Personalised knee models for knee osteoarthritis

ER1 KNEEMO research proposal

Kimmo Sakari Halonen

Project summary/abstract

Osteoarthritis (OA) is a disease in which the articular cartilage, found at the articulating ends of bones, gradually deteriorates. This results in bones grinding against each other, which causes pain and hinders joint movement. The cause of OA is not known, but mechanical stress is believed to have a major contribution in the onset of the disease. As treatment options are very limited, prevention is essential. The present research proposal focuses on developing low cost, non-invasive methods to estimate stresses in various structures of the knee. The core methods involve a detailed musculoskeletal (MS) model, incorporating patient-specific gait data, geometric morphing of bones (to obtain accurate muscle attachment sites), and inverse dynamics (to calculate the muscle forces moving the lower extremity), combined with finite element (FE) modelling to simulate stresses and strains in the knee during different activities of daily living. The developed model can be applied to analyse the relationship between external loads and the internal knee stresses and strains. Specifically, final aim of this project is to investigate how clinically applicable early interventions, such as gait modification and lateral wedge insoles, reduce the mechanical stresses in the cartilage, in both healthy and OA knees.

Section 1. Introduction

Osteoarthritis (OA), especially knee osteoarthritis (KOA) is a major burden to health care. It is estimated that in the US, over 27 million people are have clinical OA [1], and its prevalence per 100 000 people is 10 000 for people over the age of 65 in developed countries (US, EU, Japan, Australia, New Zealand) [2]. In early OA, articular cartilage, a soft non-vascularised tissue starts to fibrillate in its surface. In advanced stages, cracks start to appear and the clefts slowly extend to the cartilage-bone interface, and eventually the cartilage wears away [3]. In the late stages of OA, the disease causes pain and hinders the joint’s movement, often requiring a total knee replacement. The current understanding of OA is that the cause of the disease is a combination of mechanical wear, inflammation and changes in chondrocyte activity [4]–[6]. Furthermore, age is an important factor in the chondrocytic activity [6]. The abnormal stresses and strains the cartilage experiences may be a result of the subject’s natural walking pattern, obesity[7] or an injury [8],[9]. Currently, there is no cure for OA, which is why the prevention of the disease is crucial. The need for a pre-emptive treatment is only going to increase due to the aging of the European population.

Musculoskeletal modelling is a method that aims to describe how external forces and moments act on the human body internally [10]. Subject’s primary daily activities, such as gait, standing, stair climb etc. are first recorded in a motion laboratory by tracking the movement of body segments. This is usually done by attaching skin markers that are then tracked by a multi-camera system, accompanied by a set of force plates to record the forces the subject exerts on the soles of his/her feet. The other common type of motion analysis is dual fluoroscopy, in which two orthogonal fluoroscopes record the knee motion [11], [12]. The downside of dual fluoroscopy is the radiation

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dose it causes on the subject. The marker trajectories and ground reaction forces are then used to estimate the forces and moments acting on the body, which then produce the observed body motion [10],[13].

While musculoskeletal modelling gives the forces acting on the knee joint, it does not tell anything about the stresses and strains the soft tissue in the joint experience. Finite element (FE) modelling, on the other hand, does provide information on the biomechanics of the soft tissues. In FE modelling, the tissue geometry in the knee is divided into small (‘finite’) elements, usually hexahedrons or tetrahedrons, and the different tissues are given their material properties. These material properties are validated in experimental studies. By implementing the forces obtained from the musculoskeletal modelling into the subject-specific FE model, it is possible to simulate the stresses and strains the cartilage undergoes during daily activities like gait. Further, the model enables the simulation of non-invasive corrective measures, such as lateral wedge insoles or gait modification, giving us information on their possible protective effect against OA.

This research is part of the Workpackage (WP) 3 of KNEEMO project.

Aims of the study

1. Develop a personalised detailed knee model capable of estimating the stresses and strains of the different structures of the knee of OA patients.

2. Together with other researchers in the KNEEMO consortium, apply the developed model to investigate how non-invasive interventions, such as gait modification and lateral wedge insoles, affect the internal knee loads.

Section 2. Scientific background

Imaging modalities

In KNEEMO, various imaging modalities will be used. For this proposal, the main goal is to obtain the geometries of the tissues in the knee (cartilages, menisci, ligaments, bones etc.) as opposed to other goals such as data on inflammation. The subjects’ lower extremities will be imaged primarily with magnetic resonance imaging (MRI). If possible, the imaging will be done without using contrast-enhancing agents. If MR imaging is not feasible, the subjects will be imaged using computed tomography (CT). MRI is a non-invasive imaging modality with an excellent soft tissue contrast which causes no radiation dose on the imaged subject. CT has a superior resolution in imaging bones, but requires a contrast agent to distinguish e.g. the menisci from the images. In addition, CT causes a considerable radiation dose to the subject, which is why it is to be avoided if possible.

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Musculoskeletal modelling

The knee is composed of four bones: femur, patella, tibia and fibula. Femur and patella, as well as femur and tibia have mutual articulating surfaces, covered in cartilage on each bone. Vast majority of body weight is transmitted from the femur into tibia through the knee joints, with the menisci helping to distribute the loads to a larger surface. The patella is attached to the quadriceps tendon at its proximal end and patellar tendon at its distal end and provided an extended lever arm for the knee extension. The fibula is attached to tibia through fibrous tissue called interosseous membrane. Fibula’s main function is to provide attachment points for ligaments and muscles, and provide stability to the ankle.

