individualising kinematic modelling in the gait analysis ... · ganganalyse en het bestuderen van...

Academic year 2013 - 2014

Individualising kinematic modelling in the gait

analysis of osteoarthritic knee patients Integration of 3D bone morphological segmentation methods with classic

kinematic modelling techniques

Heleen DAELEWIJN

and

Levi HOSTE

Promotors: F. Plasschaert MD, Phd and J. Victor MD, Phd

Co-promotors: M. Forward, Phd and C. Van Der Straeten MD, Phd

Master’s thesis submitted in the 2nd Master year in fulfillment of the requirements for the

degree of

MASTER OF MEDICINE IN DE GENEESKUNDE

Acknowledgments

This master’s thesis would not have been possible without the support of many people. First

and foremost, we would like to express our sincere gratitude to our promoters and co-

promotors, Frank Plasschaert (MD, Phd), Jan Victor (MD, Phd), Malcolm Forward (Phd) and

Catherine Van Der Straeten (MD, Phd), for the great opportunity to be part of this research and

use it for our thesis.

Our special thanks goes out to Prof. Malcolm Forward for his guidance, patience, motivation

and knowledge throughout every step of this thesis for the past two years. His valuable and

constructive suggestions during the planning and development of this research work and his

willingness to give his time so generously has been very much appreciated.

We would also like to thank Prof. Catherine Van Der Straeten MD in special for the help in

recruiting the patients, keeping our progress on schedule and giving great advice in the further

development of this thesis.

We are particularly grateful for the assistance given by Prof. Jan Victor MD and his research

work on the subject of knee alignment. His previous publications have been a great base for

our own research on this matter.

Furthermore, we highly appreciate the assistance provided by all the staff members of the

Departments of Orthopaedics and Traumatology, Radiology and especially those of the

Cerebral Palsy Reference Centre at the University Hospital of Ghent. In special, we would like

to thank Ellen De Dobbelaere for her training and assistance in the gait lab.

At last, a personal word of gratitude goes out to Emma Dejonghe and Henri Himpe, both close

to us, for the great support and encouragement throughout our studies.

Table of contents

ABSTRACT (ENGLISH) ..................................................................................................................................... 1

ABSTRACT (NEDERLANDS) ........................................................................................................................... 2

INTRODUCTION ................................................................................................................................................ 3

RESEARCH PROBLEM DESCRIPTION ..................................................................................................................... 3 THESIS OVERVIEW ............................................................................................................................................... 6

1. BACKGROUND .......................................................................................................................................... 7

1.1 KNEE OSTEOARTHRITIS .......................................................................................................................... 7 1.1.1 Pathogenesis of osteoarthritis .......................................................................................................... 7 1.1.2 Epidemiology .................................................................................................................................... 8 1.1.3 Treatment of osteoarthritis ............................................................................................................... 9

1.2 GAIT ANALYSIS .................................................................................................................................... 11 1.2.1 Definitions ...................................................................................................................................... 11

1.2.1.1 Locomotion and gait .............................................................................................................................. 11 1.2.1.2 Gait analysis ........................................................................................................................................... 11

1.2.2 Difficulties and restrictions ............................................................................................................ 12 1.3 GAIT IN PATIENTS WITH KNEE OSTEOARTHRITIS .................................................................................. 14

1.3.1 Common findings in knee OA patients ............................................................................................ 14 1.3.2 Common findings after total knee arthroplasty .............................................................................. 15

2. LITERATURE REVIEW ......................................................................................................................... 17

2.1 FINDINGS ............................................................................................................................................. 17 2.2 CONCLUSIONS AND RELEVANCE FOR EXPERIMENTAL WORK ................................................................ 18

3. EXPERIMENTAL EVALUATION ......................................................................................................... 19

3.1 MATERIAL AND METHODS .................................................................................................................... 19 3.1.1 Study design and selection .............................................................................................................. 19 3.1.2 Study procedures and data collection ............................................................................................. 19

3.1.2.1 Gait assessment ...................................................................................................................................... 20 3.1.2.1.1 Introduction and preparation .......................................................................................................... 20 3.1.2.1.2 Plates with extra markers ............................................................................................................... 20 3.1.2.1.3 Gait assessment.............................................................................................................................. 23 3.1.2.1.4 Gait analysis .................................................................................................................................. 23

3.1.2.2 Medical imaging .................................................................................................................................... 24 3.1.2.2.1 Radiology ...................................................................................................................................... 24 3.1.2.2.2 Segmenting of CT slices in Mimics ............................................................................................... 24 3.1.2.2.3 Readings in 3-matic ....................................................................................................................... 25

3.1.2.3 CT-based subject-specific gait model .................................................................................................... 25 3.1.2.4 Intermezzo: Assessing the reliability of the data collection ................................................................... 25

3.1.3 Statistical analysis .......................................................................................................................... 25 3.2 RESULTS .............................................................................................................................................. 27

3.2.1 Study subjects.................................................................................................................................. 27 3.2.2 Comparison of the generic model with a replica ............................................................................ 27 3.2.3 Comparison of the generic model with a subject-specific CT-based model ................................... 29

3.2.3.1 Comparison of the kinematics ................................................................................................................ 29 3.2.3.2 Comparison of the positions of anatomical landmarks ........................................................................... 34

3.2.3.2.1 Hip joint centres............................................................................................................................. 34 3.2.3.2.2 Knee joint centres .......................................................................................................................... 37

3.2.4 Comparison of the pre and post-surgical assessments ................................................................... 37 3.2.4.1 Comparison of the temporal gait parameters .......................................................................................... 38 3.2.4.2 Comparison of the kinematics ................................................................................................................ 39

3.2.4.2.1 PIG ................................................................................................................................................ 39 3.2.4.2.2 CTM .............................................................................................................................................. 39

3.3 DISCUSSION ......................................................................................................................................... 40

3.3.1 Methodological considerations ....................................................................................................... 40 3.3.2 Health economics ............................................................................................................................ 41 3.3.3 Radiation dose ................................................................................................................................ 41 3.3.4 Conclusions and future work .......................................................................................................... 42

LIST OF FIGURES ............................................................................................................................................ 45

LIST OF TABLES .............................................................................................................................................. 45

REFERENCES ................................................................................................................................................... 47

APPENDICES ..................................................................................................................................................... 51

APPENDIX 1 : KELLGREN-LAWRENCE GRADING SCALE .................................................................................... 51 APPENDIX 2 : MIMICS AND 3-MATIC MANUAL ................................................................................................... 51 APPENDIX 3 : DIMENSIONS OF THE TIBIAL PLATE .............................................................................................. 55 APPENDIX 4 : DIMENSIONS OF THE SACRAL PLATE ............................................................................................ 56 APPENDIX 5 : ASSESSMENT OF RELIABILITY OF GAIT LAB DATA ........................................................................ 57 APPENDIX 6 : KINEMATIC GRAPHS COMPARING THE PLUG-IN-GAIT WITH THE REPLICA MODEL........................ 61 APPENDIX 7 : SUMMARY KINEMATIC VARIABLES AND THEIR COMPARISON BETWEEN THE GENERIC PIG-MODEL

AND THE REPLICA MODEL .................................................................................................................................. 67 APPENDIX 8 : KINEMATIC GRAPHS COMPARING THE PLUG-IN-GAIT WITH THE CT-MODEL ............................... 68 APPENDIX 9 : SUMMARY KINEMATIC VARIABLES AND THE COMPARISON BETWEEN THE PIG MODEL AND THE

CTM: SYSTEMATIC DIFFERENCES BY T-TESTING .............................................................................................. 71 APPENDIX 10 : SUMMARY KINEMATIC VARIABLES AND THE COMPARISON BETWEEN THE PIG MODEL AND THE

CTM: CORRELATION AND AGREEMENT TESTING .............................................................................................. 72 APPENDIX 11 : TEMPORAL PARAMETERS PRE- AND POST-SURGERY .................................................................. 73 APPENDIX 12 : SUMMARY KINEMATIC VARIABLES AND THE COMPARISON BETWEEN PRE- AND POST-SURGICALLY:

SYSTEMATIC DIFFERENCES BY T-TESTING ........................................................................................................ 74

Abstract (English)

1

Abstract (English)

Among older adults, osteoarthritis (OA) is the most common cause of walking-related

disability and the main source for total knee arthroplasty (TKA) in Western countries. Over the

past decade, TKA has developed to a complete functional and pain free result therapy.

Nevertheless, a large variation in the outcome of total knee arthroplasty procedures is still

observed. Gait analysis and the study of patient-specific kinematics and kinetics could provide

an answer to this puzzle.

Most of the software packages in gait analysis rely on a rescaled generic model of the lower

extremity. Nevertheless, this procedure can induce major errors, mainly due to (1) errors

associated with marker placement on anatomical landmarks and (2) imprecision in the

description of joint systems. This is especially so because of particular problems around the

pelvis and knee and in obese patients.

In this pilot study, where three elderly TKA patients were included, an alternative, hybrid

model was built based on information of computer tomography (CT) images of the patients.

By running this adapted, subject-specific model, joint centres, kinetics and kinematics were

redefined. We hypothesized that the kinematics calculated with the CT-model would be more

accurate and individually correct than those based on the generic Plug-in-Gait (PIG) model.

Three patients, of whom two were clinically obese, underwent a complete gait assessment, both

before and after their TKA. Major kinematic differences were found between the generic and

the subject-specific model, especially when obesity was present. Specifically hip (sagittal and

transverse) and knee (sagittal ROM, frontal and transverse) kinematics were clearly different

between the models. In the obese, pelvic differences stood out. Poor correlations were found

between the models, but they differed individually. The positions of the CT-based and generic

hip and knee joint centres diverged on a surprisingly large scale. The CT-based hip centres

were found not only different, but also more accurate according to empirical data provided by

Bell et al. Post-surgically, walking speed drastically increased in the pain free patients. In these

patients also sagittal hip and knee ROM exhibited a clear increase.

