Subject-specific upper extremity modelling

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Subject-specific

upper extremity modelling

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Subject-specific

upper extremity modelling

Proefschrift

Ter verkrijging van de graad van doctor aan de Technische Universiteit Delft;

op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben; voorzitter van het College voor Promoties

in het openbaar te verdedigen op dinsdag 28 oktober 2014 om 12:30 uur

door

Bart BOLSTERLEE

Master of Science in Biomedical Engineering, geboren te Alkmaar

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Dit proefschrift is goedgekeurd door de promotoren: Prof. dr. H.E.J. Veeger

Prof. dr. F.C.T. van der Helm

Samenstelling promotiecommissie:

Rector Magnificus, Technische Universiteit Delft, voorzitter

Prof. dr. H.E.J. Veeger, Technische Universiteit Delft / Vrije Universiteit Amsterdam, promotor

Prof. dr. F.C.T. van der Helm, Technische Universiteit Delft, promotor Prof. A.M.J. Bull, Imperial College London

Prof. dr. ir. G. Jongbloed, Technische Universiteit Delft Prof. dr. ir. H.F.J.M. Koopman, Universiteit Twente

Prof. dr. ir. J.H. van Dieën, Vrije Universiteit Amsterdam Dr. M. van der Krogt, Vrije Universiteit Medical Centre

Prof. dr. ir. H. van der Kooij, Technische Universiteit Delft / Universiteit Twente, reservelid

The research described in this thesis was part of the MXL project (www.m-x-l.eu). The MXL consortium aimed to enhance patient safety in joint surgery through assessment and prediction of overload and instability conditions after joint preserving or joint replacing interventions at the shoulder, hip and knee joints. This project has received funding from the European Union’s Seventh Framework Program for research, technological development and demonstration under grant agreement n° 248693.

ISBN: 978-94-6259-332-0

Keywords: musculoskeletal modelling, subject-specific, upper extremity, MRI, ultrasound Book and cover design: Bart Bolsterlee

Cover pictures provided by Karen J. Hatzigeorgiou – http://www.karenswhimsy.com Printed by: Ipskamp Drukkers, Enschede, Netherlands – www.proefschriften.net The paper used in this document is FSC certified.

All rights reserved. No part of this book may be reproduced by any means, or transmitted without the written permission of the author. Any use or application of data, methods and/or results etc., occurring in this report will be at the user’s own risk.

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There goes my hero, he’s ordinary. — Dave Grohl

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6

Introduction

1

Chapter

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1.1 B

ACKGROUND

At this moment in evolutionary time, the typical human body contains 206 bones and, depending on what definition is used, approximately 700 muscles. The musculoskeletal system provides stability and support to the body, protects the vital organs and gives us a distinct human shape. It also allows us to move, which is essential for the existence of human life. Not only out of curiosity on how the body works, but also to understand why the musculoskeletal system sometimes fails to fulfil its normal function (and how to effectively treat or prevent this), the working mechanisms of the human movement system have been a topic of research for centuries, or even millennia. In this line of research, insight into the forces produced by our muscles, which cause all voluntary human movements, are of great help. Many factors, for example the central nervous system and hormones, influence the development of muscle force. Regardless the processes that cause and regulate muscle activations, it results in forces on our skeleton and, in the absence of counteracting forces, skeletal movements. The interaction between muscle force and skeletal movements is dictated by mechanical principles and the application of mechanical laws – the laws that describe movement of a mass as a result of a net force on that mass – to simulate the behaviour of the human movement system can therefore help to reveal part of its function and dysfunction.

The area of biomechanics that studies the mechanical interaction between muscle action, joint function and movements of the skeleton is usually referred to as ‘musculoskeletal mechanics’. The most important simulation tools in this field are musculoskeletal models. The need for such models arises from the limitations on direct, non-invasive measurements of quantities of clinical and biomechanical interest, most importantly muscle and joint forces. Musculoskeletal models can estimate muscle and joint forces from more easily measurable external forces and movements of the skeleton. To estimate muscle forces, either a forward or an inverse dynamic approach can be adopted (Erdemir et al., 2007). In forward dynamics, the resulting movement is predicted from an assumed or measured set of muscle forces or joint torques, which can then be optimised to match kinematic recordings. Inverse dynamics follows the opposite path and uses a motion as input and predicts the muscle forces that could have resulted in this movement. Mainly for computational reasons the inverse approach has become more popular, at least for large-scale models which simulate multiple joints and muscles simultaneously.

Models have been developed for various areas of the human movement system. This thesis focuses on the shoulder, which for its ability to maintain the arm stable over a very large range of motion is one of the most complex areas of the human movement system (Veeger and Van

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11 der Helm, 2007). A more appropriate term than ‘shoulder’ is ‘shoulder girdle’, since it is the combination of movements of clavicle, scapula and humerus that enables the arm to move with respect to the trunk. Today’s shoulder models are used for a variety of applications, for example to study shoulder stability (Ackland and Pandy, 2009), find the optimal location for replacing a tendon to (Magermans et al., 2004a), improve prosthesis design (Kontaxis and Johnson, 2009) or determine joint loading in for example wheelchair propulsion (Van Drongelen et al., 2005a). In Chapter 2 of this thesis a more detailed description of biomechanical principles of musculoskeletal shoulder models and an extensive overview of their applications thus far is presented.

Model complexity and applicability

From the earliest records of biomechanical analysis of the human movement system performed by Giovanni Alfonso Borelli in the 17th_{century onwards (Fig. 1.1), complexity of}

musculoskeletal models has increased vastly to get where we are now: three-dimensional, computer-based models that solve mathematical equations to simulate the mechanical behaviour of multiple joints and many muscles simultaneously (for example Blana et al., 2008; Garner and Pandy, 2001; Nikooyan et al., 2011b). These models are often referred to as large-scale models, in contrast to simpler one- or two degree of freedom models that simulate the effect of only one or a few muscles. The development of three-dimensional, large-scale models was driven by the inability to realistically reproduce musculoskeletal behaviour with simpler models that simulate only few degrees of freedom (Jinha et al., 2006) or only two dimensions (Glitsch and Baumann, 1997). Some muscles cross multiple joints, most joints have multiple

Fig. 1.1 Schematic drawing of the arm and scapula (Borelli, 1679)

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degrees of freedom and all joints are crossed by multiple muscles. As a result, activation of one muscle strongly affects adjacent muscles and joints as well. For example, an important function of the biceps is to flex the elbow, but as it attaches to the radius and scapula (so not to the humerus and ulna for which the elbow joint forms the connection), biceps activation also has a mechanical effect around the glenohumeral and radio-ulnar joint. By only simulating its capacity to flex the elbow and ignoring the effects around other joint axes (which should be compensated for by activating other muscles when only elbow flexion is desired) elbow and shoulder function cannot be well understood. Most biomechanical researchers therefore agree that realistic prediction of musculoskeletal function often requires a three-dimensional description of multiple joints and muscles.