The knee joint is a non-conforming joint, i.e. small secondary translations and rotations are present. The motion is guided by muscles, ligaments and soft tissues like articular cartilage and menisci. In musculoskeletal modelling, the number of degrees-of-freedom (DOF) is determined carefully to avoid the overestimation of muscle forces. The relationship between the forces applied to the body and the motion of the body segments can be described in the form

M (q ) q̈+C (q ) q̇2+G (q )+R (q ) FMT+E (q ,q̇ )=0, (1)

where q̈, q̇ and q are vectors of the generalized coordinates, velocities, and accelerations, respectively. M (q ) is the system mass matrix (n × m), therefore M (q ) q̈ denotes a vector of inertial forces and torques. C (q ) q̇2 is a vector of centrifugal and Coriolis forces and torques (where C is n ×1), G (q ) is a vector of gravitational forces and torques (n×1), R (q ) is a matrix containing the muscle moment arms (n × m), FMT is a vector of musculotendon torques (m× 1, m = number of muscles) and E (q , q̇ ) is a vector of external forces and torques applied to the body by the environment [10], [14]. The system is statically, because the number of muscles is greater than the number of equations (m > n), which means that there are infinitely many combinations of muscle forces that can balance the load.

To overcome this problem, it is common to assume that the central nervous system recruits the muscles according to some optimality criterion. The muscular sharing is solved by minimizing an objective function J (typically the sum of muscle forces normalised by an estimate of muscle strength to some power), subject to constraints that represent the dynamic equilibrium equations and inequality constraints stating that muscles can only pull and not push [15]. The Anybody Musculoskeletal System used in the present research uses several formulations, most commonly the polynomial formulation (with p=3):

J (FMT )=∑i=1

n(MT )

( f i( MT)

N i )p

, (3)

where f iMT and N i are the muscle force and strength of the muscle i, respectively (i∈ {1 ,…,m }) [15].

Traditionally, the knee has been modelled as a simplified hinge with only 1 DOF (the extension-flexion angle) [16]–[18]. Other past methods include modelling the knee as a hinge type of joint

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with an extra DOF in the internal-external direction [10]. Another way of modelling the knee joint is a 1 DOF spatial mechanism that consists of two sphere-on-plane contacts, one for each articulating condyle surface of the femur [19]–[22]. This model also implements three isometric ligaments, the anterior cruciate ligament (ACL), posterior cruciate ligament (PCL) and medial collateral ligament (MCL). Together, these features describe five constraints in one DOF [23]. The advantage this model has is in its computational efficiency. However, it does not capture the complex joint mechanics that dictate the secondary joint movements. A more sophisticated model by Marra et al. (2015) [18] consists of 6 DOF in the tibio-femoral (TF) join and 5 DOF in the patella-femoral (PF) joint (the model assumes patellar tendon to be rigid).

The goal of musculoskeletal modelling is to obtain the forces and moments of the muscles acting on the knee joint, which are then used as an input for the finite element model in order to acquire the stresses and strains the articular cartilage experiences. The finite element modelling is needed because it has been shown that simply reducing the adduction moment in the knee does not guarantee a reduction in medial cartilage stresses [24].

Geometry morphing

The geometry of bones, to which the muscles are attached to, varies from subject to subject. As the attachment sites change, so do the moment arms of the muscles. Traditionally the problem has been addressed by taking measurements of cadaveric specimens or medical images, and then assuming those measurements to represent the anatomy of the investigated subjects when scaled using linear scaling laws [25]–[27]. However, it does not take into account the finer variations between patients, which can result in substantial differences in the resulting motions [28]–[30]. Some studies have used MRI scans to determine the muscle attachment sites [30]. However, it is a very time-consuming process as all muscles need to be segmented from the image stack, as well as their origin and insertion sites. AMS uses the Twente Lower Extremity Model 2.0 (TLEM 2.0), which is a cadaver-based method used to scale the bones, joints and muscle attachments relative to the subject [18], [31]–[34]. Recently, Marra et al. (2015)[18] morphed the TLEM 2.0 model to the patient-specific geometry of a total knee arthroplasty patient using a pre-operative CT scan from hip to the angle joint. Lund et al. (2015) criticized the linear scaling approach that is usually used in MS models [31]. Instead, they proposed a technique that scales cadaver-based MS models to match both segmented lengths and joint parameters of a specific subject, based on having the marker locations in either (1) a standing reference trial and regression equations for hip joint centre predictions, or (2) a standing reference trial and functional joint trials. In this research proposal, we plan to implement the technique of Marra et al. (2015)[18] on the studied subjects.

Finite Element Models

Finite element modelling is a process in which the investigated geometry is discretized by dividing it into small (finite) elements, usually hexahedrons (‘cubes’) or tetrahedrons (‘triangle-based pyramids’), which are then given material properties that have their distinct differential equations that need to be solved for. To put the process very simply, FE solver replaces second order differential equations with first order differential equations with the use of well-chosen boundary conditions and partial integration. This process reduces the equations to a variational problem (i.e. Galerkin method), which is then approximated using the following function [35]–[37]

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uh=∑l=1

N

α l ϕl , (4)

where ϕ l are conveniently chosen test functions and α l are coefficients that need to be solved for. In all FE problems, the equations are reduced into the form of

Kα=b, (5)

where K is called the stiffness matrix, α is the matrix of coefficients α l, and b a set of first order linear differential equations. By solving α=K−1b, the final solution (e.g. for stress at nodes) is

given by uh=∑l=1

N

α l ϕl. The base functions ϕl are chosen to be linear functions that have a value of 1

at nodes and 0 elsewhere, which reduces the equation to uh=∑l=1

N

α l. The approximation is always

solved numerically: first the investigated element is transformed into a base element (e.g. [0,1] in 1D) by using a variable substitution, then a numerical integration method like Gauss quadrature is used to calculate the solution [36].

In FE modelling of the human body, the subject’s tissues are first segmented usually from MR images. The resulting stl-files are transformed into solid geometry (.sat) and imported into a FE program such as Abaqus, FEBio, Ansys etc. and meshed. Meshing is a critical part of model creation. The accuracy of the simulated result depends on the mesh density: the denser the mesh (smaller elements), the more accurate the result. However, increasing the number of elements causes an exponential increase in computational cost, which is why the mesh density needs to be optimized by conducting a mesh convergence test.