In conclusion, it was found that a rescaled, generic gait model is incapable of accurately

describing kinematic patterns in elderly knee patients. Especially when obesity is present, the

differences between the models mount up. Medical imaging-based models could provide an

answer as particularly hip and knee kinematics improve in accuracy.

Abstract (Nederlands)

2

Abstract (Nederlands) Osteoartritis is één van de belangrijkste oorzaken van problemen bij het wandelen bij ouderen.

Het is in de Westerse wereld bovendien de hoofdreden voor een totale knie prothese (TKP).

De afgelopen tien jaar is TKP geëvolueerd naar een compleet functionele therapie met vaak

een pijnvrij resultaat. Toch is er nog steeds een grote variatie in de uitkomsten en tevredenheid

van TKP. Ganganalyse en het bestuderen van patiëntspecifieke kinematica en kinetica zouden

een oplossing kunnen bieden voor deze problematiek.

Heden zijn de meeste softwarepakketten bij ganganalyses gebaseerd op een geschaald

generisch model van de onderste ledematen. Deze procedure leidt tot grote fouten, vooral toe

te wijzen aan (1) moeilijkheden bij het plaatsen van markers op anatomische referentiepunten

en (2) onnauwkeurigheid bij het bepalen van de gewrichten. Deze onnauwkeurigheden komen

des te meer tot uiting ter hoogte van het bekken en de knie, vooral bij obese patiënten.

In deze pilootstudie, waar drie oudere TKP patiënten werden geïncludeerd, werd een

alternatief, hybride model ontwikkeld op basis van computertomografie (CT) beelden. Via dit

aangepast, patiëntspecifiek model werden de kinetica en kinematica herbepaald. Er werd

gesteld dat de kinematica berekend via het CT-model meer accuraat en individueel correct

zouden zijn dan de kinematica gebaseerd op het generische Plug-in-Gait (PIG) model.

Drie patiënten, waarvan twee obees, ondergingen een volledige ganganalyse, zowel vóór als

na hun TKP. Er werden grote kinematische verschillen gevonden tussen het generische en het

patiëntspecifieke model, vooral bij de obese patiënten. Ter hoogte van de heup (sagittale en

transversale kinematica) en de knie (sagittale ROM, frontale en transversale kinematica) waren

duidelijke verschillen te noteren tussen de modellen. Bij de obese patiënten werden bovendien

belangrijke verschillen ter hoogte van het bekken opgemerkt. De twee modellen waren slechts

zwak gecorreleerd, al was dit individueel sterk verschillend. De posities van de CT- en

generisch bepaalde heup- en kniegewrichtcentra verschilden sterk, waarbij deze eerste

correcter bleken, vergeleken met empirische data. Na de operatie nam de wandelsnelheid toe

in de pijnvrije patiënten. Bovendien was er in toename in sagittale heup- en kniekinematica.

In conclusie werd gesteld dat het geschaalde generische gangmodel niet in staat is om op een

accurate manier kinematische patronen te beschrijven bij oudere kniepatiënten. Vooral in geval

van obesitas, stapelen de verschillen met een patiëntspecifiek model zich op. Modellen

gebaseerd op medische beeldvorming zouden een oplossing kunnen bieden aangezien heup- en

kniekinematica er meer accuraat zijn.

Introduction: Research problem description

3

Introduction

Research problem description

Among older adults, osteoarthritis (OA) is the most common cause of walking-related

disability and the main source for knee arthroplasty in Western countries [1, 2]. Both the

prevalence and incidence of OA are increasing rapidly [2, 3] and they both rise with age [1, 4,

5]. Symptomatic OA of the knee occurs in 7 to 17% of adults 45 year and older, but prevalence

goes up to one third in people who are older than 75. From 50 years onwards prevalence is

higher in woman [1]. Radiographic evaluation reveals up to half of the elderly with OA [2]. A

higher incidence was found in the rural population and also (and mainly) obesity was shown to

be a risk factor [1-3, 5].

Several classes of medications and treatment options have been used to relieve pain due to

progressing OA and to preserve or restore knee function. Where the use of non-operative

treatments is recommended in the majority of cases, surgery, such as total knee arthroplasty

(TKA), carries a strong recommendation in advanced OA [4, 5].

TKA can be regarded as a great success and many patients are much better after surgical

treatment. Both quality of life and functional parameters improve [6, 7]. Nevertheless, a large

variation in the outcome of total knee arthroplasty procedures is observed. Even with a single

and experienced surgeon implanting the same prosthesis, there is a variability in outcome [8].

In particular, differences in daily functional capacities are reported [7, 9]. Moreover, many

differences in outcome cannot be explained by traditional clinical assessment methods [7].

Patient-specific differences could be at the base of this variability [8].

Gait analysis and the study of patient-specific kinematics and kinetics could provide an answer

to this puzzle [10]. Gait analysis has already been used to measure differences with OA patients

and to assess the functional outcome following TKA. The substantial variation in methodology

and small sample sizes of many studies has not yet given the opportunity to draw many specific

conclusions

Not only is the consistency between gait studies lacking, but even the reliability of the current

methods in gait analysis are questioned [11, 12]. The classic model used for the majority of

clinical gait analyses is based on that developed by Davis et al in 1991 [13]. This generic model

once was developed for and continues to be used with cerebral palsy patients, where neuro-

musculo-skeletal deformities are gross and relatively easy detectable when analyzing the gait

pattern.


4

When trying to analyze the kinetics and kinematics in normal subjects or, as in this study, in

osteoarthritis patients, requiring a knee replacement, using the current model it will be very

difficult to reveal statistical significant matter [14]. A large problem in the current methods of

gait analysis is the skin and soft tissue (and thus marker) movement over the underlying bones

[11, 15-22]. The accurate and consistent placement of skin markers is another common

problem. This is especially so because of particular problems around the pelvis and knee [11,

12, 18-20, 23]. At the pelvis, a major problem with knee patients is related to obesity. Even

slight obesity can cause difficulty in palpating the bony landmarks (e.g. ASIS and PSIS). The

accompanying, inaccurate placement of the markers, can results in unreliable estimation of the

hip joint centres. A similar situation is present around the knee, where bony landmarks are used

to identify the knee flexion-extension axis. When trying to compare pre- and post-knee surgical

gait where these landmarks have been eliminated, a model revision imposes itself.

The literature suggests the consistency and reliability of testing of these patients is indeed

questionable. Quite high reliability indices are quoted for the hip and knee kinematics in the

sagittal plane, but low reliability and high error rate (standard deviations or standard errors

from 16 to 34°) are frequently were cited for the hip and knee in the transverse plane [12]. The

consistency in reported measurements of kinetic data are equally variable. Knee adduction

moments, for example, were quite repeatable over four repeat gait assessments , but peak

vertical ground reaction force and knee flexion moment was not because they appear to vary

with pain [10]. Since the current standard gait lab procedure is prone to errors because of

practical problems with palpation in obesity, the use of empirical anthropometric relationships

and misplaced markers and alignment devices, high rates of error are also observed in the

determination of hip and knee joint parameters [23]. The same was found when testing one

subject at different testing laboratories [24, 25].

In summary, most errors were great enough not to be ignored during clinical data interpretation

[12]. A new and more trustworthy technique than the current is desirable in gait analysis,

preferably one that relies less on the skills of assessors in accurately placing markers and

interpreting results [11, 12, 18, 19, 23]. Already, new techniques, based on functional

calibration, have been developed [23, 26]. MR-based kinematic models have been explored to

optimise gait analysis [15, 19, 27, 28]. In this technique, skin-mounted markers, which render

opaque to both imaging modes (gait analysis and medical imaging), are used. By segmenting

these medical images, three-dimensional representations of the joints and their surrounding

bony structures and soft-tissues can be made. This allows to extract subject-specific anatomical


5

geometry back to the gait lab coordinate system. These image-based kinematic models have

been shown to significantly eliminate errors associated with the current methods [15, 19, 27,

28].

Also the use of wand markers to virtually recreate the common markers (e.g. the anterior

superior iliac spines) has been proposed [29]. This method, where markers on a stem, at known

distances from the wand, allow to virtually reconstruct the anterior superior iliac spines, could

especially be useful with obese patients. Besides, attention should indeed be given to a testing

procedure which minimises the errors due to skin and soft-tissue movement [11, 15-22].

Specifically at the knee joint, this drawback has led to imprecise measurements of the more

subtle movements, such as knee rotation and ab- and adduction [30]. Extra reflective markers

added to the standard protocol resulted in higher accuracy and more reliable capture of

movement of the knee joint [11]. To further optimise these subtle knee movements, it has also

been proposed to introduce a correction factor (based on knee rotation) for misalignment of

thigh markers [22].

To tackle the errors of the current standard gait lab procedure in knee osteoarthritis patients,

there was the need to develop a new procedure, as several factors (e.g. overweight, difficult

palpation of the bony landmarks, abnormal anatomy due to surgery,…) in this population could

not provide the full reliability of a standard gait lab test (as is for example used in the follow-

up of children with cerebral palsy). This new testing procedure should optimally respond to the

flaws of the current method, as mentioned above.