A model is by definition a simplified representation of reality, and preferably is only as complex as the application requires it to be. By adding complexity, the simulations will become more realistic and the model will thus have more applications. However, making a model structurally more complex often requires additional parameters, or higher levels of detail, to describe these structures. Because the uncertainty of model predictions is mainly determined by 1) the structure of the model elements and 2) the accuracy of the parameters that describe these structures, the increase in number and/or level of detail of parameters with increasing complexity might cancel out the effect of the more detailed structure. The until now unsuccessful implementation of Huxley-type muscle models (Huxley, 1957) in today’s large-scale models illustrates this: Huxley models describe the biological processes underlying muscle contracture and force development in great detail, yet require too many hardly measurable parameters for useful application in models of multi-joint systems. Instead, descriptive Hill-type models have become state-of-the-art (Hill, 1938), which provide less detail of muscle contraction mechanisms, but describe muscle mechanics accurately enough

Fig. 1.2 Schematic representation of the most important parts of a musculoskeletal model and the parameters used in each of these parts.

skeleton moment

arms muscle

force moment position_{external forces}

inertial parameters geometrical parameters muscle parameters motion equations load sharing muscle dynamics neural input

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13 for many musculoskeletal modelling purposes.

Over the past decades, advancements in computational power and tools to quantitatively describe muscle, joint and skeletal function provided the means to implement higher levels of detail in musculoskeletal models, resulting in an increase in complexity of models. The trade-off between model complexity, measurability of model parameters and applicability has now resulted in musculoskeletal models that include three major components: a muscle model, a joint geometry model and a skeletal model (Fig. 1.2). Each component requires a set of parameters that describe different aspects of anatomy, often referred to as morphological parameters.

The muscle model describes the biological processes from arrival of a neural input until force production and (in absence of counteracting forces) muscle contraction. Hill-type parameters are typically used to describe this process (Hill, 1938; Winters and Stark, 1985) and most importantly include muscle length, muscle optimal length, physiological cross-sectional area (PCSA), maximum muscle stress (σmax) and tendon length. In large-scale models, the

mechanical effect of muscles with large attachment sites is included by subdividing a muscle into multiple lines of action or muscle elements (Valente et al., 2012; Van der Helm and Veenbaas, 1991). Through tendons each muscle is connected to two or more bones and thus crosses at least one joint, but in the case of the shoulder and elbow almost always multiple joint axes. Muscle activation will therefore result in mechanical moment(s) equal to the product of the muscle force and moment arm(s) with respect to those joint(s). Moment arms, which depend on the location of joint rotation centres and musculotendon attachments, are thus the most important parameters to describe joint function. A non-zero sum of the moments produced by all muscles crossing a joint will result in joint rotation or forces on the environment, or both. This step from joint moments to movement is mathematically described by the motion equations in the skeletal model and requires segment dimensions, masses and inertia. Once joint moments are calculated from measured movements and external forces (as typically done in an inverse dynamic analysis), it is not straightforward at all to calculate the muscle forces that have produced these joint moments. This is because almost all joints are crossed by multiple muscles, and multiple combinations of muscle activations can therefore result in the desired joint moments. To solve this muscle redundancy problem, a strong assumption based on motor control principles is made: from all possible solutions, those muscles are selected that minimise a cost function related to some physiological quantity, for instance muscle stress or muscle energy (see Tsirakos et al. (1997) for an overview).

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The three-component structure – a muscle model, a joint geometry model and a skeletal model – is used by most large-scale models today, but different applications and parts of the human movement system place different demands on these components and its parameters. This will be explained for the Delft Shoulder and Elbow Model (DSEM, Nikooyan et al., 2011b; Van der Helm, 1994b), one of the more sophisticated shoulder models today and used for the simulations in this thesis. The basis for the current DSEM was formed by a kinematic model of the shoulder designed to study glenohumeral arthrodesis (Pronk, 1989). Soon it was concluded that forces on the shoulder could only be understood by simulating muscle function as well. This resulted in the Delft Shoulder Model (Van der Helm, 1994b) which included four bones (thorax, clavicle, scapula and humerus) and twenty muscles. The data required to describe these anatomical structures were measured on seven cadavers (Van der Helm et al., 1992; Veeger et al., 1991), implemented in the model and used to evaluate the dynamic behaviour of the shoulder (Van der Helm, 1994a). However, the close functional relationship between shoulder and elbow posed limitations on the fidelity of the simulation results (and thus on the applicability of the model), leading to the inclusion of the elbow (Minekus, 1997; Veeger et al., 1997). Combining morphological data from different sources

Fig. 1.3 Publications with ‘subject- or patient-specific biomechanical model’ as topic as percentage of total ‘biomechanical model’ publications. Source: Web of Science™ Core Collection. Search query for total biomechanical model publications: “biomechanic* AND model”. Search query for subject- or patient-specific publications: “biomechanic* AND model AND (subject-specific OR patient-specific)”

1995 2000 2005 2010 2013 0 1 2 3 4 5 6 Year of publication

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15 leads, as we will also see later, to inconsistencies in the model, so in an extensive cadaver study all relevant model parameters were measured on one specimen (Klein Breteler et al., 1999). This dataset also includes optimal fibre length (a muscle parameter that describes the force-length characteristics of a muscle) and is implemented in the latest version of the DSEM (Nikooyan et al., 2011b). Both in mechanical structure as well as quality of morphological parameters, the DSEM is among the most sophisticated shoulder models available today and has been proven useful to answer many research questions related to shoulder function and dysfunction (see Chapter 2 for an overview of applications).

Subject-specific modelling

Until recently, it was very difficult or infeasible to measure morphological parameters in vivo and almost all models are generic models based on cadaveric data (for example Amis et al., 1979; An et al., 1981; Klein Breteler et al., 1999; Langenderfer et al., 2004; Lieber et al., 1992; Murray et al., 2000; Veeger et al., 1991; Veeger et al., 1997; Ward et al., 2006). Generic models thus have a limited capacity to differentiate between subjects, since inter-individual morphological differences are not included. These differences are believed to, for example, explain differences in onset, progression and response to treatments of muscle and joint diseases and can so assist in the development of more effective, patient-specific treatments. It has also been suggested that the use of a generic version of the DSEM was one of the main sources of error when comparing predicted glenohumeral joint forces against in vivo measurements (Nikooyan et al., 2010). The inclusion of subject-specific morphology is thus thought to be of great value for future applications (Blemker et al., 2007; Prinold et al., 2013). Advancements and increasing availability of imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasonography have opened new possibilities for in vivo measurement of formerly less accessible parameters. This makes subject-specific models – models that include morphological characteristics of specific subjects of patients – more feasible today and. As evidenced by the increase in development of subject- of patient-specific models over the past two decades, subject- or patient-specific modelling is a topic of increasing interest in biomechanics (Fig. 1.3). Methods have for example been proposed to personalise bone geometry (Scheys et al., 2006), muscle volume and PCSA (Holzbaur et al., 2007b), moment arms (Arnold et al., 2000), three-dimensional muscle architecture (Froeling et al., 2012) and even sarcomere lengths (Llewellyn et al., 2008). It should however be kept in mind that, as also mentioned previously, a model should be kept as simple as the application requires it to be. It will thus strongly depend on the ability of subject-specific models to improve upon existing models whether this trend will indeed lead to successful, new applications. In a previous PhD study by Kaptein (1999) it was concluded that

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new techniques (he mainly used MRI scans) indeed allowed for in vivo estimation of previously less accessible parameters, but that applications of subject-specific models were still lacking due to the substantial effort associated with the data acquisition and parameter estimation, and the inability to measure all relevant model parameters even with the most sophisticated techniques.

