics can be calculated along with joint kinetics, thus characterising an individual’s generalised joint coordinates (i.e., joint motion) and external joint loads. Without and with the incorporation of electromyograms (EMG) acquired during these tasks, further methods can be employed to estimate muscle forces and subsequently joint contact loading (Lloyd and Besier, 2003; Pandy and Andriacchi, 2010; Pizzolato et al., 2015; Sartori et al., 2012a; Sasaki and Neptune, 2010; Saxby et al., 2016b; Sritharan et al., 2012; Winby et al., 2009). Indeed, substantial research has focused on developing methods for estimating the magnitude of the joint contact loading within the tibiofemoral joint (TFJ) during a range of locomotion tasks (Fregly et al., 2012; Gerus et al., 2013; Kim et al., 2009). Understanding typical TFJ contact loading is crucial, as the magnitude of joint contact loading has been associated with the development and progression of TFJ osteoarthritis (Andriacchi and Mundermann, 2006). Tibiofemoral joint contact loading is primarily caused by muscles, which act to compress the joint (Sasaki, 2010). Although net and grouped muscle contributions to TFJ contact loading has previously been investigated during walking gait (Pandy and Andriacchi, 2010; Sasaki and Neptune, 2010; Saxby et al., 2016b; Sritharan et al., 2012; Winby et al., 2009), other locomotion tasks such as running and sidestep cutting, herein referred to as sidestepping remain largely unexplored. Along with the loading magnitude, other loading parameters may play a vital role influencing joint health, such as the region of loading in combination with the distribution of loading (Chaudhari et al., 2008) Models previously used to estimate the magnitude of joint contact loading have typically been linearly scaled versions of a generic musculoskeletal model, e.g., “gait2392” (Delp et al., 2007) or TLEM 2.0 (Carbone et al., 2015). These models use generic bone geometries which may not reflect each individual’s anatomy, even after linear scaling (Kainz et al., 2017a). As such, these models may be inappropriate tools to accurately estimate TFJ contact loading magnitudes, as bone geometry influence muscle tendon unit (MTU) force estimates and contact mechanics (Demers et al., 2014; Gerus et al., 2013; Lerner et al., 2015). Furthermore, these models may be inappropriate for the estimation of the region of loading within the TFJ, as this feature is highly dependent on joint anatomy (Lerner et al., 2015). Additionally, limitations within these linear scaled generic models, particularly the TFJ kinematic models, further hamper their utility for investigating regional loading within the TFJ (Demers et al., 2014). If regional loading is to be investigated, computational models that accurately represent subject-specific three-dimensional (3D) bone and joint geometry, 6 degree of freedom (DOF) joint kinematics, and feasible MTU pathways, lengths, and moment arms are required. The overarching aim of this thesis was to investigate features related to TFJ contact loading. Specifically, estimate individual muscle contributions to medial and lateral TFJ contact loading during walking, running, and sidestepping. Second, develop a framework that automatically creates and tunes highly detailed subject-specific computational musculoskeletal models that can be used to investigate various feature of TFJ loading. To investigate individual muscle contributions to TFJ contact loading during various dynamic locomotion tasks, 54 healthy individuals were recruited as part of an ongoing project. Each participant underwent a standard motion capture gait analysis session, wherein whole body and segment motions were captured using 3D motion capture. Ground reaction forces were acquired via in-ground force plates and muscle activation patterns acquired via surface EMG. Motion capture data were used within the free and open-source musculoskeletal modelling platform OpenSim (Delp et al., 2007) to estimate model joint kinematics and kinetics. Using established calibrated EMG-informed neuromusculoskeletal modelling methods (Hoang et al., 2018; Pizzolato et al., 2015; Saxby et al., 2016b), muscle forces and subsequently muscle contributions to TFJ contact loading (Winby et al., 2009) were estimated. Results for walking, running, and sidestepping showed during weight acceptance, the vastus medialis and vastus lateralis muscles dominated contribution to medial and lateral TFJ contact loading respectively. During mid-stance and push-off, the contribution to medial and lateral TFJ contact loading was dominated by the medial and lateral gastrocnemii muscles respectively for all three tasks. Although there were similarities in which muscles