Brand & Crowninshield [31] commented, already back in 1981, on this matter. In response to

a discussion about the usefulness of certain tests in gait analysis, they described when a patient

evaluation tool could be useful and should be implemented. According to Brand &

Crowninshield any patient evaluation tool should match the following criteria:

1. The measured parameter(s) must correlate well with the patient’s functional capacity

2. The measured parameter must not be directly observable and semi-quantifiable by the

physician of therapist (being able to add precision to a measurement does not

necessarily add to its value in the overall evaluation of the patient, particularly if the

measurement is only one of many necessary in that evaluation)

3. The measured parameters must clearly distinguish between normal and abnormal

4. The measurement technique must not significantly alter the performance of the

evaluated activity

Introduction: Thesis overview

6

5. The measurement must be accurate and reproducible

6. The results must be communicated in a form which is readily identifiable in a physical

of physiological analog

Thesis overview

This thesis is structured as follows:

The background to osteoarthritis in the context of the knee is reviewed along with gait analysis

and its application in the assessment of (knee) osteoarthritis patient’s gait.

This is followed by a detailed literature review specific to the study aims from which the study

design is derived.

The main core of the thesis describes the adaptation of gait analysis methodology through the

incorporation of CT-derived patient-specific anatomical data into an adapted kinematic model.

The results of a pilot study involving 3 patients assessed pre and post operatively are presented

with analysis.

The thesis is completed with a discussion and conclusion section which also identifies a number

of areas for future work.

Background: Knee osteoarthritis

7

1. Background

1.1 Knee osteoarthritis

1.1.1 Pathogenesis of osteoarthritis

Osteoarthritis (OA) is known to be the most common form of progressive degenerative joint

disease, especially in the elderly [2, 32-34]. OA affects mainly knees, hands, hips and feet.

Several risk factors are identified, such as obesity and age. Also, it is already well established

that the three main tissues affected by the pathology of OA are: the synovium, the cartilage and

the bone [34]. OA manifests by damaging articular cartilage, formation of chondro-osteophytes

and thickening of subchondronal bone1. These manifestations can cause secondary arthralgia,

joint deformation and permanent moving disability [35].

Most of the current available OA studies focus on the pathological and biological processes in

the cartilage. These processes are the result of an unbalance in metabolic processes and the

appearance of degradation indicators, driven by many different cytokine cascades, and the

production of several inflammatory mediators [34, 36] .

In OA, chondrocytes and synovial cells produce more inflammatory cytokines than normal.

Therefore the anabolic collagen synthesis is reduced and there is an increase in catabolic and

other inflammatory signals [35, 36] . Among those inflammatory mediators are also several

oxidising agents which are accountable for the promoted apoptosis found in chondrocytes, the

catabolic processes and the destruction of matrix material. With this process in mind, the two

most important pathogenic events seen in the chondrocytes of OA are premature senescence

and apoptosis. This theory forms the base of the current pathogenic concept of OA, namely

that OA is a disease of premature aging of the joint articulation [34, 35]. All these degradative

biochemical processes are correlated with biomechanical changes in the joint. It has been

proven that those biochemical and biomechanical derangements both predispose and

perpetuate OA [36].

Even in early stages of OA, synovitis can be (subclinically) found. The histological changes in

the synovium enclose synovial hypertrophy and hyperplasia, with an increased number of

lining cells, most often joined by lymphocyte infiltration of the sublining tissue. When the

1 The most important grading system in OA, The Kellgren-Lawrence Grading Scale, is based on these three

manifestations. This grading system can be found in Appendix 1.


8

synovium is activated, it releases the proteinases and cytokines that accelerate the destruction

of the cartilage nearby [32, 36, 37].

1.1.2 Epidemiology

Osteoarthritis is one of the most common diseases of the joints of adults and the eldery

population [32-34]. As OA is the main source of arthroplasty of hip and knee, it is therefore a

main public health problem [1]. From an epidemiological viewpoint, OA is often divided into

3 different entities: radiological OA, clinical (symptomatic) OA and both [1].

By means of worldwide epidemiological research, it has been found that approximately a third

of all adult patients and half of the patients elder than 75 year have radiological signs suggesting

OA of the knee [32, 33]. Only 6% of all adults would also have symptoms [32]. Just 15% of

patients with proven radiological OA do have symptoms [32], where pain is most principally

observed [33]. A true subjective component clearly plays in patient experience of OA.

Although the exact mechanisms and details in pathogenesis of OA remain unclear, it has been

confirmed that various endo- and exogenous factors play a role [2]. Nevertheless, many

different causes leading to secondary OA have already been identified. Both can found in Table

1 and Table 2 below.

Endogenous and exogenous risk factors for osteoarthritis of the knee

Endogenous Exogenous

Age Macrotrauma

Gender Reptitive microtrauma

Heredity Overweight

Ethnic origin (more common in persons of European

descent)

Resective joint surgery

Post-menopausal changes Lifestyle factors (alcohol, tobacco)

Table 1: Endogenous and exogenous risk factors for osteoarthritis of the knee

Etiologies of secondary osteoarthritis of the knee Post-traumatic and post-operative Congenital/malformation

Malposition (varus/valgus) Aseptic osteonecrosis

Metabolic:

- Rrickets

- Hemochromatosis

- Chodrocalcinosis

- Ochronosis

Endocrine disorders:

- Acromegaly

- Hyperparathyroidism

- Hyperuricemia

Table 2: Etiologies of secondary osteoarthritis of the knee

It has often been assumed that age was one of the main risk factors in the development of knee

OA. According to some authors of high-quality studies on this subject however, no consistency

could be established [2]. However, it has been found that the prevalence of OA does increase

with age [1, 33]. This could be attributed to the fact that old age brings several other co-


9

morbidities with it. Recently, a two-phase population-based survey, revealed a rising

prevalence of symptomatic OA with aging. More interestingly, this phenomenon was observed

in a higher degree in females and the prevalence ran parallel with the distribution of obesity

[1].

When patients with unilateral OA of the knee were compared to a healthy control population,

it has been shown that there is abnormal joint loading on both lower limbs, despite of the

unilateral condition of the OA. This abnormal joint loading was defined by the co-contraction

index. This index is the measurement of the simultaneous contraction of the hamstrings and

the quadriceps in stance phase. Joint reaction forces can be increased when co-contraction of

these agonist muscles appear across the joint. Since the difference in external knee adduction

moment was significant between the patients with unilateral OA and the healthy control

population group, one could assume that abnormal joint load on both lower limbs leads to OA

of the knee [38].

Furthermore, an abnormal alignment of the lower limbs might be one of the most important

risk factors in the development and progression of OA of the knee. In presence of existing OA

of the knee, accelerated structural deterioration are observed when malalignment is present.

Varus malalignment creates a higher load on the medial compartment of the knee, as this

creates a higher load on the lateral compartment. In this way there is a higher risk of the

progression of the pre-existing OA in each specific compartment. Alongside the direct

influence of malalignment on the cartilage, abnormal alignment potentiates its effect in indirect

ways. Abnormal alignment works as a part of the vicious circle of the progression of OA but it

also has his indirect effect such as alteration in the knee-related tissues [39].

Symptomatic OA of the knee shows its impact by reduced mobility of the joint, which results

in a change of gait pattern [38]. But conversely, there is no clear indication of how mobility

and even movement and exercise can lead to OA of the knee. It is assumed that athletes

performing exercises with heavy loading of the knee joint, have a greater risk of knee joint

injuries or other injuries to the lower limb that result in limited mobility of their lower limbs

[2].

1.1.3 Treatment of osteoarthritis

The treatment of osteoarthritis can be divided in three main parts: non-pharmacological

therapy, pharmacological therapy and surgical therapy. This stepped-care strategy (SCS) works


10

as a framework in which different treatments are covered by increasing degree of effect and

impact [40].

Non-pharmacological therapy

In the first step of treatment of OA the main goal is to try to stop the progress of the disease by

taking out the main causes. An important factor in the decision of the treatment is the presence

of any possible co-morbidity of the individual patient. Before OA can be treated, any co-

morbidities should be eliminated (e.g. excess weight). Therefore, in the first phase of treatment,

one is focused on lifestyle advice, weight management, strength training, self-management and

education [41].

At the point where these lifestyle changes are not enough to stop the progress of the disease or

when more pain starts to occur, there are still some non-pharmacological therapies on which

the patient can rely. Some examples are: acupuncture, water therapy, cane and crutches, land

and water based exercise and strength management [40].

Pharmacological therapy

There are many different pharmacological drugs available in OA therapy. Once the decision is

made to incorporate pharmacological drugs in the therapy plan of the patient, still some kind

of sequence is followed based on the different grades of impact of the medicine on the body.

Pharmacological therapy is often started with acetaminophen and/or glucosaminesulphate.

When this is insufficient to suppress symptoms and pain, one can transfer to (topical) NSAID

and/ or tramadol [40]. In addition to the two mentioned groups of medication, all kinds of other

pharmacological therapies are available e.g. capsaicin, corticosteroids, chondroitin, diacerein,

duloxetine, glucosamine, hyaluronic acid, opioids, risedronate and roship [41].

Surgical therapy

When every possible non-pharmacological or pharmacological therapy fails, surgical therapy

remains an option, but only when the pain is unbearable and (knee) function is compromised

despite all other therapy management. Surgical therapy of osteoarthritis saw its first important

developments in the 1950s and 1960s. In this period three main techniques, that are still used

today, were introduced: surgical debridement, realignment osteostomy and prosthetic

arthroplasty, both unicompartimental (UKA) and total (TKA). During the last five to ten years,

surgical therapy has developed from a pain reliever but with stiff knee functional disability to

a complete functional and pain free result therapy, with the outcome greatly influenced by the

quality of the materials used [42].