The different tissues are assigned their material models, which have been validated in previous experimental studies. Specifically, all articular cartilages will be modelled as fibril-reinforced poroviscoelastic. Articular cartilage is a structurally highly complex, non-vascularized tissue with high water content (approximately 65-80% of the wet weight, depending on the depth). String-like collagen fibres take approximately 75%, proteoglycans 20-30% and chondrocyte cells less than 10% of the dry weight [38]. The collagen fibres form an organised network, where they are aligned parallel to cartilage surface in the superficial zone, randomly in the middle zone, and perpendicularly to the surface in the deep zone [3], [38]. Specifically, all articular cartilages will be modelled as fibril-reinforced poroviscoelastic (FRPVE) [39]–[41].

In the material model, the articular cartilage is modelled as a biphasic material consisting of a fluid phase and a solid phase. The solid phase is further divided into a fibrillar and non-fibrillar part. The total stress σtot is given as

σ tot=σ f +σ nf−I p, (6)

where σf is the fibrillar stress, σnf non-fibrillar stress, p fluid pressure and I unity tensor. Further, the fibrillar stress is defined as a sum of stresses in each individual collagen fibril:

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σ tot=∑i=1

N

σ if +σ nf−I p, (7)

where σ if is the stress of one fibril. Collagen fibrils are defined as viscoelastic, featuring a linear

spring in parallel to a set consisting of a non-linear spring in series with a dashpot. The collagen fibrils are divided into primary and secondary fibrils: four primary fibrils that are oriented along the split-lines in the cartilage surface [42] and otherwise follow the arcade-like model described by Benninghoff (1925) [43], and 13 secondary fibrils that are randomly oriented. The collagen fibril density is defined as a function of depth, and the ratio of primary fibrils to secondary fibrils is 12.16 [39].

The non-fibrillar part is defined as a Neo-Hookean hyperelastic material. The fluid is considered incompressible and water content defined as depth-dependent. Permeability is defined as strain-dependent [39]–[41]. Depending on the type of modelling, other depth-dependent characteristics can be implemented, e.g. fibril volume density, proteoglycan content and damaged fibrils.

Application of models

The FE method will be applied first into a single healthy knee joint geometry as a proof of concept. The next step is to streamline the process in order to apply the method to a test subject cohort of 25 people: 10 healthy knees, 15 OA knees. The method is applied to both the healthy and the OA knees.

The method can be divided into two parts:

1) Implementation of the FE model into the geometry of the patients’ knees and 2) Modelling the effect of insoles and gait modification on cartilage stresses in OA patients

The first part consists of the standard procedures of FE modelling: segmentation, meshing, boundary conditions etc. but in case of OA patients, the material model for articular cartilage can be modified to represent the osteoarthritic cartilage more accurately. This can be accomplished by either modifying material parameters based on literature or by implementing subject-specific data, e.g. collagen fibre orientation or fixed charge density (FCD) distribution from MRIs.

The second part involves gathering gait data from OA patients using lateral wedge insoles, as well as patients undergoing gait modification training. The MS model uses this data to produce the force and moment input, which is then used as an input for the FE models. It has been shown that simply changing the adduction moment in the knee does not necessarily result in reduced stresses in the tibial cartilage [24]. However, our preliminary results [44] indicate the potential reduction in contact forces, which are likely to translate into reduced stresses, therefore alleviating the OA symptoms. This is investigated by simulating the resulting stresses and strains in the tibial cartilage.

Our preliminary results [44] indicate that the toe-in technique causes the greatest decrease in medial forces (Fig. 1), making it the best candidate to reduce stresses in the medial compartment. The relation is not straightforward, as the knee geometry can cause the reduced load to be distributed

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through a smaller contact area, therefore actually increasing the medial stresses [45]. For this reason, FE modelling is implemented in addition to the MS modelling.

Using MS and FE modelling, Ardestani et al. (2014) [46] showed a potential reduction of 25% in medial tibial contact pressures. However, the MS model they used lacked subject-specific muscle and tendon attachment sites, and the knee was modelled as a simple hinge with 1 DOF. In addition, the subject had had a total knee arthroplasty. The present study uses actual subject-specific attachment sites, obtained through a validated shape morphing method, as well as a highly sophisticated cartilage material model, implemented into healthy or osteoarthritic cartilage geometry.

Figure 1. The effect of gait manipulation and lateral wedge insoles on compressive forces in the medial compartment of the knee. Preliminary results. Adapted from Dzialo et al. (2015) [44].

Gait modification

Gait modification aims to non-invasively reduce the loads in the knee, which then could reduce the loads in the medial tibial plateau [46]. This is desirable as the OA is most common in the medial tibial plateau. Because the type of gait modification needs to be designed correctly, MS models are commonly used in the task [16], [47]–[54]. These models are computationally expensive, which is why the present research proposal first investigates a simplified, sliding hinge type of joint model.

There are several strategies in gait modification: walking with toes pointing inward, toes pointing outward, and knees bent inward. The details of investigated techniques are listed in Table 1.

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Table 1. Gait modifications

Type of modification Description

Increase step width

Shifting the centre of pressure (CoP) by increasing the step width. Aims to decrease the moment arm from the knee joint centre to the line of action of the ground reaction force in frontal plane, therefore reducing the knee adduction moment (KAM).

Foot progression angle – Toe in

Increasing the internal rotation of the foot causes the knee joint centre (KJC) to shift medially and closer to CoP. This in turn reduces the moment arm from the KJC to the line of action of the force, reducing KAM.

Foot progression angle – Toe out

Incresing the external rotation of the foot shifts the centre of pressure laterally in the second part of the stance phase. However, in the early part of the stance phase, the CoP shifts medially. This increases the moment arm to the ground reaction force during the early part of stance phase. As the CoP progresses forward under the foot, the increased toe out position serves to reduce the moment arm and hence the KAM during the second peak of the GRF.