Regardless of the method that is used to make a parameter subject-specific, it involves extra effort compared to the use of a generic value. Mainly three factors determine whether making a parameter subject-dependent is worth this effort: sensitivity, variability and measurability. By sensitivity we mean the effect that alterations from a given nominal parameter value have on relevant model predictions, in our case muscle and joint forces. The higher the sensitivity of a parameter, the more force predictions are changed as a result of discrepancies between generic and actual parameter value and the more model predictions will benefit from personalisation. There is no straightforward answer to the question what the most sensitive model parameters are, as this will depend on the simulated task, the simulated parameter variations and how interactions (co-variations) between parameter values are modelled (Hughes and An, 1997). A comprehensive sensitivity study on Hill-type muscle parameters of a lower extremity model showed that tendon length was the most sensitive parameter for simulated gait (Ackland et al., 2012). Because of the complex interaction between model parameters, it is hard to generalise the results of sensitivity studies and the sensitivity should therefore be determined separately for each model, parameter and task.

Although sensitivity is an important factor, it is mainly the combination of sensitivity and variability that determines the impact of personalisation. This is illustrated in Fig. 1.4: personalisation of a highly sensitive parameter that is relatively constant across individuals will still have marginal effect on model predictions, while a less sensitive parameter with considerable inter-individual variation can change muscle and joint force predictions more. Because most studies that present data have focused on a subset of the parameters used by large-scale models and/or measured only a few subjects, limited information is available on parameter variability.

Measurability refers to the ease and accuracy with which a parameter can be measured, whether directly or indirectly, and thus relates to the effort to be spent for personalisation. If the personalisation impact of a given parameter is low, but a personalised value can easily and accurately be obtained, it is still worth including in the model. Measurability thus relates to the effort spent on making a parameter subject-specific. Driven by further technological

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17 advancements or clinical research, the measurability of at least some parameters can be expected to further increase in the future.

As customising all morphological parameters is infeasible and probably not necessary for many applications, insight into these three factors – sensitivity, variability and measurability – will help decide on which parameters researchers should focus to make a model subject-specific. However, the improvement of model predictions will ultimately determine the possible increase in application range and thus the success of the model. To validate this improvement, model predictions should be compared against a reference standard. Since the most relevant model outcomes – muscle and joint forces – cannot be measured (this is, paradoxically, the reason to model), validation has traditionally been a problem in musculoskeletal modelling. To date even the dataset that provides the most advanced reference standard for a shoulder model, directly measured glenohumeral joint force (Bergmann et al., 2007), has severe limitations: joint contact force is only a summation of individual muscle forces and the forces were measured on patients with shoulder implants and known limitations in range of motion, and thus not representative for typical shoulder loading of healthy subjects for which most models are designed. The observation that DSEM predicted glenohumeral joint forces on average 31% lower during arm elevation and 34% higher during maximum force tasks than measured on these patients (Nikooyan et al., 2010), can therefore not be generalised to other DSEM predictions. In earlier efforts to evaluate DSEM predictions, muscle force estimates were compared to electromyographic (EMG) recordings of muscle activity (Van der Helm, 1994a) and found to agree reasonably well. This

Fig. 1.4 Schematic representation of the influence of model sensitivity and parameter variability on the effect that personalisation has on model predictions. The parameters that have the highest combination of sensitivity and variability will have the largest effect.

Model

sensitivity

P

ara

met

er

va

ri

ab

ili

ty

low

Small effect

of personalisation

high

low

high

Medium effect

of personalisation

Large effect

of personalisation

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can only be considered a qualitative validation, as the EMG signal provides information about when a muscle is active or inactive, but not much about the magnitude of muscle force. Problems related to model validity pose perhaps the most challenging problem in subject-specific modelling: it should be demonstrated that subject-subject-specific model predictions are significantly closer to the actual values than predictions of a generic model.

1.2 P

ROBLEM DEFINITION AND AIM

Insight into inter-individual differences in muscle and joint loading is expected to explain differences in human function and to lead to the design, improvement and evaluation of personalised treatments of muscle and joint diseases. Because these forces can hardly be measured in vivo, musculoskeletal model estimates are used. The current state-of-the-art models have limited capacity to differentiate between subjects because inter-individual anatomical characteristics are generally not or only partially included. Recent advancements in medical imaging methods have increased the in vivo measurability of some model parameters that make the development of subject-specific models more feasible today. Whether personalisation outweighs the effort will however depend on the trade-off between measurability, sensitivity and variability of parameters. Given the improvement in measurability of certain parameters, but the largely unknown impact of the use of personalised parameters on musculoskeletal model predictions, a re-evaluation of the optimal trade-off between these factors is needed. This gave rise to the work presented in this thesis. The main goal of the research presented in this thesis is to determine whether personalisation of musculoskeletal modelling parameters is worth the effort. To achieve this main goal, three objectives have been defined:

1. To develop new recording and data processing methods to personalise modelling parameters.

2. To quantify the effect of personalisation (sensitivity and variability) on muscle and joint force predictions with the DSEM.

3. To validate the improvement of parameter personalisation by comparing both generic as well as subject-specific DSEM predictions to functional measurements. Achieving these objectives will help us understand what personalisation can contribute to the improvement of the DSEM. On a more general level, the findings will also be relevant for future developments of all other subject-specific musculoskeletal models.

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1.3 R

ESEARCH METHODS

To achieve the goals of this thesis, some of the most advanced methods to measure in vivo anatomy have been used: magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). To obtain model inputs or validation measures, some conventional measurement methods were used to record skeletal kinematics, muscle activations and external forces. The focus will be on parameters which can be measured from MRI and DTI scans: muscle volumes, PCSA, fascicle lengths and pennation angles. Because in subsequent chapters the specific methods used for that study will be described in more detail, we will here only give a brief description of the use of MRI and DTI to personalise morphological parameters.