Background: Gait analysis

11

The first knee athroplasties were all TKA. Though, in 5 to 20% of TKA-patients, only one

compartment of the knee joint was involved in the osteoarthritis process. Because of this,

unicompartmental arthroplasty was designed. Over the years these two procedures were refined

and new quality material was introduced to optimise satisfaction outcomes [42, 43]. Today,

knee replacement is the most common form of surgical therapy in OA. It is even stated that

TKA is the only curative procedure for knee OA [33].

1.2 Gait analysis

1.2.1 Definitions

1.2.1.1 Locomotion and gait

Locomotion is a complex phenomenon, which can only be meticulously described by means

of a multidisciplinary approach. Traditionally, the classical mechanical viewpoint has quite a

large share in this [44]. Gait, more specifically, can be defined as any method of locomotion

characterised by periods of loading and unloading of the limbs [45].

The quality of gait depends on two major factors [46]. First, the locomotor system imposes a

certain degree of limitation, based on the functional and structural properties of the subject’s

body. Secondly, the gait pattern is associated with the ability to put this locomotor system into

action in an effective way. These two concepts should be in the back of our heads when looking

at gait.

Furthermore, five parameters that are essential in normal gait can be described [47]. These are

the following: (1) stance phase stability (2) swing phase stability (3) foot preposition in

terminal swing (4) adequate step length, and (5) energy conservation.

1.2.1.2 Gait analysis

Although more than 30 years of intense research has passed in the field of gait analysis, a clear

single concept of it is lacking [45]. Every approach seems to attend on its own principles.

According to Davis et al. [13] analysis of gait, in general, “is the systematic measurement,

description, and assessment of those quantities thought to characterise human locomotion.” By

the acquaintance of kinematic and kinetic data, the gait characteristics of the studied subject

are described and interpreted by the clinician. This last element may not be underestimated.

Although the main objective of gait analysis is to assign a value to the quality of gait, this

valuation is only the first step and a multidisciplinary, clinical interpretation of the results is

indispensable and needed in an early stage [44].


12

Gait analysis has been around for over nearly 180 years. The Weber brothers were the first to

measure temporal and distance factors of gait [15, 31]. Over the years, just as in probably every

(para)medical discipline, the share of synthesis by computer (and thus strictly numerical

analysis) has grown. Furthermore, it simplified, amplified and structured the data collection

and analysis. Gait analysis evolved from art to science [47]. Nevertheless, up to date, clinical

interpretation and intervention stays essential, already in an early phase [46].

The original application of gait analysis was to assess, in a quantitative way, the degree to

which gait is affected by an already diagnosed disorder. Subsequently, gait analysis was used

as a diagnostic tool (for separating out complex movement patterns into primary cause and

secondary effects), instead of just an evaluation tool, and, for example, it radically changed the

treatment of cerebral palsy [47]. Gait analysis needs to be seen as a special investigation, like

e.g. radiology or blood biochemistry. Patient history and physical examination stay elementary

to it [48].

The kinematic model used for the majority of clinical gait analyses is based on that developed

by Davis et al in 1991 [13]. Nowadays, the gold standard is the so called “computerised three

dimensional gait analysis” (3DGA) [20] and clinical gait analysis usually involves 5

components [48]: video recording, quantifying of general gait parameters (cadence, stride

length, speed,…), kinematic analysis, kinetic measurement (primary the reaction force of each

foot stride), and electromyography (EMG). By combining the kinematic and kinetic data, it is

possible to create a three-dimensional representation of joint moment and powers. Sometimes

also oxygen consumption, which is an indicator for the metabolic cost, is tracked. All data now

tends to be stored on, processed and accessed through computers.

1.2.2 Difficulties and restrictions

Instruments for measuring gait have become more and more sophisticated and practical for

clinical use. Today, hardware and software are in most cases able to eliminate the problems

associated with manual marker trajectory identification experienced in the past [18]. Markers

can now be automatically tracked in real time with some possibility to identify potential

tracking problems and correct them while the patient is still in the lab.

Nevertheless, the measurements in gait analysis stay prone to error, often of surprisingly large

magnitudes [49]. The reliability and validity of gait assessment should be known to the user in

order to be used appropriately [12, 20]. For example, the precise timing of toe-off stays – even


13

with the use of a force platform – difficult [18]. Further, as already mentioned in the

introduction of this paper, a large problem in the current methods of gait analysis is the skin

and soft tissue (and thus marker) movement over the underlying bones [11, 15-22]. In this way,

the depending calculations of the knee joint centres will be no more than a fair estimation (it

maximises varus/valgus and minimises flexion/extension range of motion) [11].

The accurate and consistent placement of skin markers is another common problem [11, 12,

18-20, 23]. Even more, when testing one subject at 12 different laboratories, marker placement

among examiners was identified as the most variable parameter that influenced clinical

outcome [25]. When 11 children with cerebral palsy were tested at four different centres, only

two of them got the same treatment recommendation after gait analysis [50]. Thus, training of

clinical staff and the build-up of experience of this clinical personnel is considered to be

essential [12]. In addition, inconsistent anthropometric measurements, variation in walking

speed, data processing or measurement equipment errors are reported as having a major impact

on data variation [12]. Furthermore, the calculations for e.g. the hip joint centre, used in nearly

all software systems, is based on cadaver studies and is therefore far from patient specific [18].

To optimise the determination of the knee joint axis, the use of knee alignment devices (KADs)

has been introduced in many labs, although this was found to be difficult to handle and less

reliable within or between therapists [11]. The use of CT or MRI imaging and subject-specific

approach could provide a solution for this matter.

A meta-analysis of 23 studies [12] showed moderate to good reliability for the sagittal and

coronal plane variables. Pelvic tilt and knee varus/valgus alignment are the major exceptions

to this rule. Spatio-temporal parameters (such as cadence, velocity and step width) were shown

to be highly repeatable when the same observer retested a subject [20]. In the same study it was

concluded that range of joint motion was more repeatable than maxima and minima of the same

movements. McDermott et al. [51] already presumed that this might be due to variations in

marker placement, resulting in an offset from flexion to extension. In this way, measured

maxima and minima differ where the total range is in fact unchanged.

Kinematic data however was shown to be quite repeatable (the standard error of measurement

was lower than 5°) [20]. In the transverse plane, hip and knee rotation mostly had a reported

error of more than 5°, a value where most of the other variables stayed under and which can be

seen as the upper limit of trustworthy data collection [12]. Measurements in the transverse

plane in general were found less reliable [11, 20].

Background: Gait in patients with knee osteoarthritis

14

Various studies have concluded that age, gender, height and weight, can all affect the results

of gait analysis [52]. There are many different normalization methods to reduce the influence

of those different parameters. One method is for example to divide the joint moments by body

weight times height [53].

1.3 Gait in patients with knee osteoarthritis

1.3.1 Common findings in knee OA patients

Although OA affects large portions of the (elderly) population, the exact mechanisms of the

pathogenesis of the disease remains unclear [32]. Also, more and more diagnostic methods and

therapeutic strategies are investigated. Novel therapeutic agents (symptom modifying drugs),

but also OA therapy and follow-up in general, require excessive health care time and costs [54].

Nevertheless, pain, the major clinical symptom in OA, is largely subjective and difficult to

quantify, especially between patients. Various clinical knee scores available, differ

considerably in terms of validity, reliability and responsiveness. As pain is a complete

subjective feeling, further research should be done to create an objective assessment of the

disease status.

Gait analysis is receiving increasing attention in the evaluation of osteoarthritis patients and

could provide a solution to this hiatus [10, 55]. A key factor in the development and progression

of knee OA is excessive and/or abnormal mechanical loading, which could be detected in an

early stage through gait analysis [6, 56].

The (external) knee adduction moment, which correlates with the medial loading of the knee,

has been linked to the presence, severity and development of (medial) knee OA [6, 38, 55, 57-

62]. In one six-year follow-up study a high knee adduction moment at baseline could even

predict radiographic OA [63]. Every 1% increase of adduction moment above baseline would

correspond to a 6.5 times greater chance of OA [62]. Nevertheless, recent studies found that

high knee adduction or other gait changes do not occur in early OA [64, 65], although altered

muscle activation (gluteus medius muscle on both sides and hamstrings and quadriceps on the

affected side) does appear in the early stages and becomes apparent when testing balance [38,

64]. Also, in already developed OA, only the adduction moment impulse (the integral of all of

the frontal plane knee joint moments), in contrast to the peak adduction moment, correlates

well with pain [10, 66]. Furthermore, also speed, the magnitude of the first peak in the ground

reaction force and knee flexion moments varied with pain and could be used as an objective

way of quantifying pain levels [10]. Internal or external quantities are, however, weak


15

indicators of internal knee contact forces [67], although medial knee OA patients do have large

medial contact loads [59].

Secondary gait changes observed among knee OA patients may reflect a strategy to shift the

body's weight more rapidly to the support limb and to unload the knee as fast as possible

(reduce the moment arm of the ground reaction force as soon as possible). This is thought to

be successful merely in patients with less severe knee OA, as these strategies only increase the

axial forces and thereby not only worsen the progression of knee OA over time, but also help

develop OA in adjacent joints [61]. Additionally, patients with knee OA have been found to

make initial contact to the ground with a more extended knee than their symptom free controls

[61], walk slower [38] , have a longer stance time and smaller average ground reaction force

[68].

Patients with a valgus deformity reported lower pain and less functional deficits compared to

patients with a varus knee [69]. Also patients with varus knee augmented their upper body gait

compensations, mainly in the frontal plane [69, 70].