Lateral wedge insoles

As the name suggest, lateral wedge insoles are wedged insoles that try to shift the load from medial to lateral tibial plateau by introducing a wedge (typically 5° or 10°) in the lateral side of the foot. The insoles help reduce the adduction moment, which could then reduce the medial contact pressures in tibial cartilage. The efficacy of lateral wedge insoles is debated, showing a reduction of only 5-7% in medial loading [55]–[57]. A recent meta-analysis suggests that there is no evidence to support the claim that wedged insoles reduce contact pressures on the medial tibial plateau [58].

The weakness of those studies is that they either use patients’ self-reported pain levels as an indicator of efficacy, or do not utilise patient-specific gait input. In this study, these weaknesses are removed by implementing a patient-specific MS and FE model in order to investigate if lateral wedge insoles do in fact help reduce stresses in the medial tibial cartilage.

Collaboration

The present proposal is done in joint collaboration with ESR1 (Marco Mannisi, GCU) and ESR9 (Christine Dzialo, AAU). The divide is presented in Table 1.

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Table 2. Division of tasks in present joint collaboration.

ResearcherName and education

Individual contribution Joint contribution

ESR1Marco Mannisi,

M.Sc.

Data gathering (MRI/CT, gait) Incorporating MS data done by

ESR9 into FE model. FE modelling of the effect of

lateral wedge insoles (0°, 5° and 10°) in healthy and OA patients

Segmentation of tissues from MRI

Building the FE model according to a workflow based on the conceptual model (meshing, convergence test, material implementation)

ESR9Christine Dzialo,

M.Sc.

MS modelling based on the gait data by ESR1

Creating a workflow from MS to FE model

Validation of MS models using EOS and dual fluoroscopy imaging

ER1Kimmo Halonen,

Ph.D.

Incorporating the MS data by ESR9 into FE model

FE modelling of the effect of gait modification in healthy and OA patients

Validation

Validation is a vital part of modelling, as a model with no relation to reality serves no purpose. The guidelines for MS model validation [59] divide the process into seven steps:

1) Formulation of research question2) Prototyping of the method and planning the verification and validation3) Verifying the software4) Validation or results by comparing the model to independent experiments/other models5) Sensitivity analysis of used parameters6) Documentation and dissemination of the model and results7) Generate hypotheses that can be tested in experiments

The KNEEMO protocol and this proposal, as well as the research plans of ESR1 and ESR9 form step 1). Regarding step 2), the MS model has already been built and is ready to be applied to FE modelling. Similarly, a FE model of a subject’s knee joint combined with data from gait analysis has been created. Step 3) essentially means checking the models and custom-made programs (e.g. Matlab) for any errors. Step 4) primarily consists of EOS imaging and dual fluoroscopy, which will be used to validate the simulated joint movements. In EOS the knee is imaged simultaneously from lateral and frontal directions, either in unloaded or loaded conditions. EOS is able to capture the whole body if needed to, but its downside is that only static images can be obtained. Dual fluoroscopy, on the other hand, is able to capture the motion of bones in the knee during dynamic

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activity, but its downsides are a relatively high radiation dose and limited field of view. Step 5) is problematic as the number of parameters in a full lower extremity model is very large, which makes a complete sensitivity analysis computationally expensive. Step 6) consists of keeping all data organized and commented as well as publishing the results in peer-reviewed journals and conference proceedings. Finally, for step 7) the effect of insoles and gait modification can be clinically investigated in future studies.

The material models used in FE modelling have been validated in previous in vitro experimental studies [39]–[41], and in some cases, in vivo experiments [60]. Typical in vitro tests include unconfined compression tests [61], [62], and confined compression tests [61], [63]–[65]. Parameters such as the dashpot constant for viscoelastic collagen fibres cannot be obtained experimentally. Those parameters are defined by fitting them against experimental creep and stress-relaxation data. Specifically, many parameters of the material model used in this study have been fitted to the experimental data of DiSilvestro et al. (2001) [39], [40], [65].

Erdemir et al. (2012) [66] list good practises when reporting results obtained with finite element models:

1) Model should be properly identified: its purpose, physiological domain (anatomical principles), mechanical domain (mathematical formulation), structure of interest (knee), the highlights of results and the model’s limitations.

2) Model’s structure should be clearly stated: detailed loading and boundary conditions, primary (results) and secondary (useful for other purposes) output variables, coordinate systems, details of possible subsidiary models that use multiple structural components (i.e. cartilage and bone).

3) Simulation structure: name of the simulation software, version number, solution strategy, numerical algorithms, convergence criteria and post-processing.

Step 1)-3) are acknowledged when reporting the findings in peer-reviewed forums.

Limitations

Being the basis of MS modelling, motion analysis plays a critical role in the project. Marker-based motion analysis is prone to errors caused by the skin moving under the markers [67]. However, the error is minimized by using a large number of markers, which together form the moving segment. As the segment’s position is calculated by determining the centre of mass for a cluster of markers (each with arbitrary individual mass), the weight of a single misaligned marker is minimized.

In the FE modelling part, some of the parameters used in soft tissue material models are not yet obtainable from experimental studies. They are therefore fitted to experimental data by an optimization process. An example of these parameters is the non-linear fibril modulus in FRPVE meniscus model [68]. Segmentation introduces the partial volume effect, which might over- or underestimate thicknesses of tissues. This limitation is most prominent in areas where the tissue is thin (i.e. relative to pixel size).

Section 3. Project outline

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A.) Project Objectives

Create a subject-specific FE model of the subject’s knee in order to simulate the stresses and strains the articular cartilage experiences.

Implement muscle forces obtained from MS analysis into the FE model. Using the FE model, study the effect of gait modification on cartilage stresses in OA

patients during gait.

B.) Specific goals

Understand the structure and function of the knee joint, as well as its constituent anatomy and complex function in healthy and OA states.

Understand the process of MS analysis, specifically the use of Anybody Modeling System software, and how to input the MS data into the FE model.

Produce two scientific articles, published in respected peer-reviewed journals. Gain experience working abroad during secondments, both in other research institutes and

companies. Understand the ethical principles of scientific research, effective collaboration,

communication and dissemination of results.