dominated medial and lateral TFJ contact loading, differences were shown in the magnitude of relative muscle contributions between locomotion tasks. These differences were driven by different kinematic (Novacheck, 1995), kinetic (Novacheck, 1995), muscle activations (Besier et al., 2003a), and stabilisation requirements present in each tasks. Specific differences were, quadriceps contribution to medial and lateral TFJ contact loading were higher during running compared to walking, while gastrocnemii contribution to medial and lateral TFJ contact loading were higher during walking compared to running. Comparing running and sidestepping, contribution of selected muscles to medial TFJ contact loading were higher during sidestepping, while selected muscle contributions to lateral TFJ contact loading were higher in running. Muscles which dominate the contribution to TFJ contact loading, also provide a majority a TFJ stabilisation during these tasks. Results may provide valuable information for rehabilitation following orthopaedic surgeries to restore TFJ stability and prevent future injuries. To further address the overarching aims of this thesis, highly detailed subject-specific musculoskeletal models were required and thus developed. Subject-specific musculoskeletal models may contain joints and MTU pathways that are both physically and physiologically infeasible. First, the articulating bones of joints may interpenetrate, and similarly MTUs can penetrate bone surfaces. Second, joint kinematics can have discontinuities and may not follow the patterns of previously reported cadaveric studies. Likewise, MTU pathways, if inappropriately defined, can create MTU lengths and moment arms (MTU kinematics) that exhibit discontinuities and do not follow patterns of previously published cadaveric data. This thesis created subject-specific musculoskeletal models that addressed these shortcomings along with shortcomings of previous modelling methods. To evaluate the framework developed to create detailed subject-specific musculoskeletal models, a set of 6 individuals from an on-going study were used. These subjects spanned age (21 – 32 years), height (160.5 – 185 cm), and mass (45 – 89 kg) ranges, and were composed of three females and three males. Each subject underwent a standard gait analysis session as well as a comprehensive magnetic resonance imaging (MRI) protocol enabling detailed visualisation of their bones, muscles, cartilages, and other articular structures (i.e., ligaments). The framework developed within this thesis was built atop a pre-existing open-source framework, the Musculoskeletal Atlas Project (MAP) Client (Zhang et al., 2014), written in Python (Python Software Foundation. Python Language Reference, version 2.7. Available at http://www.python.org). The MAP Client was used in combination with manually segmented MRIs to well reconstruct subject-specific bone geometry using direct image segmentation and pre-developed MAP Client statistical shape models (SSMs). Bone geometries were then used to customise a generic OpenSim model (Delp et al., 2007) with subject-specific bone geometries and other personalised features (i.e., joint positions , MTU origin and insertions, and MTU via points). This generated model represents the standard model produced via the MAP Client pathway; however a number of further developments were required. Required improvements ranged from simple inclusions, such as adding customised marker sets, patellae, and patellofemoral joints (PFJs). More complex additions involved the definition of 6 DOF TFJ and PFJ kinematics, MTU origins, insertions and pathways. Six DOF (1 independent and 5 coupled) subject-specific TFJ and PFJ mechanisms were built from segmented MRIs. These mechanisms were then automatically tuned to be physically and physiologically feasible using previously published methods (Brito da Luz et al., 2017), which were incorporated into the MAP Client framework. The MTU pathways, i.e., origins, insertions and wrapping surfaces, were defined using the MAP Client mean SSMs and subject-specific MAP Client-generated bone reconstructions, which were automatically tuned to be physically and physiologically feasible (discussed in detail later). In the present study, MTU origins and insertions were defined using an atlas-based method (Zhang et al., 2015). The MTU origins and insertions were positioned on the MAP Client mean SSMs in the same anatomical regions (i.e., node point) as the atlas and could be queried using node indices. Node indices were used to define subject-specific MTU origin and insertions on the subject-specific MAP Client-generated bone models. The MTU wrapping surfaces were defined in a two-step process: (i) placement and fitting of wrapping surfaces, and (ii) optimisation of the wrapping surfaces’ geometrical dimensions and positions. Initial selection and placement of wrapping surfaces was done manually based on anatomical regions and landmarks defined as bone mesh elements and nodes indices on the MAP Client mean SSM bones. Following this manual identification, using individual’s subject-specific bones, wrapping surfaces were automatically positioned using bone elements and nodes identified through the MAP Client using custom written Python (Python Software Foundation. Python Language Reference, version 2.7. Available at http://www.python.org) software. Wrapping surface dimensions were based on analytical shapes automatically fit to anatomical regions of subject-specific MAP Client-generated bones using custom written Python software. Once wrapping surfaces were placed, their positions, orientations, and dimensions were automatically optimised with the aim of producing MTU pathways that were physically and physiologically feasible. “Physically feasible” MTU pathways (i) did not penetrate bones, and (ii) did not produce non-sensible wrapping scenarios, such as completing a circumferential loop of a wrapping cylinder. “Physiological feasible” MTU lengths and moment arms (i) closely follow the pattern of measurements taken from cadavers, available in literature, and (ii) are free of discontinuities. The developed framework created and tuned subject-specific rigid body musculoskeletal models that largely produced the desired outcomes while overcoming limitations with previous modelling methods. With respect to the definition of physically feasible MTU pathways, the inclusion of MTU wrapping surfaces fit to each subject’s anatomy largely reduced the number of bone MTU penetrations. However, prior to tuning, including MTU wrapping surfaces fit to each subject’s anatomy were detrimental to many MTU kinematic metrics (i.e., MTU kinematic smoothness, and pattern similarity to literature data). The designed optimisation routine, to tune MTU wrapping surfaces provided further improvements to MTU pathways, and more importantly improved MTU kinematic smoothness and pattern similarity with literature data. Improvements to both MTU pathways and kinematics was present in models which contained simplified (MAP Client standard) as well as subject-specific 6 DOF TFJ and PFJ kinematic mechanisms. The fact that improvements were shown in both models, regardless of the joint model, provided further confidence in the MTU wrapping surface optimisation process that was developed. Additionally, the fact that improvements were only shown once tuned, provided further evidence that the tuning of subject-specific musculoskeletal models is a necessary step in the developed framework. The optimised subject-specific musculoskeletal models containing simplified joint models, compared to subject-specific joint models, performed more consistently and often produced favourable MTU kinematics and pathways. Models with simplified joint models often exhibited fewer bone MTU penetrations, smoother MTU kinematics, and kinematics which more closely matched the pattern of previously reported cadaveric data. The less consistent results seen in models with subject-specific joint kinematics may be due to greater inter-subject variability within estimated kinematics for both the TFJ and PFJ. Further improvements to both the developed MTU wrapping surface optimisation framework and joint kinematic models may produce more consistent results for models with subject-specific joint kinematic models. Although not implemented within this thesis, the models produced using this proposed framework can be used to investigated the regional loading of the TFJ during a range of locomotion and other dynamic tasks. The framework presented here represents a large advancement of the field of subject-specific computational modelling. Along with addressing a number of short comings of previous models, the methods presented in this thesis are predominately automated which reduces the time and cost burdens which are typically associated with building subject-specific models. These time and cost burdens are related to the collection and segmentation of full lower limb MRI. Additionally, all developed methods utilise free and open-source software, facilitating the sharing and wider adoption of these subejct specific methods and models. Improvments to these developed models and methods facilitate their use in both academic research as well as a number of clinical and medical applications. The highly automated and tuned framework reduces both the time and knowledge burden on the user, providing further advantages to these methods.