Still, it should be noted that, although the discriminative capacity (healthy-unhealthy) of gait

analysis in OA is demonstrated, its validity in decision-making is not [14].

1.3.2 Common findings after total knee arthroplasty

After total knee arthroplasty, changes in articular surface, soft tissue or limb alignment can

modify normal lower limb kinetics, kinematics and function [52, 71-73]. Although a temporary

result, the outcome of minimally invasive surgical techniques suggest that reduced trauma of

surgery could speed up early rehabilitation [72]. In general, it was found that the type of

surgical technique significantly influenced variability and stability of gait post-op [74].

As before knee arthroplasty, the post-surgical external knee adduction moment receives a lot

of attention [6, 52, 55, 62], mainly because it was found associated with early component

loosening. The same was found for peak flexion moment of the knee [55, 75, 76]. Also the

speed of progression is found slower and stride length shorter [76]. After TKA, a decrease in

adduction moment has been noted [6, 62, 77]. Although this is positive with respect to joint

loading, this may not enhance the prosthesis survival rate and may worsen anterior knee pain

[6]. This effect, however, is found to be no longer present after 1 year [62].

A systematic review [52] reported that subjects with TKA walked with less total range of

motion (ROM) of the knee than normal subjects. Specifically knee flexion was reduced during


16

the swing phase. Only 20 to 36% of TKA patients walked with a normal biphasic moment

pattern2, although more patients had this bimodal waveform after surgery [6, 77]. Of all

functional parameters, this reduced ROM is most quoted by patients and surgeons [72].

Nevertheless, in a study of 42 patients with severe OA, gait parameters after TKA, with the

exception of external knee rotation moment, moved to a more asymptomatic pattern (including

knee adduction moment, knee flexion moment, speed, stride length,…) [6]. In another study

with 32 patients [78], no significant changes in knee joint kinematics and kinetics were found

despite improvements in pain and function. Although pain rapidly improves after TKA, gait

parameters did not always [55, 68, 79]. Abnormal loading on other major joints of the lower

limb also persisted [77].

A deviation of the mechanical axis of the leg of more than 3° in the coronal plane (varus/valgus)

is believed to be associated with reduced longevity of the prosthesis [80]. On the other hand,

the concept of constitutional varus, which does not affect joint line orientation, should be taken

into account and should influence decision-making in surgery [72]. Furthermore, static

alignment has been found to not influence the dynamic loading of the knee, which means that,

even when nearly restoring the mechanical axis, excessive medial wear could still be present

[62].

However, variations in subjects, prosthetic designs and methodology of gait analysis make

comparison of studies (again) very difficult [52]. Prosthetic design in particular has a major

impact on the gait pattern, although some observations (stiff-legged knee motion during the

loading phase, reduction of knee range of motion, abnormal knee moment patterns and

prolonged and increased co-contraction) has been found irrespectively of the TKA design [71].

2 The biphasic moment pattern around the knee is associated with normal gait. Approximately 80% of normal

subjects walks with such a pattern. The biphasic moment is observed in the sagittal plane. The initial external

moment around the knee normally tends to extend the knee. When walking, this moment rapidly changes to a

flexion moment, after which it goes back to extending the knee to finish the stance phase with a flexion moment.

Sagittal moment that are not biphasic are typically called quadriceps overuse (extension throughout stance) or

quadriceps avoidance (flexion in stance) patterns.

Literature review: Findings

17

2. Literature review

2.1 Findings

Most of the software packages in gait analysis rely on a generic model of the lower extremity.

Different empirical datasets, mainly based on normal subjects, are available for this purpose

[34, 36, 81, 82]. In order to calculate kinematics in gait analysis, rescaling of these generic

models is often felt necessary [19, 81]. Nevertheless, this procedure can induce major errors,

mainly due to (1) errors associated with marker placement on anatomical landmarks [16, 17,

21] and (2) imprecision in the description of joint systems [11]. The kinematic errors were most

pronounced in the sagittal and transverse planes, mainly hip and knee flexion and hip rotation

[19].

Cadaveric studies already showed that combination of MR imaging and kinematic modelling

provides an accurate estimation of muscle-tendon lengths and moment arms in vivo [83] Three-

dimensional reconstructions of human joints were of equal high quality based on CT and MR

scan [84].

On information gathered from academic and industrial research sites throughout Europe [85],

it was concluded that for many neuromusculoskeletal treatments, as for OA patients, “one size

fits none”. Every patient is simply too different and, according to the experts, this affects

treatment in a significant way. Although time and cost-consuming, based on medical imaging,

more accurate and subject-specific kinematic models can be constructed [19, 28, 81, 86].

Design of prosthetics, orthopedics, injury prevention, and understanding of cartilage

degeneration would indeed improve through detailed, individual knowledge of the mechanical

loading of the knee [87]. To date, however, more generic than personalised models are still

used [85].

A brief overview of review of the literature for attempts to utilise individual skeletal

morphology in the kinematic modelling process reveals only a few references summarised:

- Innovative work was done by Dr. Viceconti and his team, who – based on CT imaging

– developed a subject-specific musculoskeletal gait model of a patient with a massive

biological skeletal reconstruction [88]. The patient walked in the gait lab with 34

reflective markers and was scanned with the same marker set. Even 82 muscular paths

were extracted from the CT scan to complete the model. The knees, however, were only

crudely modelled.

Literature review: Conclusions and relevance for

experimental work

18

- Based on both MRI and CT scans, personalised ankle and foot biomechanics were

generated to improve orthotic design [89].

- Using a subject-specific CT model, Dao et al. found an influence on gait parameters

varying up to 75% in a post-polio residual paralysis patient [90]. Kinematic parameters

however were not found sensitive to error.

- Subject-specific modelling of the hip geometry was already found to be crucial in

quantification of musculoskeletal loading of the hip joint. Medical imaging was used in

closely reconstruct the subjects hip anatomy [28, 91, 92]. Without the medical imaging

data, a substantial underestimation of the hip contact force was found and incorrect

conclusions on the inclination angle were made. Also, the loading conditions before

and after total hip prosthesis were evaluated with these subject-specific models [91].

- Very recently, a musculoskeletal model [93] combined with subject-specific CT data

was used to predict the knee forces in a 83-year old male with a total knee prosthesis

[87]. The knee joint contact forces, vertical ground reaction forces and muscle and

ligament forces were efficiently forecasted.

2.2 Conclusions and relevance for experimental work

To our knowledge, no work has yet been performed on the use of a subject-specific model in

gait analysis in (knee) OA patients. Nevertheless the literature offers evidence of huge potential

improvement that may be achieved with such techniques. In the following pilot study, the use

of such a model for knee OA patients is developed, explored and assessed through comparison

with conventionally and simultaneously derived gait analysis data.

Experimental evaluation: Material and methods

19

3. Experimental evaluation

3.1 Material and methods

3.1.1 Study design and selection

Recruitment for this pilot study took place during consultations at the polyclinic of the

Department of Orthopedics of the University Hospital of Ghent and started the 1st of February

2013. When consulting patients needed a knee prosthesis, they were screened based on the in-

and exclusion criteria found in Table 3. When none of the exclusion criteria were found and

patient matched all the inclusion criteria, he or she was invited to take part in the study and was

individually approached. Patients were free to participate. No (financial) compensation was

given.

The minimum age was set to 60 years. This because of issues with radiation dose (patients were

scanned twice by a CT scanner).

INCLUSION EXCLUSION

1. >60 years old 1. <60 years old

2. Primary knee prosthesis 2. Traumatic or orthopedic history of the lower limbs. Or

history of systemic disease

3. Normal bilateral anatomy of back, hip, knee, ankle and

foot

3. Neurological of visual diseases that affect gait

4. Normal bilateral mobility and function of back, hip,

ankle and foot. Normal unilateral mobility and

function of knee

4. Contralateral pain at hip, knee, ankle and/or foot

5. Although possible pain and complaints, patient can

walk for 400 meters without needing to sit without

help.

5. Arterial insufficiency or thromboembolic diseases

Table 3: In- and exclusion criteria for gait study

This study was approved by the Ethics Committee of the hospital. All patients provided

informed consent.

3.1.2 Study procedures and data collection

Patients included in the study underwent a full-leg CT-scan and a gait assessment. The

radiology appointment was planned so as to directly follow the gait assessment. Three

aluminum plates were attached to the skin of the patient, one over the sacral area and one over

the anterior region of each tibia for both the gait assessment and the CT. This procedure (gait

analysis and CT-scan) was repeated pre and 3-6 months post-surgery.


20

3.1.2.1 Gait assessment

3.1.2.1.1 Introduction and preparation

The gait assessments were carried out in the Gait and Movement Analysis Laboratory in the

Cerebral Palsy Reference Centre at the University Hospital of Ghent. A ‘Vicon 612’ 3D

photogrammetric movement analysis system (©Vicon Motion Systems, Oxford, United

Kingdom) was used to record the three-dimensional movement of the lower limbs.

Prior to each assessment the laboratory system was calibrated according to routine gait lab

procedures. Various anthropometric data were measured at the start of the gait assessment. This

data is required by the Vicon software in order to scale the kinematic model to the individual

patient. The measurements required are outlined in Table 4.