Magnetic resonance imaging

An MRI scanner uses a combination of a powerful magnet and radio waves to measure the nuclear spin properties of hydrogen atoms in the scanned volume. Because these properties are different per tissue, the MRI signal can be used to obtain detailed images with high contrast between different anatomical structures such as bone, muscle and fat (Fig. 1.5). When made at sufficient resolution, the boundary between muscles can be distinguished, but not the orientation of fibres within the muscle. The use of magnetic resonance imaging or MRI has many potential applications in biomechanics (Blemker et al., 2007). Direct MRI-based measurements of morphological parameters are for instance muscle volume (Holzbaur et al., 2007b), but from three-dimensional reconstructions of muscle attachment locations and muscle wrapping surface moment arms can also be derived for some muscles (Arnold et al., 2000). The main advantage of MRI is its ability to image different anatomical structures with high spatial resolution without exposing the subject to harmful radiation. Its low temporal resolution, however, strongly limits its applicability to study dynamic processes such as skeletal movements, although some advances are made in this area (Sheehan, 2007; Sheehan, 2012). MRI scans come at substantial costs and availability of scan time is often limited. Also, the processing of MRI data to get to useful muscle parameters for musculoskeletal models is time-consuming, as automatic methods to trace the boundaries of muscles are not yet available. In this thesis, MRI scans of the shoulder and elbow are used to measure muscle volumes (Chapter 5).

Diffusion tensor imaging

An MRI protocol based on the diffusion properties of hydrogen atoms, diffusion tensor imaging or DTI (Mori and Zhang, 2006), is capable of revealing muscle architecture three-dimensionally (Froeling et al., 2012; Sinha et al., 2011). Orientation of fibres within a muscle cannot be obtained from anatomical MRI scans, but the DTI signal can be used to reconstruct

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muscle fascicle orientation, pennation and length (Heemskerk et al., 2009). The DTI signal has a lower signal-to-noise ratio, is more susceptible to imaging artefacts and has longer acquisition times than anatomical MRI scans. The application of DTI to study muscle architecture is still in its early stages and the muscle reconstructions have not yet been applied in large-scale musculoskeletal models. In Chapter 6, one step towards possible implementation is taken by the introduction of a new method to reconstruct muscle fascicles from the noisy DTI signal. These reconstructions are then compared to a simpler method for

Fig. 1.5 a) Field of view of the MRI scan. The grey area indicates the scanned volume, the dashed line the location of the image displayed in b and c. b) Example of an axial T1-weighted MRI slice of the shoulder. Note the high contrast between skin (white), muscle (grey) and bone (black = cortical bone, white = cancellous bone). The thin layer of fatty tissue between different muscles shows up as white. c) Same MRI slice as presented in (b), but with each muscle masked by a different colour. d) Three-dimensional reconstruction of shoulder muscles from the MRI scan.

anterior posterior lateral medial humerus scapula a) b) c) d) skin +fat muscle

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21 obtaining information on fascicle orientation, namely a method based on ultrasound.

Ultrasound

Ultrasonography or ultrasound is a relatively low-cost, easy to apply and clinically widely used technique to visualise internal tissues. From the reflection of a pulse of ultrasound (a high frequency wave), an image of the structural anatomy of the underlying tissue can be reconstructed. For our purposes the most interesting feature is that ultrasound can be used to visualise the orientation of muscle fascicles (Narici, 1999). In contrast to MRI and DTI, which can almost exclusively be used for static imaging, ultrasound has the benefit of having a good temporal resolution and can so be used for studying active muscle contractions (Cronin and Lichtwark, 2013). However, the field of view is low (in the order of 5 × 5 cm) and the image is two-dimensional. Because the probe is typically placed manually on the skin, it is usually not known under what angle to the fascicle the image was obtained. Any deviation from the plane in which the fascicle is oriented influences architectural parameter estimates (Klimstra et al., 2007). In Chapter 6 the accuracy of ultrasound-based fascicle length and pennation angle estimates is determined by comparing these to three-dimensional fascicle reconstructions from DTI.

1.4 O

UTLINE

This thesis comprises seven chapters. Although they have a common theme – subject-specific musculoskeletal shoulder modelling – they can be read independently and have been published as such.

Chapter 2 gives an extensive overview of applications of the most commonly used comprehensive shoulder models. The working principles of these models are explained there as well. A substantial part of this chapter is dedicated to recent advancements in measurement methods of musculoskeletal model parameters and the implications of this for future models. The three chapters that follow each present methods to individualise model parameters and evaluate the effect of the personalised values on model predictions of the DSEM. In Chapter 3 an improvement to an existing method to estimate muscle attachment sites of the scapula is presented. A sensitivity study is performed to assess the effect of errors in these attachment sites on force predictions. The sensitivity to variations in attachment site locations is compared to variations in other muscle parameters. A kinematic aspect specific to the shoulder – the closed-chain mechanism formed by the thorax, clavicula and scapula – is studied in Chapter 4. A scaling method is presented that reduces the discrepancy between measured and simulated clavicular and scapular kinematics. The effect on kinetic model

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predictions is evaluated as well. Chapter 5 uses MRI data to measure muscle volumes, which are then used to calculate subject-specific PCSA values. A new validation technique based on the calculation of maximum muscle stress from maximum force recordings is developed and used to quantify the improvement of the subject-specific models over the default, generic model. Electromyographic (EMG) recordings are used to further validate some individual muscle force predictions.

Muscle architectural measures obtained with ultrasonography are compared to DTI measures in Chapter 6. Because DTI has only recently been applied to study muscle architecture, a muscle with a relatively simple architecture (the medial gastrocnemius) is used for evaluation. Although no musculoskeletal model simulations were performed for this study, the results give insight into the feasibility of using ultrasound- and DTI-based measures to make musculoskeletal models subject-specific.

In the last chapter, Chapter 7, it will be discussed what the inclusion of subject-specific parameters has brought to improve model predictions, what this implies for the future of subject-specific modelling and, more broadly, if adding complexity to large-scale models will indeed lead to new applications, as is often suggested. The chapter ends with some recommendations for topics on which, in our opinion, future research in musculoskeletal modelling should focus.

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Clinical applications of musculoskeletal

modelling for the

shoulder and upper limb

This chapter is published as:

Bolsterlee, B., Veeger, H.E.J., Chadwick, E.K., 2013. Clinical applications of musculoskeletal modelling for the shoulder and upper limb. Medical & Biological Engineering & Computing 51 (9), 953-963.

2

Chapter

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A

BSTRACT

Musculoskeletal models have been developed to estimate internal loading on the human skeleton, which cannot directly be measured in vivo, from external measurements like kinematics and external forces. Such models of the shoulder and upper extremity have been used for a variety of purposes, ranging from understanding basic shoulder biomechanics to assisting in preoperative planning. In this review, we provide an overview of the most commonly used large-scale shoulder and upper extremity models and categorise the applications of these models according to the type of questions their users aimed to answer. We found that the most explored feature of a model is the possibility to predict the effect of a structural adaptation on functional outcome, for instance, to simulate a tendon transfer preoperatively. Recent studies have focused on minimising the mismatch in morphology between the model, often derived from cadaver studies, and the subject that is analysed. However, only a subset of the parameters that describe the model’s geometry and, perhaps most importantly, the musculotendon properties can be obtained in vivo. Because most parameters are somehow interrelated, the others should be scaled to prevent inconsistencies in the model’s structure, but it is not known exactly how. Although considerable effort is put into adding complexity to models, for example, by making them subject-specific, we have found little evidence of their superiority over current models. The current trend in development towards individualised, more complex models needs to be justified by demonstrating their ability to answer questions that cannot already be answered by existing models.