C.) Key Methods

Literature Review of scientific publications related to project objectives outlined above. Collaboration with two KNEEMO Early Stage Researchers from GCU (ESR1) and AAU

(ESR9) to establish IRB approved experimental procedures to obtain healthy knee subject-specific data. This includes but is not limited to: MR images of the leg, anthropomorphic measurements, knee laxity, range of motion, full body motion capture kinematics and ground reaction forces with EMG, as well as dual fluoroscopy or dynamic MRI data of knee joint motion.

Using finite element modelling software Abaqus (Dassault Systèmes, Leuven, Belgium) to create a subject-specific FE model from the MR images of the subject’s knee joint. The tissues will be segmented using Mimics (Materialise, Leuven, Belgium) segmentation software. The model uses a custom-made, validated material model for soft tissues and is written in Fortran code. All other data analyses, preparation steps etc. are done in Matlab (Massachusetts, US).

C.) Data collection

Data collection is conducted by ESR1 in Glasgow Caledonian University (GCU). The following data will be collected from a total of 25 test subjects (10 healthy, 15 OA):

MR images of the lower extremity, motion capture data from various activities (e.g. gait, sit-to-stand), joint laxity data, plantar pressure readings with and without custom foot orthotics laterally wedged at 0, 5 and 10 degrees.

Three types of MRI acquisitions will be performed on the subjects’ lower extremity: 1) detailed knee with various imaging sequences (following the osteoarthritis initiative

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protocol) 2) full lower limb acquisition consisting of three images that are then combined in post-processing 3) 90° flexion image of the knee (for the sliding hinge MS model)

The gait data collection uses 33 retroreflective markers, based on anatomic landmarks, plus 6 markers for the trunk. The movement of segments, defined by the clusters of skin markers, is then determined in Visual 3D commercial software.

Laxity tests in frontal and sagittal planes will be performed on the subjects’ knees. In sagittal plane, a moment of 7.7 Nm is applied in valgus/varus direction, and the angular displacement is recorded. In addition, a Lachman test to determine anterior-posterior knee laxity.

EMG data is collected from the following muscles: rectus femoris, vastus lateralis, vastus medialis, semitendinosus, biceps femoris, tibialis anterior, lateral gastrocnemius and medial gastrocnemius.

Other gathered data include: maximal voluntary isometric contraction for the muscles, activity of daily living, a static standing trial to calibrate the biomechanical model and record the centre of pressure for each subject, determining the static knee alignment, ‘time up and go’ trial, stair ascent/descent trials.

E.) Potential significance and applications

A substantial reduction in medial cartilage stresses would indicate that the non-invasive methods investigated here can help OA patients.

These methods could provide a low-cost treatment option for OA symptoms.

F.) Timetable

Table 3. Timetable.

Event2015 2016 2017

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4Literature Review XResearch proposal XCourses X X X XExternal Co-operation1st Secondment (GCU) X X2nd Secondment (XST) X XObjectivesConceptual MS+FE model X XSegmentation of 5 healthy + 5 OA patients

X X

MS+FE model of patients with gait modification

X X

Segmentation of 5 more healthy + 10 OA patients

X X

ConferencesWrite abstract/proceedings X XAttend conference X XJournal ArticlesWrite paper X X X XSubmit paper I II

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H.) Tentative titles on papers

Table 4. Titles of planned scientific publications.

Journal Article 1 Journal of Biomechanics

Title“Changes in cartilage stresses following gait modification in OA patients with knee injuries – a

combined musculoskeletal and finite element analysis”Co-authors Christine Dzialo (ESR9), Marco Mannisi (ESR1), Mark de Zee, Michael Skipper Andersen,

Journal Article II Annals of Biomedical EngineeringTitle “Effect of gait modification on the stresses in the knee cartilage in hypermobile OA patients.”

Co-authors Christine Dzialo (ESR9), Marco Mannisi (ESR1), Mark de Zee, Michael Skipper Andersen

Section 4. Training plan

Approximately 20% of the working time will be allocated to training. Training includes courses and secondments abroad.

A.) Secondments

Two secondments are included in the 2-year period:

30 days at the Glasgow Caledonian University (GCU) in Glasgow, Scotland 45 days at Xsens (XST) company in Netherlands

B.) Plan for courses

Courses Place/Organized by Planned venue Status

Workshop on KOA biomechanics MUN Munster, Germany -Workshop on advanced computational modelling of the knee joint structure AAU Aalborg, Denmark -

Workshop on non-pharmacological interventions for KOA SDU Odense, Denmark -

KNEEMO Concluding conference GCU Conincide with major conference, TBC -

Section 5. Plan for fulfilment of knowledge dissemination

The outcomes of this research will be published in respected peer-reviewed journals such as Journal of Biomechanics, Journal of Biomechanical Engineering or Computer Methods in Medicine and Biomechanics and Biomedical Engineering.Abstracts will be disseminated through conference participation. In addition, periodic progress reports will be released through the KNEEMO network.

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Section 6. References

[1] R. C. Lawrence, D. T. Felson, C. G. Helmick, L. M. Arnold, H. Choi, R. A. Deyo, S. Gabriel, R. Hirsch, M. C. Hochberg, G. G. Hunder, J. M. Jordan, J. N. Katz, H. M. Kremers, and F. Wolfe, “Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: Part II,” Arthritis Rheum., vol. 58, no. 1, pp. 26–35, 2008.

[2] A. D. Woolf and B. Pfleger, “Burden of major musculoskeletal conditions,” Bull. World Health Organ., vol. 81, no. 9, pp. 646–656, 2003.

[3] J. A. Buckwalter, H. J. Mankin, and A. J. Grodzinsky, “Articular Cartilage and Osteoarthritis,” AAOS Instr. Course Lect., vol. 54, pp. 465–480, 2005.

[4] L. Sharma, D. Kapoor, and S. N. Issa, “Epidemiology of osteoarthritis: An update,” Curr. Rheumatol. Rep., vol. 8, no. 1, pp. 7–15, 2006.