REQUIRED ANTHROPOMETRIC DATA

Height (0.5cm) Weight (0.5kg)

Knee width (L and R)

As defined: most inner to most outer bony

structure (0.1cm)

Ankle width (L and R)

As defined: medial to lateral malleolus

(0.1cm)

Distance between ASISs (0.5cm) Leg length (L and R)

As defined: from ASIS to medial malleolus

(0.5cm)

Tibial external rotation (L and R)

As defined: (natural) external rotation of the

lower leg measured at the ankle, with knee at

zero degrees (degrees)

Table 4: Anthropometric data collected before gait assessment

Sixteen reflective markers of the Vicon Plug-in-Gait marker set (© Vicon, Oxford, United

Kingdom) were attached to the patients’ bodies. During walking, each marker is tracked within

a three-dimensional Cartesian coordinate system (x-, y- and z-coordinates) and these

trajectories are reconstructed in the Vicon system software. The standard markers for lower

body gait assessment, based on Davis et al. [13], were used (see Table 5 below). In a static

trial, ‘knee alignment devices’ (KAD) (© Motion Lab Systems, Baton Rouge, USA) were used

to define the knee joint axis. For the dynamic trials the KAD’s were detached and replaced by

a simple marker placed on the lateral side of the knee.

3.1.2.1.2 Plates with extra markers

The principle concept on which the hybrid model combining CT and 3D marker data was

proposed, was the use of a common patient based reference frame attached to the skin in an


21

area near to relevant underlying bone in which the skin/adipose/muscle tissue movement was

minimal. Three aluminum plates attached to the patient’s body over the sacral and anterior

tibial region were used to form 3 such reference axes that could be visualised and defined in

both the gait laboratory and CT reference frames. These plates were worn during the gait

assessment and remained in place during the CT scan. The plates allowed identification of the

relative position of anatomical areas of interest (e.g. the hip joint centres and the ASIS). The

exact positions of the latter could then be reconstructed in relation to the plates and the

morphologically based axes defined from the CT derived joint centres and bony reference

points, as opposed to the joint centres derived from empirical relationships between the surface

markers and key anatomical points.

The exact placement of the plates on the subjects’ bodies wasn’t relevant,

even though comparable positions were desirable for methodological

consistency – the principle was to locate the plates as close to the bony

structure of interest above an area of low levels of adipose tissue with low

skin/adipose tissue/muscle movement so that the plate remained fixed in

distance and orientation with respect to the pelvis or tibia respectively.

Therefore, the sacral area and tibial surface plateau regions were

identified as the likely optimum (see also Figure 1). During gait, it was

hoped that the plate movement with respect to for example the hip joint

centres (derived by 3D empirical means) for the sacral plate and knee joint

centres (also derived by 3D empirical means) for the tibial plates would

be low. The movement (real and/or because of inaccuracy of the system)

was rated by repeated measurements on the same normal subjects (see 3.1.2.4).

The plates contained tapped holes (see Figure 2 and Figure 3), into which three 3D-markers

could be secured with plastic threaded bar during the gait analysis. Each of the 3 markers,

attached to each plate during the gait assessment, were unscrewed from its respective tapped

hole to allow the subject to lay supine in the CT scanner and to enable clothing to be worn over

the plates during the transition between the gait lab and CT scanner. With these extra 3D-

markers, nine extra markers were added to the patient in addition to those required by the

standard Plug-in-Gait model (see Table 6).

Figure 1: Positions of the

plates attached to the

patients' body


22

REFLECTIVE SKIN MARKERS ATTACHED TO THE PATIENT’S BODY

Abbreviation Anatomical position

LPSI, RPSI Posterior superior iliac spines

LASI, RASI Anterior superior iliac spines

LTHI, RTHI Lateral side of thigh, left and right at different heights from the ground

LKNE, RKNE Lateral side of knee, based on the position of the knee alignment device (KAD)

LTIB, RTIB Lateral side of shank, left and right at different heights from the ground

LANK, RANK Lateral malleolus

LHEE, RHEE Heel, at same height from the ground as LTOE/RTOE

LTOE, RTOE Base of metatarsal II, dorsal side of foot

Table 5: Standard set of reflective markers used in lower body gait assessment

EXTRA REFLECTIVE MARKERS ATTACHED IN THE PLATES’ HOLES

Abbreviation Position

LSPL, RSPL, MSPL Left, right and middle (caudal) holes of sacral plate

LTP1, LTP2, LTP3 Most cranial (1), middle (2) and caudal (3) holes of left tibial plate

RTP1, RTP2, RTP3 Most cranial (1), middle (2) and caudal (3) holes of right tibial plate

Table 6: Extra sets of reflective markers screwed in holes of plates

The plates were made out of aluminum, which showed up clearly on the CT imaging, but didn’t

cause any disturbing scatter. For optimal discrimination of the markers and tracking of the

position and orientation of the plate, especially in gait, the markers needed to be maximal apart

(e.g. in the three corners of the triangular sacral plate) but the plate size had to be kept small

enough to not encumber the patient nor be so large as to hinder the movement of the patient or

positioning in the CT scanner. The dimensions of the plates (in mm) are outlined in the CT

derived images below (measurements were made in 3-matic software)3.

Figure 2: Dimensions of the tibial plate

Figure 3: Dimensions of the sacral plate

For easy attachment – and more importantly from a patient comfort point of view – detachment,

the plates were fixed to some hook-and-loop fastener (Velcro®). The other side of the Velcro®-

3 Larger size images (with readable dimensions) can be found in Appendix 3 and Appendix 4.


23

tape was stuck to a thin flexible plastic sheet with the same dimensions as the plate. The plastic

sheet was attached to the patient with hypoallergic double-sided tape. In this way, first the rigid

plate and one side of the Velcro® could be removed without pain. Afterwords, it was much

more easy and more comfortable for the patient to be able to peel the other thin plastic sheet

and the double-sided tape off the skin rather than having to detach a stiff plate stuck directly to

the skin. Once attached, the plates remained on for the gait data collection and the CT scan.

3.1.2.1.3 Gait assessment

Patients were asked to walk at comfortably, self-selected speed and data trials were collected

until at least 5 successful trials at each side were captured. A successful run was defined as a

walk having the appearance of natural, relaxed gait from start to finish and with at least one

single step on one of the two force plates, without aiming or hesitating towards it.

Patients were not briefed about the presence of the force plates, since an awareness of the

presence/use of the force plates is frequently found, in clinical practice, to alter the gait pattern

as patients try to help target the plate rather than stepping on it naturally. A few additional trials

were captured in case any of them were not useable as a result of poor marker tracking. Patients

were informed that they could rest sitting on a chair whenever they felt the need to as a result

of knee pain or fatigue.

Data was collected at 120 Hz using the infrared motion capture system with data from 2 Kistler

force plateforms (© Kistler Instrument Corporation, Amherst, New York, U.S.) located in the

laboratory floor. In addition, conventional video data of each trial, alternately taken of the

transverse, sagittal and coronal planes, was captured and stored synchronously with each walk.

The video provided a visual record of the subjects gait to facilitate subsequent analysis and

data processing.

Software modules Vicon Workstation (v5.2), Vicon Polygon (v3.5) and Vicon BodyBuilder

(v3.6.1) software were used to collect, process and present the kinematic, kinetic and video

data (© Vicon Motion Systems, Oxford, United Kingdom).

3.1.2.1.4 Gait analysis

In processing the gathered gait data, Vicon Workstation (v5.2) was used in the first instance.

The 3D-trajectories of the reflective markers were reconstructed and auto-labelled (after

defining subject measures and manually labelling the first trail). Data was filtered using the

Woltring Filter (predicted MSE of 20). Close inspection of each trial was necessary to correct


24

for incorrectly labelled markers and ensure that filtering and small trajectory gap filling

routines completed successfully. Gait cycle events (strike and toe-off of each stride) were

automatically determined based on force plate data, but manual intervention and gait cycle

event identification (and subsequent generalising of events) was frequently required. The

dynamic Plug-in-Gait model was used to determine joint centres and to calculate the kinematic

and kinetic data. Ten successful trials (5 left and 5 right) were loaded into the Vicon Polygon

(v3.5) module and kinematic data were exported to SPSS (see 3.1.3 Statistical analysis).

3.1.2.2 Medical imaging

3.1.2.2.1 Radiology

Scanning was carried out using a multi detector CT scanner (MDCT) (® Somatom Definition

Flash, Siemens, Erlangen, Germany) at the Department of Radiology of the University Hospital

of Ghent. Patients were positioned supine with their feet towards the scanner (feet first

position). Patient’s feet were taped to each other at the level of the right and left metatarsal-

phalangeal joint I and phalangeal I. In this way the legs were fixed in a slight endorotated

position. Scanning occurred from the top of the attached sacral plate (mostly at the level of L3-

L4) down to just below the tibiotalar joint (both malleoli had to be fully scanned).

In view of reducing patients’ radiation dose as much as possible, a “low dose scanning

protocol” was used. Further enhancements were made by means of dose modulation and

iterative constructions. This resulted in an average Dose Length Product (DLP) of 1080 mGy

cm.

3.1.2.2.2 Segmenting of CT slices in Mimics

For purpose of extracting skeletal data from the CT scans the 3D image processing software

Mimics (v16.0 – © Materialise, Leuven, Belgium) was used. With Mimics, it is possible to

convert the stacks of 2D-slices (in the axial, coronal and sagittal plane) to 3D surface objects.

The bony structures of the lower body (hips, sacrum, femurs, patellae, tibiae and fibulae) were

extracted from the CT-scans, as well as the three plates. Because, post-surgery, the knee

prosthesis caused troublesome scatter, ready-made 3D surface objects of the prosthesis

(available in every femoral and tibial size) were imported and inserted in place. This way, the

cumbersome segmenting of the prosthesis wasn’t necessary and, nevertheless, a reliable surface

model was present. The 3D models of the bony structures, the plates and, if applicable, the

prosthesis, were exported to the software package of 3-matic (v8.0 – © Materialise, Leuven,

Belgium), where various measurements could be done (Appendix 2).