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2.1 I

NTRODUCTION

Knowledge of internal forces on the human musculoskeletal system is useful when attempting to understand human movement control, to assist in prevention and treatment for musculoskeletal disorders and to predict the effect of surgical intervention. In general, these forces cannot be measured in vivo. A musculoskeletal model applies the basic laws of mechanics to the human musculoskeletal system to estimate internal loading from measurable variables such as motion descriptions (kinematics) and forces exerted on the environment (external forces). This review is concerned with musculoskeletal models of the human shoulder and upper extremity and discusses only those models that, in contrast to finite element models (Favre et al., 2009), assume bones to be undeformable, i.e., rigid-body models. After describing some principles of shoulder biomechanics and some typical features of shoulder musculoskeletal models, we give an overview of studies that used these models. We categorise these studies according to the type of question the researchers aimed to answer by the use of their model. The purpose of this review is to discuss the current state of musculoskeletal shoulder and upper extremity models and give our view on what can be expected from recent and future advances in shoulder modelling.

2.2 S

HOULDER BIOMECHANICS

Although the most substantial part of motion between the arm and the trunk originates from rotation of the glenohumeral (GH) joint, neglecting scapulothoracic (ST) motion is a simplification of shoulder biomechanics that seriously limits its ability to help in understanding shoulder (dys)function. At the GH joint, the humeral head articulates with the glenoid fossa of the scapula. The articulation between thorax and scapula is formed by an area on the posterior wall of the thorax, called the scapulothoracic gliding plane, over which the anterior wall of the scapula is restricted to move. The clavicle articulates on the medial side with the sternum at the sternoclavicular (SC) joint and on the lateral side with the scapula at the acromioclavicular (AC) joint and so closes the kinetic chain of thorax, clavicle and scapula. The SC, AC and GH joint are typically modelled as ideal three degree-of-freedom (DOF) ball-and-socket joints, thereby ignoring joint translations (see section ‘Glenohumeral stability’). In most models, the ST gliding plane is incorporated by assuming a constant distance between the medial border of the scapula and the posterior side of the thorax, usually approximated by an ellipsoid (see section ‘Modelling clavicular and scapular movements’). In healthy subjects, up to 120° of thoracohumeral elevation can be established by the GH joint (Harryman Ii et al., 1993). Furthermore, the humerus can typically rotate around its long axis

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up to 135°, allowing for positioning the hand in an area that covers almost 2/3 of a sphere (Engin and Chen, 1986). Above 30° of thoracohumeral elevation, ST motion typically follows GH elevation in a ratio of approximately 1:2, called the scapulohumeral rhythm (Codman, 1934). At full arm elevation, the scapula is rotated laterally by approximately 50°–55°, but to execute most tasks of daily living, the humerus is not fully elevated and scapular lateral rotation does typically not exceed 30° (Magermans et al., 2005; Van Andel et al., 2008). The relatively small area of contact between the humeral head and the glenoid fossa results in high mobility of the GH joint, but this comes at the price of low mechanical joint stability (Veeger and Van der Helm, 2007). The ligamentous structures of the GH joint (capsule, glenohumeral ligaments) almost exclusively have a mechanical effect at the end of the range of motion. Therefore, active stabilisation of the GH joint by the rotator cuff muscles is thought to be necessary in intermediate positions. Because the scapula’s only bony connection is formed by the AC-joint, its position is predominantly determined by a balance between scapulothoracic muscles (serratus anterior and pectoralis minor versus trapezius and the rhomboids). An overview of the most important shoulder muscles and their function can be found in a review by Veeger and Van der Helm (Veeger and Van der Helm, 2007). Because most shoulder muscles span multiple joints, and all shoulder joints have multiple DOF, activation of one

Fig. 2.1 Visual representation of a musculoskeletal upper extremity model (Holzbaur’s Model) in OpenSim. Muscles are represented in red. Only a selection of all muscles in the model is shown.

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27 muscle to produce a torque in one specific direction will typically result in (undesired) torque production around other joint axes too. To avoid this undesired torque to have an effect on the resulting motion, it should be compensated for by the activation of other muscles, leading to involvement of the full kinetic chain. This imposes serious limitations on models that only model parts of this chain, for instance, by assuming a fixed scapula or by considering only two-dimensional motions (De Luca and Forrest, 1973; Dul, 1988; Poppen and Walker, 1978).

2.3 S

HOULDER MODELLING

Forward and inverse dynamic models

From Newton’s laws it is known that in the absence of counteracting forces, a force on a body will result in acceleration. A forward dynamic musculoskeletal model follows this line of cause and consequence: the forces (for instance muscle and gravitational forces) on the skeleton are input and the result of these forces – motion of the skeleton – is predicted. However, in human movement analysis, motion is typically the measured variable, and muscle forces are the unknowns. The predicted motions can be compared to motion recordings (Anderson and Pandy, 2001; DeGoede and Ashton-Miller, 2003), or muscle parameters can be optimised such that they match the recorded motions or joint torques (Koo and Mak, 2005). Benefits of forward dynamic models are that they do not require a priori assumptions on the motion that should be followed to perform a certain task and the possibility to explicitly include muscle dynamics (force–length–velocity characteristics, activation dynamics). The high computational load imposed by forward dynamic simulations and the uncertainties in the required musculotendon parameters have, however, until now limited their applicability, although recent improvements in simulation speed may increase their use in future (Chadwick et al., 2009; Van den Bogert et al., 2011).

An inverse dynamics approach is more popular for large-scale musculoskeletal (shoulder) models. Given a motion or posture, the net joint moments are calculated by solving a set of motion equations. Then, a combination of muscle forces is selected that produces these joint moments. Because typically more than one muscle can produce torque around a joint, a load-sharing optimisation is used to select those muscle forces that produce the required joint moments while minimising an objective criterion. Different minimisation criteria can be used, for example muscle stress (Crowninshield and Brand, 1981), muscle energy consumption (Praagman et al., 2006) or muscle fatigue (Dul et al., 1984a). Each load-sharing criterion assumes a strategy for muscle recruitment and therefore influences muscle force predictions (Dul et al., 1984b). In contrast to traditional gait models, most shoulder models use an additional constraint to this optimisation that regulates joint stability (see section

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‘Glenohumeral stability’). Putting additional constraints on this optimisation can simulate the effect of pathological conditions on muscle recruitment strategies. For example, the maximal force of some rotator cuff muscles was reduced for a biomechanical analysis of rotator cuff tears (Magermans et al., 2004a). Another study analysed glenohumeral loading in tetraplegic patients by excluding certain muscles from the load-sharing optimisation, depending on lesion level. Muscle properties are usually included in the model as a part of the optimisation criterion, but can also be used more explicitly to define physiological bounds on muscle forces (Happee and Van der Helm, 1995). A combination of both forward and inverse dynamics that aims to combine the benefits of both methods, i.e., fast computation and explicit muscle dynamics, has also been proposed (Nikooyan et al., 2011b).