[5] N. Arden and M. Nevitt, “Osteoarthritis: Epidemiology,” Best Pract. Res. Clin. Rheumatol., vol. 20, no. 1, pp. 3–25, 2006.

[6] J. W. J. Bijlsma, F. Berenbaum, and F. P. J. G. Lafeber, “Osteoarthritis: An update with relevance for clinical practice,” Lancet, vol. 377, no. 9783, pp. 2115–2126, 2011.

[7] L. S. Lohmander, M. Gerhardsson de Verdier, J. Rollof, P. M. Nilsson, and G. Engstrom, “Incidence of severe knee and hip osteoarthritis in relation to different measures of body mass: a population-based prospective cohort study,” Ann. Rheum. Dis., vol. 68, no. 4, pp. 490–496, 2009.

[8] B. Barenius, S. Ponzer, A. Shalabi, R. Bujak, L. Norlen, and K. Eriksson, “Increased Risk of Osteoarthritis After Anterior Cruciate Ligament Reconstruction: A 14-Year Follow-up Study of a Randomized Controlled Trial,” Am. J. Sports Med., vol. 42, no. 5, pp. 1049–1057, 2014.

[9] J.-P. Pelletier, J.-P. Raynauld, M.-J. Berthiaume, F. Abram, D. Choquette, B. Haraoui, J. F. Beary, G. a Cline, J. M. Meyer, and J. Martel-Pelletier, “Risk factors associated with the loss of cartilage volume on weight-bearing areas in knee osteoarthritis patients assessed by quantitative magnetic resonance imaging: a longitudinal study.,” Arthritis Res. Ther., vol. 9, no. 4, p. R74, 2007.

[10] A. Erdemir, S. McLean, W. Herzog, and A. J. van den Bogert, “Model-based estimation of muscle forces exerted during movements,” Clin. Biomech., vol. 22, no. 2, pp. 131–154, 2007.

[11] M. Kozanek, A. Hosseini, F. Liu, S. K. Van de Velde, T. J. Gill, H. E. Rubash, and G. Li, “Tibiofemoral kinematics and condylar motion during the stance phase of gait,” J. Biomech., vol. 42, no. 12, pp. 1877–1884, 2009.

[12] J.-S. Li, T.-Y. Tsai, S. Wang, P. Li, Y.-M. Kwon, A. Freiberg, H. E. Rubash, and G. Li, “Prediction of In Vivo Knee Joint Kinematics Using a Combined Dual Fluoroscopy Imaging and Statistical Shape Modeling Technique,” J. Biomech. Eng., vol. 136, no. 12, p. 124503, 2014.

[13] C. L. Vaughan, B. L. Davis, and J. C. O’Connor, Dynamics of Human Gait. 1999.

[14] M. G. Pandy, “Computer modeling and simulation of human movement,” Kinesiology, vol. 3, no. 1, pp. 245–273, 2001.

[15] M. Damsgaard, J. Rasmussen, S. T. Christensen, E. Surma, and M. de Zee, “Analysis of musculoskeletal systems in the AnyBody Modeling System,” Simul. Model. Pract. Theory, vol. 14, no. 8, pp. 1100–1111, 2006.

[16] B. J. Fregly, J. A. Reinbolt, K. L. Rooney, K. H. Mitchell, and T. L. Chmielewski, “Design of patient-specific gait modifications for knee osteoarthritis rehabilitation.,” IEEE Trans. Biomed. Eng., vol. 54, no. 9, pp. 1687–95, 2007.

[17] M. D. Klein Horsman, H. F. J. M. Koopman, F. C. T. van der Helm, L. P. Prosé, and H. E. J. Veeger, “Morphological muscle and joint parameters for musculoskeletal modelling of the lower extremity,” Clin. Biomech., vol. 22, no. 2, pp. 239–247, 2007.

[18] M. A. Marra, V. Vanheule, J. Rasmussen, N. J. J. Verdonschot, M. S. Andersen, R. Fluit, B. H. F. J. M. Koopman, J. Rasmussen, N. J. J. Verdonschot, and M. S. Andersen, “A Subject-Specific Musculoskeletal Modeling Framework to Predict in Vivo Mechanics of Total Knee Arthroplasty.,” J. Biomech. Eng., vol. 137, no. 2, p. 020904, 2015.

[19] S. Duprey, L. Chèze, and R. Dumas, “A constraint-based approach to model the lower limb: preliminary results for running motions,” Comput. Methods Biomech. Biomed. Engin., vol. 12, no. March 2015, pp. 105–106, 2009.

[20] J. D. Feikes, J. J. O'Connor, and A. B. Zavatsky, “A constraint-based approach to modelling the mobility of the human knee joint,” J. Biomech., vol. 36, no. 1, pp. 125–129, 2003.

[21] F. Moissenet, L. Chèze, and R. Dumas, “A 3D lower limb musculoskeletal model for simultaneous estimation of musculo-tendon, joint contact, ligament and bone forces during gait,” J. Biomech., vol. 47, no. 1, pp. 50–58, 2014.

[22] F. Moissenet, L. Chèze, and R. Dumas, “Anatomical kinematic constraints: Consequences on musculo-tendon forces and joint reactions,” Multibody Syst. Dyn., vol. 28, no. 1–2, pp. 125–141, 2012.

[23] D. R. Wilson, J. D. Feikes, and J. J. O'Connor, “Ligaments and articular contact guide passive knee flexion,” J. Biomech., vol. 31, no. 12, pp. 1127–1136, 1998.

Page | 14

[24] J. P. Walter, D. D. D’Lima, C. W. Colwell, and B. J. Fregly, “Decreased Knee Adduction Moment Does Not Guarantee Decreased Medial Contact Force During Gait,” J. Orthop. Res., vol. 28, no. 10, pp. 1348–1354, 2010.