25

3.1.2.2.3 Readings in 3-matic

In 3-matic, the markers of the plates were virtually recreated at 15mm from the centre of the

holes. Furthermore, the ASIS, PSIS and ankle malleoli were manually pinpointed. The hip,

knee and ankle joint centres were localised by means of surface marking and subsequent

determining of the best fitting spheres.

With 3-matic it was possible to obtain the exact relative positions of the plates in respect to the

anatomical areas of interest (e.g. femoral hip joint centre). These positions were exported and

used as reference data which defined the individual skeleton of the subject in gait analysis

software (described in more detail in the next chapter).

Technical aspects about how these readings were done can be found in Appendix 2, though

this description is quite procedural. It is therefore mainly to be used as a manual for researchers

in the future.

3.1.2.3 CT-based subject-specific gait model

After processing the CT slices in Mimics and 3-matic, the three-dimensional coordinates of the

markers and anatomical areas of interest were exported in a text file. With Vicon Bodybuilder

these coordinates were used to re-determine the patient’s gait data. To do this, a replica model

of the Plug-in-Gait model (since not open-source) was created. Built upon this replica, the

hybrid model was defined. Before running the model, based on the CT coordinates imported

from the text file, hip and pelvis markers and joint centres were calculated in reference to the

sacral plate. For the knee and ankle this was done with respect to the two tibial plates. By

running the adapted model, joint centres, kinetics and kinematics were redefined and the file

was saved as a copy of the original. This was done for the same ten trials as were previously

used in the generic model.

3.1.2.4 Intermezzo: Assessing the reliability of the data collection

Both the reliability of gait data collection and 3D-CT imaging were estimated by repeated

measurements by different observers. The results were necessary to interpret forthcoming

results. A brief overview of this assessment of reliability can be found in Appendix 5.

3.1.3 Statistical analysis

All data was analyzed by the statistical software package of SPSS Statistics (v21.0 - © IBM,

Armonk, New York, U.S.). Further calculations and graph building were done in MS Excel


26

(v2013 – © Microsoft, Redmond, Washington, U.S.). Temporal parameters and kinematic and

kinetic data were imported from the Vicon Polygon module. Data of the 20 gait cycles (10

trials, left and right) were averaged for left, right and both legs. Since the kinematics are time

varying data, thirty-four kinematic summary variables were extracted for a gait cycle for each

trial (for both left and right data) per patient and per assessment. These key variables were

calculated based on ranges, minima and maxima and, if necessary, filtered for one of the gait

cycle phases (initial contact, stance phase, swing phase).

Paired samples T-tests (significance level of p<0.05 and p<0.001) were used to detect

differences between the two models for each patient. The average differences between the

generic and subject-specific kinematic data were calculated. The limits of agreement (95%

confidence interval for the mean differences) were determined. Also, indirect measures for the

accuracy of a lower-body gait model have already been explored [23]. From that viewpoint, an

accurate model is characterised by minimised cross-talk (one joint rotation in one plane being

interpreted as one in another). On the knee, therefore, the smaller the range of motion (ROM)

in the frontal plane (varus/valgus) and the larger the ROM in the sagittal plane

(flexion/extension) is, the more trustworthy the model is. Knee ROMs in these two planes were

determined for each trail and a paired samples T-test was used to detect significant differences

between the ROMs of the two models. To further elaborate the precision of the subject-specific

model, the relative positions of the hip and knee joint centres of each model were exported and

their differences (coordinates in each plane) calculated. In a paper published in 1990, Bell et

al. [94] concluded that the location of the hip joint centres (HJCs) could be predicted as a

percentage of the distances between the anterior superior iliac spines (ASISs). HJC were – in

normal adults – on average located 22% of the interASIS distance posteriorly, 14% medially

and 30% distally with respect to the ASIS on the same side. The manufactures state that given

the choice of optional anthropometric parameters entered into the VICON Workstation

software that this is the model utilised by it’s PIG module. Both for the PIG model and CTM

this relative position was determined for each hip joint centre for each assessment.

Pearson correlation coefficients were calculated for the differences between the generic and

subject-specific kinematic data. To correct for the fact that the two measurements could be

highly correlated but without much agreement, also the intraclass correlations coefficients

(ICC(3,1) - two-way mixed effect, absolute agreement, single measures) were determined.

Scores were interpreted as no (<0.00), poor (0.00–0.40), moderate (0.41–0.60), strong (0.61–

Experimental evaluation: Results

27

0.80), and almost perfect correlation (0.81–1). An F-test was performed on the ICC values to

determine the significance level of the differences.

Independent samples T-tests (significance level of p<0.05 and p<0.001) were used to compare

the pre- versus post-surgical assessments of each patient. Temporal parameters (walking speed,

stride length, step length, step width and cadence) and sagittal hip and knee kinematic summary

variables were compared and systematic differences were identified.

3.2 Results

3.2.1 Study subjects

Three patients were included in the study. They were planned for surgery between May and

October 2013. A gait analysis was planned from one month before surgery onwards. Patients

were contacted again (4 to 9 months) after surgery for a second gait analysis. All three of the

patients completed the study. Patient characteristics can be found in Table 7. The results of

their body measurements before gait assessment are in Table 8.

ID (initials) Patient 1 (BMJ) Patient 2 (DBM) Patient 3 (DRM)

Sex Female Female Female

DOB (age at surgery) 24/05/1947 (66y) 14/09/1940 (73y) 20/08/1942 (70y)

Side affected Right Right Left

Date of pre-surgery gait

assessment 23/08/2013 18/10/2013 03/05/2013

Date of surgery 19/09/2013 21/10/2013 27/05/2013

Date of post-surgery gait

assessment 28/02/2014 07/03/2013 28/02/2014

Type of prosthesis Journey II Journey II Journey II Table 7: General characteristics of the three included patients

ID (initials) Patient 1 (BMJ) Patient 2 (DBM) Patient 3 (DRM)

Pre- or post-surgery Pre Pre Pre Post Pre Post

Height (cm) 154.5 163.5 163.5 163.5 163.0 164.5

Weight (kg) 88.0 65.0 65.0 64.0 100.0 100.0

BMI (kg/m²) 36.9 24.3 24.3 23.9 37.6 37.0

BMI interpretation obese normal Obese Table 8: Subject measurements before the pre- and post-surgery gait assessment

3.2.2 Comparison of the generic model with a replica

The Plug-in-Gait Model (PIG) is built into the Vicon Workstation software. The software is

described in the software manuals but it is not open source software and so the finer details of

model implementation are missing from these manuals. Since PIG is the reference model for

most clinical labs and the focus of the study was on the impact of better joint centre location

using CT data, the approach taken was to first build a replica of the PIG model and then modify

this model so that it utilised data derived from CT data using Mimics and 3-matic.


28

Thus, as the CT-derived model (CTM) was based on a replica model of the original Plug-in-

Gait (PIG) model, the accuracy of this replica (PIGr) had to be checked beforehand. Ten trials

per patient and per assessment were imported in the Vicon Polygon module. After filtering for

outliers, left and right average joint kinematics were calculated for each assessment. The

averages were plotted to allow visual inspection of the differences between the kinematics

produced by each model.

Inspection of the pre-surgery kinematics (the graphs can be found in Appendix 6) showed that

the PIG and the PIGr-model overall gave the same results. Perhaps of note, the PIG and PIGr

graphs are almost perfectly overlapping in the patient with a normal BMI, but tend to slightly

differ in the other patients with much higher BMI, especially in the sagittal and frontal hip and

knee kinematics.

These kinematic joint values were exported and for each trial, for each point in the gait cycle

the average absolute differences between the PIG model and the replica were determined (see

Figure 4). The overall average of the absolute differences (PIG-PIGr) was set at 2.395 degrees.

The replica model was most accurate in copying the PIG model in the pelvic angles (average

differences of 0.663°, 0.777° and 1.241° for the 3 patients which averages 0.849°). Hip

(2.171°), knee (3.259°) and foot or ankle (3.258°) angles were less accurate. Frontal plane

angles were most identical to the PIG model (1.182°), although also angles in the sagittal plane

(1.989°) stayed under an absolute difference of 2°.

Figure 4: Average absolute differences between the generic PIG-model (Plug-in-Gait) and the replica

The kinematics derived from PIG and the replica model were subjected to statistical analysis

to test for differences. Thirthy-four key kinematic summary variables were used. After filtering

0,663°

0,777°

1,241°

1,965°

0,559°

3,990°

2,078°

2,211°

5,488°

3,247°

4,354°

2,171°

2,395°

Pelvic tilt

Pelvic obliquity

Pelvic rotation

Hip flexion/extension

Hip ab/adduction

Hip rotation

Knee flexion/extension

Knee ab/adduction

Knee rotation

Ankle dorsi/planter flexion

Ankle rotation

Foot progression angle

Average


29

for outlying values, 86 values per kinematic variable could be analysed in pairs, one set for

each kinematic model.

A paired samples T-test (PIG-PIGr) was used to detect systematic differences between the two

models (see Appendix 7). Fourteen parameters were found to be significantly different

(significance taken at the 95% confidence level i.e. p<0.05). Especially ankle joint kinematics

in the sagittal plane were significantly different, as peak ankle plantar flexion (mean difference

of -2.716°), peak ankle dorsiflexion, both in stance (-2.324°) and in swing (-2.190°) and ankle

position at initial contact (-2.083°) all had p-values less than 0.001 (highly significant).

The average absolute difference for the summary variables was 0.883°. The absolute

differences were used to avoid negative values compensating for positive values. Differences

smaller than 1° are however considered rather too small to be clinically relevant.