Anatomical data

Locations of joint rotation centres, bony dimensions, segment inertia, bony landmarks and musculotendon properties are among the most important parameters to quantitatively describe the anatomy for a musculoskeletal model. Inertial properties are required to relate joint moments to joint rotations and have most influence on simulations of highly dynamic motions. Values are mostly derived from regression equations that use anthropometric measurements as dependent variables (Hinrichs, 1985). Muscles are typically modelled as Hill-type one-dimensional structures (lines) that, dependent on the complexity of the muscle model, consist of a passive (viscoelastic) and active (contractile element) part (Fig. 2.1). The force generating capabilities are usually defined by parameters such as physiological cross-sectional area (PCSA), optimal fibre length, tendon length and pennation angle. Muscle moment arms in the simulated position are usually derived from geometrical data, namely muscle attachment sites and joint rotation centres. Whenever the straight line between origin and insertion of a muscle intersects a bone or another anatomical structure, a more realistic path is calculated by the use of wrapping objects (as in Van der Helm et al., 1992), by constraining the muscles to pass ‘via points’ (as in Holzbaur et al., 2005) or by a combination of both. The muscles are assumed to slide over the wrapping objects without friction.

In most currently available models, anatomical parameters are derived from cadaver experiments (for example Högfors et al., 1987; Lieber et al., 1992; Veeger et al., 1991) and are often referred to as generic models. Because of the high coupling between morphological structures and soft tissue parameters within a human, the use of a consistent dataset (measured on one cadaver) seems most warranted (Praagman et al., 2010). However, most models use data originating from different sources, raising the question of to what extent morphological differences between donors will influence prediction results. This is in fact the

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29 same issue as the question of to what extent morphological differences between the subject that is analysed and the model that is used should be taken into account.

Methods have been proposed to individualise, for instance, muscle attachment sites (Bolsterlee and Zadpoor, 2013; Kaptein and van der Helm, 2004) or optimum fibre length (Winby et al., 2008). Because many parameters in a model co-vary (for example, muscle attachment sites and bone dimensions (Murray et al., 2002), adapting only one parameter in a model will by definition introduce inconsistencies (Patel et al., 2012). Adapting the model to the subject by scaling, or other individualisation techniques, improves the anatomical resemblance between the model and the subject (Arnold et al., 2000). However, the goal is to improve force predictions, but the in vivo muscle force measurements needed to verify this have not been available in humans thus far. Later in this paper (see section ‘Model individualisation’ in 2.5), we will discuss in more detail the status and implications of subject-specific modelling.

Glenohumeral stability

The GH joint comprises a small and shallow contact area, allowing dislocation of the joint to occur easily as a result of a glenohumeral reaction force directed outside the glenoid. Typically, the glenohumeral joint is modelled by three orthogonal hinge joints, and translation or dislocation in the joint is not captured. Stability is therefore modelled by constraining the load-sharing optimisation to solutions that result in a GH reaction force that is directed inside the rim of the glenoid (Van der Helm, 1994b). This simplification of GH stability has proven a useful technique in shoulder models to date, as it is considerably simpler than modelling actual subluxation, i.e., translation of the joint. A shortcoming is, however, that for example narrowing of the subacromial space (which is believed to be related to subacromial pain) cannot quantitatively be estimated with models that preclude GH translation. We discuss this latter aspect separately in the section ‘Glenohumeral translation’ (section 2.5).

Modelling clavicular and scapular movements

Abnormal scapular motion (sometimes labelled scapular dyskinesia Kibler et al., 2012) is known to be related to a number of shoulder pathologies such as AC separation, clavicle fracture, impingement, glenohumeral instability, rotator cuff injury and labral injury. For study of the injured shoulder, taking into account the role of the scapula is therefore essential. Models that relate movements of the scapula to thoracohumeral movements by the use of population regression equations (Dickerson et al., 2007; Holzbaur et al., 2005), or ignore scapular movements completely, can neither simulate any abnormal scapular behaviour such

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as scapular winging, nor account for the strong inter-individual differences between healthy subjects (de Groot and Brand, 2001).

Using recorded scapular motion as a model input is not at all trivial. When including inter-individual differences and abnormal patterns of clavicular and scapular motion in a model, two problems arise: first, movements of the clavicle and scapula are difficult to measure because a substantial part of the movement is difficult to track with traditional skin-fixed marker methods (Van Andel et al., 2009). Studies that use bone pins to record bone rotations (Ludewig et al., 2009; McClure et al., 2001) allow for more accurate measurements of shoulder girdle kinematics and are very useful for quantifying (ab)normal behaviour. However, the invasiveness of this procedure limits its application in a clinical setting. Second, the thorax, clavicle and scapula form a closed kinematic chain, causing SC and AC-joint rotations to be highly coupled. Only some combinations of joint rotations are therefore possible, and these allowable rotations are dependent on bone geometry. As a consequence, imposing measured joint angles directly on a differently sized model will often result in an unrealistic simulation with, for example, the scapula inside the ribcage. Measurements therefore need to be adapted for simulations. This can be done, for example, by constraining the scapula to have a (somewhat) fixed distance to the thorax, but it is not yet clear what the consequence of this is for force predictions (Bolsterlee et al., 2014).

2.4 O

VERVIEW OF MODELS AND APPLICATIONS

To date, only a few comprehensive musculoskeletal shoulder models have been developed. In this review, we only discuss the most frequently used large-scale models that include scapular and clavicular motions:

 Swedish Shoulder Model (Högfors et al., 1991; Högfors et al., 1987)

 Delft shoulder and elbow model (DSEM) (Nikooyan et al., 2011b; Van der Helm, 1994b)

 Newcastle shoulder model (NSM) (Charlton and Johnson, 2006)  Holzbaur’s upper extremity model (HM) (Holzbaur et al., 2005)  Anybody upper extremity model (Damsgaard et al., 2006)  Garner’s model (Garner and Pandy, 2001)

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31 Since this paper is concerned with the applications of musculoskeletal models, we looked up publications that used these models. We sorted the studies according to the type of question they aimed to answer and defined the following categories:

1. General principles to understand shoulder biomechanics Studies that provide new or deeper insight into basic principles of shoulder biomechanics.

2. Effect of task differences on shoulder function Studies that compare shoulder loading under different tasks or working positions. Applications in this category are mainly in the field of ergonomics.

3. Effect of structural alterations on shoulder function Studies that assess the effect of a change in morphological structure on shoulder function, for example, to predict the result of a tendon transfer.

4. Effect of inter-individual morphological differences Studies that compare differences in shoulder loading as a result of morphological differences between subjects and thus aim to provide predictions that are specific to that subject.

The type of question a model can answer is of course highly dependent on the level of complexity of the model. Per category, we will discuss the applications for which the models have been used, comment on the complexity of the used models and highlight the limitations. Fig. 2.2 gives a schematic overview of the studies that we included.