[25] S. L. Delp, J. P. Loan, M. G. Hoy, F. E. Zajac, E. L. Topp, J. M. Rosen, and A. N. D. J. M. Rosen, “An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures.,” IEEE Trans. Biomed. Eng., vol. 37, no. 8, pp. 757–67, 1990.

[26] K. R. S. Holzbaur, W. M. Murray, and S. L. Delp, “A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control,” Ann. Biomed. Eng., vol. 33, no. 6, pp. 829–840, 2005.

[27] S. R. Ward, C. M. Eng, L. H. Smallwood, and R. L. Lieber, “Are Current Measurements of Lower Extremity Muscle Architecture Accurate?,” Clin. Orthop. Relat. Res., vol. 467, no. 4, pp. 1074–1082, 2009.

[28] T. A. Correa, R. Baker, H. K. Graham, and M. G. Pandy, “Accuracy of generic musculoskeletal models in predicting the functional roles of muscles in human gait.,” J. Biomech., vol. 44, no. 11, pp. 2096–105, 2011.

[29] D. W. Wagner, V. Stepanyan, J. M. Shippen, M. S. DeMers, R. S. Gibbons, B. J. Andrews, G. H. Creasey, and G. S. Beaupre, “Consistency Among Musculoskeletal Models: Caveat Utilitor,” Ann. Biomed. Eng., vol. 41, no. 8, pp. 1787–1799, 2013.

[30] L. Scheys, A. Van Campenhout, A. Spaepen, P. Suetens, and I. Jonkers, “Personalized MR-based musculoskeletal models compared to rescaled generic models in the presence of increased femoral anteversion: Effect on hip moment arm lengths,” Gait Posture, vol. 28, no. 3, pp. 358–365, 2008.

[31] M. E. Lund, M. S. Andersen, M. de Zee, and J. Rasmussen, “Scaling of musculoskeletal models from static and dynamic trials,” Int. Biomech., vol. 2, no. 1, pp. 1–11, 2015.

[32] M. S. Andersen, M. Damsgaard, B. MacWilliams, and J. Rasmussen, “A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems,” Comput. Methods Biomech. Biomed. Engin., vol. 13, no. 2, pp. 171–183, 2010.

[33] P. Pellikaan, M. M. van der Krogt, V. Carbone, L. Vigneron, J. Van Deun, N. Verdonschot, and H. F. J. M. Koopman, “Evaluation of a morphing based method to estimate muscle attachment sites of the lower extremity,” J. Biomech., vol. 47, no. 5, pp. 1144–1150, 2014.

[34] J. A. Reinbolt, J. F. Schutte, B. J. Fregly, B. Il Koh, R. T. Haftka, A. D. George, and K. H. Mitchell, “Determination of patient-specific multi-joint kinematic models through two-level optimization,” J. Biomech., vol. 38, no. 3, pp. 621–626, 2005.

[35] S. C. Brenner and R. Scott, NoThe Mathematical Theory of Finite Element Method. United States: Springer Science+Business Media, 2008.

[36] J. Tervo and M. Vauhkonen, Elementtimenetelmän perusteita. Kuopio: University of EAstern Finland, 2000.

[37] M. S. Gockenbach, Understanding and Implementing the Finite Element Method. Philadelphia, PA, US: Society for Industrial and Applied Mathematics, 2006.

[38] V. C. Mow and X. E. Guo, “Mechano-Electrochemical Properties Of Articular Cartilage: Their Inhomogeneities and Anisotropies,” Annu. Rev. Biomed. Eng., vol. 4, no. 1, pp. 175–209, 2002.

[39] W. Wilson, C. C. van Donkelaar, B. van Rietbergen, K. Ito, and R. Huiskes, “Stresses in the local collagen network of articular cartilage: a poroviscoelastic fibril-reinforced finite element study,” J. Biomech., vol. 37, no. 3, pp. 357–366, 2004.

[40] W. Wilson, C. C. van Donkelaar, B. van Rietbergen, K. Ito, and R. Huiskes, “Erratum to ‘Stresses in the local collagen network of articular cartilage: a poroviscoelastic fibril-reinforced finite element study’ [Journal of Biomechanics 37 (2004) 357–366] and “A fibril-reinforced poroviscoelastic swelling model for articular cartil,” J. Biomech., vol. 38, no. 10, pp. 2138–2140, 2005.

[41] P. Julkunen, P. Kiviranta, W. Wilson, J. S. Jurvelin, and R. K. Korhonen, “Characterization of articular cartilage by combining microscopic analysis with a fibril-reinforced finite-element model.,” J. Biomech., vol. 40, no. 8, pp. 1862–70, 2007.

[42] P. Bullough and J. Goodfellow, “The significance of the fine structure of articular cartilage,” J. bone Jt. surgery., vol. 50, no. 4, pp. 852–857, 1968.

[43] A. Benninghoff, “Form und Bau der Gelenkknorpel in ihren Beziehungen zur Funktion,” Z. Anat. Entwicklungsgesch., vol. 76, no. 1–3, pp. 43–63, 1925.

[44] C. M. Dzialo, M. Mannisi, M. A. Marra, R. Richards, S. Telfer, J. Woodburn, M. de Zee, and M. S. Andersen, “A subject-specific analysis framework to investigate gait alterations,” in OARSI 2016 World Congres on Osteoarthritis, Amsterdam, The Netherlands, 2015, pp. 1–2.

[45] D. D. D’Lima, N. Steklov, B. J. Fregly, S. A. Banks, and C. W. Colwell, “In vivo contact stresses during activities of daily living after knee arthroplasty,” J. Orthop. Res., vol. 26, no. 12, pp. 1549–1555, 2008.

[46] M. M. Ardestani, M. Moazen, and Z. Jin, “Gait modification and optimization using neural network-genetic algorithm approach: Application to knee rehabilitation,” Expert Syst. Appl., vol. 41, no. 16, pp. 7466–7477, 2014.

Page | 15

[47] M. Ackermann and A. J. van den Bogert, “Optimality principles for model-based prediction of human gait,” J. Biomech., vol. 43, no. 6, pp. 1055–1060, 2010.