General principles: understanding shoulder biomechanics

We found seven studies that used a musculoskeletal shoulder model to gain more insight into the biomechanics of the non-pathological shoulder. The DSEM was used to calculate the capacity of muscles to produce torque around the shoulder joints. This led to the interesting observation that typical muscle function as described in the clinical literature can be fairly limited, because it mainly describes the function of a muscle around a single joint axis, while almost all shoulder muscles drive multiple axes simultaneously (Veeger and Van der Helm, 2007). This model was also used to analyse muscle function during goal-directed movements (Happee, 1994) and wheelchair propulsion (Van der Helm and Veeger, 1996). More recently, the DSEM was used to evaluate the effectiveness of wheelchair propulsion (Bregman et al., 2009). Garner’s model was applied to investigate joint coupling between the elbow and shoulder (Yu et al., 2011) and the stabilising potential of shoulder muscles (Ackland and Pandy, 2009), while trapezius function was evaluated using the NSM (Johnson and Pandyan, 2005).

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The studies mentioned above have in common that they address basic principles of normal shoulder function and as such do not discriminate between individuals, groups or tasks. Because generic models were used with group averaged kinematics or forces as input, the output of the model should be interpreted likewise, namely as the average behaviour of the (cadaveric) shoulder on which the model is based. Using the absolute output values, for instance, muscle forces, to make predictions for a specific subject is therefore not feasible. However, when the outputs are presented as ratios, for example, percentage of scapulothoracic shoulder muscle effort compared to total muscle effort (Happee and Van der

Fig. 2.2 Schematic overview of applications of the most frequently used large-scale musculoskeletal models of the shoulder and upper limb, categorised according to the type of questions the researchers aimed to answer.

Specificity of the model and model inputs

Type of question

Examples of studies Tendon transfer □ Saul (2003) ■ Magermans (2004a,b) ■ Steenbrink (2009) □ Ling (2009) □ Jastifer (2012) Shoulder implants ■ Van der Helm (1994) ■ Suarez (2009) ● Kontaxis (2009) ● Masjedi (2010) Tetraplegia ■ Van Drongelen (2005b) ■ Van Drongelen (2006) Cuff tear □ Saul (2011) ▼ Lemieux (2012a,b) Malunion ■ Chadwick (2004) □ Patel (2012) Neuromuscular control ■ Blana (2012) ■ Hincapie (2008,2009) ■ Kirsch (2001) Stenlund (2002) ■ Veeger (2002) ■ Van Drongelen (2005a) ○ Fischer (2012) ■ Arnet (2012) □ Morrow (2010)

□ Holzbaur OpenSim Model ■ Delft Shoulder and Elbow Model ○ Dickerson Model

● UK National Shoulder Model Swedish Shoulder Model ▲ Garner’s Model

▼ Anybody Upper Extremity Model

■ Happee (1995) ■ Van der Helm (1996) ● Johnson (2005) ■ Veeger (2007) ■ Bregman (2009) ▲ Ackland (2009) ▲ Yu (2011) Wheelchair propulsion ▼ Dubowsky (2008) ▲ ▲

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33 Helm, 1995), these models certainly do help understanding the underlying mechanics of the shoulder. The models were complex enough to capture shoulder function adequately: they include scapular motion, glenohumeral stability constraints and represent muscles with a sufficient number of elements to realistically simulate the diverse lines of action of muscles with broad origins and insertions (Van der Helm and Veenbaas, 1991).

Effect of task differences on shoulder function

In ergonomics research, musculoskeletal models are used to answer questions that are related to differences in loading between different executions of occupational tasks (Stenlund et al., 2002) or to design guidelines for maximal acceptable forces (Fischer et al., 2012). Other examples in this category are studies that calculate shoulder loading and glenohumeral stability during wheelchair propulsion (Morrow et al., 2010; Van Drongelen et al., 2005b; Veeger et al., 2002) and hand cycling (Arnet et al., 2012).

For studies in this category, subject-specific motions or group averages of different tasks were recorded and used as input to predict shoulder loading. The major goal of these studies was to find the least demanding execution of a given task in terms of loads on the upper extremity (Stenlund et al., 2002). Therefore, force predictions between tasks were compared to each other. Most studies ignored inter-individual differences in morphology and used a generic model version of the DSEM, but also the Swedish shoulder model (Stenlund et al., 2002) and the Dickerson’s model were used (Fischer et al., 2012). One study in this category adopted a different approach (Morrow et al., 2010) and scaled the morphology of Holzbaur’s model to fit the subject. They, however, generalised scapular motion by using regression equations and did not include a GH stability constraint. It is not known what the effect of this is on the reported GH joint contact force values, but it may lead to simulations where the muscle forces lead to a dislocated GH joint.

In the studies above, no attempts were made to individualise models. We are of the opinion that this was indeed not necessary, since their prime goal was to differentiate between tasks rather than between individuals. Of greater importance is therefore a realistic description of kinematic and external force differences between tasks. It might of course be possible that as a consequence of anatomical variations, the optimal task execution is subject-dependent, but as long as the differences between the analysed tasks are large enough, we do not expect this to play a major role.

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Effect of structural alterations on shoulder function

Because a musculoskeletal model quantitatively describes the relationship between structure and function of the musculoskeletal system, a shoulder model can be used to predict to what extent (pathological) structural alterations change shoulder function. We found several studies that used this feature by adapting the structure of a shoulder model to simulate the mechanical effect of tendon transfers (Jastifer et al., 2012; Ling et al., 2009; Magermans et al., 2004a; Magermans et al., 2004b; Saul et al., 2003; Steenbrink et al., 2009), shoulder implants (Kontaxis and Johnson, 2009; Masjedi and Johnson, 2010; Suarez et al., 2009; Van der Helm and Pronk, 1994), tetraplegia (Van Drongelen et al., 2005a; Van Drongelen et al., 2006), cuff tears (Lemieux et al., 2012a; Lemieux et al., 2012b; Saul et al., 2011) and malunion of the clavicle (Patel et al., 2012) or scapular neck (Chadwick et al., 2004). Studies in this category have been referred to as ‘what-if’ analyses (Veeger, 2011) and make up the most significant proportion of the publications we found. All of the studies adapted the morphology of a generic model to model changes in structure. Holzbaur’s model predicted the attachment site of the infraspinatus to be the best new attachment location of the latissimus dorsi to compensate for loss in rotator cuff strength (Ling et al., 2009). The use of the DSEM resulted in a different conclusion, namely the supraspinatus attachment site (Magermans et al., 2004a). This clearly demonstrates that the choice of model might lead to a different answer to the research question.

Because almost all available models use an inverse dynamics approach, a motion is used as input, and the effect of the altered structure on this motion is often ignored. The DSEM was used to give a simple ‘yes’ or ‘no’ answer on whether the model could still reproduce a series of movements as measured on non-pathological subjects in the case of large rotator cuff tears before and after tendon transfer (Magermans et al., 2004a). A successful simulation means that the model could find a solution for muscle forces that balance the required net joint moments to make the movement, while maintaining the GH joint stable. A possible change in movement execution was not incorporated. However, by performing a large number of (slightly different) simulations, the change in the number of successful ones under different conditions gives an idea of how successful the transfer could be expected to be. Not all simulation methods appear valid: Patel et al. (2012) studied clavicular malunion with Holzbaur’s model without taking kinematic differences in shoulder girdle movements into account that inevitably result from this structural adaptation (Hillen et al., 2012), which renders the validity of their findings questionable. It is essential to decide beforehand what the effect of the structural change is and how this can best be simulated.

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35 Related to structural alterations, models have been used to assess the effect of restoring function to pathological structures by, for example, using functional electrical stimulation (FES). The DSEM and a SIMM model (Blana et al., 2008) based on the same morphology and principles as the DSEM, have been used to identify optimal muscle stimulation sets for restoration of function in mid- and high-level spinal cord injury (Blana et al., 2012; Hincapie et al., 2008; Kirsch and Acosta, 2001) and to examine control mechanisms for neuroprostheses (Hincapie and Kirsch, 2009).

Effect of inter-individual morphological differences

To date, only one study has made a step towards finding differences at the level of individuals by studying the effect of seating position and seating height on shoulder loading during wheelchair propulsion with a subject-specific version of the Anybody Model (Dubowsky et al., 2008). They did not measure the altered kinematics and kinetics on the rim that were the result of the changed position, however, so their model simulations could not be verified. The reason to use a subject-specific model is to identify inter-individual differences, but because Dubowsky et al. only analysed differences in axle placements for one subject, it cannot be said whether their model would have resulted in different conclusions for different subjects. This was nonetheless an interesting application of the model, and we look forward to further data from this work.

2.5 F

UTURE WORK AND CHALLENGES

Model individualisation

The term ‘subject-specific’ is regularly used for models that modify the morphological structure of a model to more closely match the subject. Subject-specific models have been developed with different levels of detail, ranging from simple scaling methods to full three-dimensional reconstruction from MRI or CT scans. It is currently one of the key research interests in musculoskeletal modelling, because it is generally believed that a subject-specific model will result in closer predictions of shoulder loading for a specific individual or patient and can therefore be applied to answer very specific patient-related questions.

Unfortunately, individualising all parameters of a large-scale musculoskeletal model is very difficult, if not impossible. Geometrical properties such as bone dimensions can accurately be obtained from marker data or, with more detail, from image data such as MRI or CT scans. MRI has also been used to estimate inertial properties (Cheng et al., 2000). Arnold et al. showed that moment arms reconstructed from MRI are only 10 % different from estimations based on the tendon excursion method, which is currently believed to be the most accurate

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method (Arnold et al., 2000). Murray et al. (2002) found linear relationships between muscle moment arms and bone dimensions for some muscles that cross the elbow, but some other muscles did not scale linearly. However, to maintain structural consistency within the model, individualising bone dimensions also requires adaptation of musculotendon properties such as tendon and optimum fibre lengths, but not much is known about how this should be done. These properties are very difficult to obtain in vivo, but are the parameters to which muscle force predictions are most sensitive (Ackland et al., 2012; Scovil and Ronsky, 2006). MRI scans and ultrasound recordings are especially suitable for visualisation of soft tissue structures, but the extraction of model parameters from these image data is not trivial. For example, the directions of fibres within a muscle can hardly be seen on MRI data, complicating the estimation of the muscle’s architectural properties. Ultrasound does provide more detail on muscle fibre directions, but is limited to superficial muscles and small volumes. Diffusion tensor imaging (DTI) may be able to resolve these problems in the future, because it provides a high level of detail on the direction of muscle fibres and can so be used to estimate musculotendon properties (Galban et al., 2004).

Testing the hypothesis that a subject-specific model outperforms a generic model is extremely difficult, because there is no in vivo validation possible on the level of individual muscle forces. Validation is therefore limited to comparison of subject-specific with generic model predictions and by comparison of predicted muscle activations with measured EMG values. A recent study compared model-predicted glenohumeral joint contact forces to in vivo measurements by an instrumented prosthesis (Nikooyan et al., 2010). Although a unique dataset with an unprecedented level of in vivo, quantitative detail was obtained, validation of a model that is supposed to resemble normal behaviour with these patient data is fairly limited, because the patients’ muscle recruitment strategies were most likely still abnormal.

Sensitivity studies demonstrate the relative importance of parameters and are therefore useful for identification of those parameters that can best be individualised. However, the sensitivity values should be combined with observed variations in populations to estimate to what extent the model could benefit from individualisation. Holzbaur et al. (2007b) showed that muscle volume (which is proportionally related to PCSA and therefore to muscle and model strength) varies as much as three-fold across a healthy subject population. To estimate maximum strength of a subject with a musculoskeletal model, PCSA should therefore definitely be individualised. Interestingly, they also found that volume fractions (muscle volume as fraction of total muscle volume of that subject) are highly comparable among different subjects, which implies that PCSA scaling of all muscles of a subject can be done by the same factor. From

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37 data of a study on six elbow muscles of ten cadaveric specimens (Murray et al., 2000), we calculated that variation, expressed as the standard deviation divided by the mean value (coefficient of variation), was as large as 36 % for PCSA, while this was 17.1 % for optimum muscle length, 13.2 % for muscle moment arms and only 10.1 % for tendon length (Table 2.1). Thus, it is mainly PCSA that strongly varies among subjects, while tendon length can rather reliably be estimated (10 % accuracy) by using the average value.

Model individualisation can also be achieved on a different level, namely by individualising the input, for instance, by adding subject-specific EMG recordings (Nikooyan et al., 2012). Furthermore, a model that uses individualised shoulder girdle kinematics instead of regression equations (such as the DSEM and NSM) will result in an output that depends on the subject’s structure (because the recorded motion will be influenced by that structure) and could therefore be called subject-specific.

We feel that a comment on the quest for subject-specific models is warranted. The development of subject-specific models should be driven by clinical questions that cannot be answered by the already available generic models, not by the increase in availability and accessibility of in vivo data. Personalised models have not yet successfully been implemented to (correctly) predict patient-specific clinical outcome measures, although this is one of the key reasons to develop them. To date, validation of only some of the intermediate steps in the

Table 2.1 Variation of musculotendon parameters in six elbow muscles over ten cadaveric specimens. The values are standard deviations expressed as percentage of the mean (or coefficient of variation). Adapted from the data measured by Murray et al.(Murray et al., 2000). Muscle Tendon length (%) Average moment arm (%) Optimal muscle length (%) PCSA (%) Brachioradialis 10.0 11.1 18.0 50.0 Biceps 10.3 8.1 19.4 31.4 ECRL* 4.0 19.0 15.1 33.3 Brachialis 11.2 14.3 13.7 24.1 Pronator Teres 13.3 16.7 16.5 32.1 Triceps 11.5 10.0 19.6 45.0 Average 10.1 13.2 17.1 36.0

Subject-specific upper extremity modelling

Subject-specific

upper extremity modelling

Subject-specific

upper extremity modelling

Table of contents

Introduction

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Clinical applications of musculoskeletal

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shoulder and upper limb

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