[48] F. C. Anderson and M. G. Pandy, “Dynamic optimization of human walking.,” J. Biomech. Eng., vol. 123, no. 5, pp. 381–390, 2001.

[49] J. A. Barrios, K. M. Crossley, and I. S. Davis, “Gait retraining to reduce the knee adduction moment through real-time visual feedback of dynamic knee alignment,” J. Biomech., vol. 43, no. 11, pp. 2208–2213, 2010.

[50] J. A. Barrios and I. S. Davis, “A Gait Modification to Reduce the External Adduction Moment at the Knee: A Case Study,” in 31st annual meeting of the American society of Biomechanics, 2007, pp. 1–2.

[51] B. J. Fregly, D. D. D’Lima, and C. W. Colwell, “Effective Gait Patterns for Offloading the Medial Compartment of the Knee,” J. Orthop. Res., vol. 27, no. 8, pp. 1016–1021, 2009.

[52] M. A. Hunt, T. B. Birmingham, D. Bryant, I. Jones, J. R. Giffin, T. R. Jenkyn, and A. A. Vandervoort, “Lateral trunk lean explains variation in dynamic knee joint load in patients with medial compartment knee osteoarthritis,” Osteoarthr. Cartil., vol. 16, no. 5, pp. 591–599, 2008.

[53] A. Mündermann, J. L. Asay, L. Mündermann, and T. P. Andriacchi, “Implications of increased medio-lateral trunk sway for ambulatory mechanics,” J. Biomech., vol. 41, no. 1, pp. 165–170, 2008.

[54] J. Willson, M. R. Torry, M. J. Decker, T. Kernozek, and J. R. Steadman, “Effects of walking poles on lower extremity gait mechanics.,” Med. Sci. Sports Exerc., vol. 33, no. 1, pp. 142–7, 2001.

[55] W. Kakihana, M. Akai, K. Nakazawa, T. Takashima, K. Naito, and S. Torii, “Effects of laterally wedged insoles on knee and subtalar joint moments,” Arch. Phys. Med. Rehabil., vol. 86, no. 7, pp. 1465–1471, 2005.

[56] R. S. Hinman, C. Payne, B. R. Metcalf, T. V. Wrigley, and K. L. Bennell, “Lateral wedges in knee osteoarthritis: What are their immediate clinical and biomechanical effects and can these predict a three-month clinical outcome?,” Arthritis Care Res., vol. 59, no. 3, pp. 408–415, 2008.

[57] R. J. Butler, K. I. Minick, R. Ferber, and F. Underwood, “Gait mechanics after ACL reconstruction: implications for the early onset of knee osteoarthritis,” Br. J. Sports Med., vol. 43, no. 5, pp. 366–370, 2009.

[58] M. J. Parkes, N. Maricar, M. Lunt, M. P. LaValley, R. K. Jones, N. a Segal, K. Takahashi-Narita, and D. T. Felson, “Lateral wedge insoles as a conservative treatment for pain in patients with medial knee osteoarthritis: a meta-analysis.,” JAMA, vol. 310, no. 7, pp. 722–30, 2013.

[59] J. L. Hicks, T. K. Uchida, A. Seth, A. Rajagopal, and S. Delp, “Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of human movement,” J. Biomech. Eng., vol. 137, no. February, 2015.

[60] K. S. Halonen, M. E. Mononen, J. S. Jurvelin, J. Töyräs, J. Salo, and R. K. Korhonen, “Deformation of articular cartilage during static loading of a knee joint – Experimental and finite element analysis,” J. Biomech., vol. 47, no. 10, pp. 2467–2474, 2014.

[61] M. Fortin, J. Soulhat, a Shirazi-Adl, E. B. Hunziker, and M. D. Buschmann, “Unconfined compression of articular cartilage: nonlinear behavior and comparison with a fibril-reinforced biphasic model.,” J. Biomech. Eng., vol. 122, no. 2, pp. 189–195, 2000.

[62] R. K. Korhonen, M. S. Laasanen, J. Töyräs, J. Rieppo, J. Hirvonen, H. J. Helminen, and J. S. Jurvelin, “Comparison of the equilibrium response of articular cartilage in unconfined compression, confined compression and indentation,” J. Biomech., vol. 35, no. 7, pp. 903–909, 2002.

[63] M. a. Soltz and G. a. Ateshian, “Experimental verification and theoretical prediction of cartilage interstitial fluid pressurization at an impermeable contact interface in confined compression,” Journal of Biomechanics, vol. 31, no. 10. pp. 927–934, 1998.

[64] S. Treppo, H. Koepp, E. C. Quan, A. A. Cole, K. E. Kuettner, and A. J. Grodzinsky, “Comparison of biomechanical and biochemical properties of cartilage from human knee and ankle pairs,” J. Orthop. Res., vol. 18, no. 5, pp. 739–748, 2000.

[65] M. R. DiSilvestro, Q. Zhu, M. Wong, J. S. Jurvelin, and J. K. Suh, “Biphasic poroviscoelastic simulation of the unconfined compression of articular cartilage: I--Simultaneous prediction of reaction force and lateral displacement.,” J. Biomech. Eng., vol. 123, no. 2, pp. 191–197, 2001.

[66] A. Erdemir, T. M. Guess, J. Halloran, S. C. Tadepalli, and T. M. Morrison, “Considerations for reporting finite element analysis studies in biomechanics,” J. Biomech., vol. 45, no. 4, pp. 625–633, 2012.

[67] C. O. Dyrby and T. P. Andriacchi, “Secondary motions of the knee during weight bearing and non-weight bearing activities,” J. Orthop. Res., vol. 22, no. 4, pp. 794–800, 2004.

[68] L. P. Li and K. B. Gu, “Reconsideration on the use of elastic models to predict the instantaneous load response of the knee joint,” Proc. Inst. Mech. Eng. Part H J. Eng. Med., vol. 225, no. 9, pp. 888–896, 2011.

Page | 16