The kinematics derived from each model were also tested for correlation (Pearson) and

agreement (ICC(3,1)). The kinematics were found to be highly correlated with 29 of the 34

variables showing significant statistical results (with p-values less than 0.001 for the Pearson

correlation test and F-test for the intraclass correlation). Twenty kinematic variables (55.5%)

had Pearson and intraclass correlations higher than 0.80 (nearly perfect correlation). Only four

summary parameters (11.1%) were not significantly correlated, i.e. range pelvic tilt, peak and

range of pelvic rotation and peak knee external rotation. The four variables that missed

significance all had negative ICC values in their confidence interval4.

Based on the results of the T-testing and the correlation values, it could be concluded that the

replica model was accurate enough to use as a basis for developing a subject-specific CT-

model. Average absolute differences were after all clinically negligible and nearly all summary

variables were highly correlated and showed good agreement.

3.2.3 Comparison of the generic model with a subject-specific CT-based model

3.2.3.1 Comparison of the kinematics

A similar approach was used to compare the subject-specific CT-model (CTM) with the PIG.

However, because the knee prosthesis in itself caused an extra variation, the pre- and post-

surgery data was split and only the pre-surgery data was analysed in this context.

4 Negative ICC estimates indicate the true ICC is low, which means the intra-group variance is higher than the

inter-group variance.


30

Again, to make a first comparison between the models, the Vicon graphs of the pre-operative

trials were studied (see Appendix 8). At first sight, for each patient a clearly defined difference

can be seen between the PIG model and the CTM in almost every joint and plane, some

differences being greater than others. For the pelvic rotation, ankle dorsi/plantar flexion and

the foot progression angle both models were more comparable. In addition, the degree of

difference between the models was not alike in each patient individually. The differences

between the two models appear to be patient specific and/or rather BMI specific with greater

differences in the patients with higher BMI. This is true for pelvic tilt, pelvic obliquity, pelvic

rotation, hip flexion/extension, hip ab/adduction, knee flexion/extension, ankle dorsi/plantar

and foot progress angles. For the other parameters there is a clear difference between the results

visible for all of the patients.

To statistically ratify these observations, the same 34 kinematic summary variables were

calculated for the CTM and compared with the generic PIG-model by the same methods as

described above. The mean differences (PIG-CTM) were calculated for each summary variable

for each patient (see Appendix 9). For each subject (BMJ, DBM and DRM respectively), the

paired samples T-test was significant in 21 (61.8%), 17 (50%) and 19 (55.9%) of the 34

variables. The second patient (non-obese) overall did better. Not only were there fewer

variables with significant difference, but with a mean absolute difference of less than 4°, she

scored only half of the other two subjects (both obese). Her ankle and foot kinematics however,

were worst.

Nine (26.5%) of the summary variables were significantly different in all three of the patients.

Peak hip flexion, hip rotation and peak knee rotation kinematics had significant differences, as

had knee flexion/extension ROM, peak knee varus and peak ankle external rotation. When

analysing the data of all the three patients together all of the hip parameters were significantly

different between the models, except for the peak hip adduction. Peak knee extension and peak

knee flexion in loading response were the only knee values that weren’t. With none of the ankle

kinematics significant, this joint clearly was more alike between the two models.

In contrast to the comparison of the PIG with the replica, high correlation values were seldom

when comparing the PIG with the CTM (see Appendix 10). An average Pearson correlation of

0.292 and intraclass correlation of 0.191 was found, both corresponding to a poor agreement.

Averaged for the three patients, ICCs for each variable were smaller than the Pearson

correlations. It should nevertheless be noted that large individual differences were found in the


31

correlations values. When looking at the Pearson values, BMJ had 15 at least strongly

correlated variables. DBM and DRM only had 8 of them. BMJ, however, also had as many as

6 strongly negative correlated parameters. Of all the variables that had Pearson correlations

larger than 0.41 (moderate correlation) only the peak foot progression angle (0.762, 0.500 and

0.510) was moderately correlated in each of the three subjects. None of the summary

parameters were significantly different from 0 (no correlation) in all three. Not one joint or

movements in one plane did notably better or worse in all three.

No summary parameter had intraclass correlations higher than 0.41 (moderate correlation) in

all three of the patients. Again, no joint or plane did markedly well or worse in the ICCs of all

three, but moreover, just two (BMJ) and four (DBM and DRM) at least strongly correlated

ICCs were found. Conversely, many negative ICC-values were found (12 for BMJ, 17 for DBM

and 9 for DRM). Typically, negative ICCs occur when the between-subject variation (between

the models) is small compared to the within-subject variation. In this case, the variation of the

summary parameters is relatively large compared to the difference observed between the two

models. These ICCs are therefore not quotable. Not taking into account these negative values,

the average ICC increased to 0.269, 0.348 and 0.236 for the three patients and 0.241 for the

grouped values, still corresponding to poor correlation. For the grouped determination (the

three patients combined) of the summary parameters, the corresponding correlations are

outlined in the Table 9 and Figure 5.

Pelvis Hip Knee Ankle

No

(<0.00)

Range pelvic obliquity Peak hip internal rotation

Peak hip flexion

Range knee rotation Peak ankle internal

rotation

Poor

(0.00–0.40)

Peak and range of pelvic

tilt

Peak hip abduction

Range hip

abduction/adduction

Peak hip adduction

Peak hip extension

Peak hip external

rotation

Peak knee varus

Peak knee external

rotation

Peak knee flexion in

stance and in swing

Peak knee extension

Peak ankle plantarflexion

and dorsiflexion in swing

and in stance

Range of dorsi/plantar

flexion

Ankle position at initial

contact

Range ankle rotation

Peak ankle external

rotation

Moderate

(0.41–0.60)

Peak pelvic obliquity Range hip rotation

Range hip

flexion/extension

Range knee

flexion/extension

Range knee varus/valgus

Peak knee internal

rotation

Peak knee valgus

None

Strong

(0.61–0.80)

Peak and range of pelvic

rotation

None

None None

Almost perfect

(0.81–1)

None

None None Peak foot progression

angle

Table 9: Interpretation of correlation scores (PIG model versus CT-based model) for each kinematic summary variable. The

average value of the Pearson and intraclass correlation coefficients (all three patients combined) were used.


32

Figure 5: Pearson and intraclass correlations for the summary variables when comparing the generic PIG-model with the

CT-based model (CTM). The average values for the three patients combined were used.

Putting the results in a physical context - the average absolute difference between the models

was 5.794° for all the variables. Just under half of them had differences larger than 4°. Hip

differences were largest (absolute difference of 9.826°), but also knee differences were clearly

elevated (8.157°). Pelvic (2.843°) and ankle and foot (1.101°) kinematic values differed less

between the models. Average differences in peak values (7.356°) were more than doubled

versus differences in range of motions (2.951°). Except for the non-obese patient (DBM)

similar observations were made for each patient individually (see Table 10).

-0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0

Average

Range pelvic obliquity (°)

Peak hip flexion (°)

Peak hip internal rotation (°)

Peak ankle internal rotation (°)

Range knee rotation (°)

Peak ankle plantarflexion (°)

Peak hip external rotation (°)

Range pelvic tilt (°)

Peak knee flexion in swing (°)

Peak ankle external rotation (°)

Range ankle rotation (°)

Ankle position at initial contact (°)

Peak ankle dorsiflexion in stance (°)

Peak hip extension (°)

Peak knee extension (°)

Peak knee flexion in stance (°)

Peak hip adduction (°)

Peak knee external rotation (°)

Peak knee varus (°)

Range hip abduction/adduction (°)

Range of dorsi/plantar flexion (°)

Peak hip abduction (°)

Peak ankle dorsiflexion in swing (°)

Peak pelvic tilt (°)

Peak knee valgus (°)

Peak pelvic obliquity (°)

Peak knee internal rotation (°)

Range knee varus/valgus (°)

Range hip flexion/extension (°)

Range hip rotation (°)

Range knee flexion extension (°)

Peak pelvic rotation (°)

Range pelvic rotation (°)

Peak foot progression angle (°)

Intraclass correlation Pearson Correlation


33

BMJ DBM DRM ALL

Pelvis 3.759 0.867 3.676 2.844

Hip 9.510 2.580 13.380 9.826

Knee 10.636 6.870 11.914 8.157

Ankle/foot 6.579 3.841 3.450 1.101

Peak 10.365 3.935 10.822 7.357

ROM 3.341 2.889 3.852 2.951

Peak/ROM (%) 310% 136% 281% 249%

Table 10: Average absolute differences between the generic model (PIG) and the CT-based subject-specific model (CTM) per

joint and for peak and range of motion (ROM) values (only pre-surgery values were used)

Sagittal and frontal knee ROMs were determined (see Figure 6). Pre-surgery knee ROM in the

sagittal plane was on average 39.0° (SD=5.9°) with the PIG model and 47.7° (SD=8.7°) with

the CTM (p<0.001). This increase was observed in each of the three patients and was moreover

highly significant in each of them (p<0.001). On the pre-surgical data, the frontal knee ROM

however declined from 11.2° (SD=4.67°) with the PIG model to 9.6° (SD=3.61°) with the CTM

(p<0.05). The same was true for the individual subjects (p<0.05 for BMJ and p<0.001 for

DBM), except for DRM, where the frontal ROM in the CTM even increased, although slightly

(1.6°) and not significantly (p=0.162). These two observations (increase in sagittal ROM and

decrease in frontal ROM) could suggest minimised cross-talk, a feature of a more accurate

model.

Figure 6: Box-and-whisker plot for knee sagittal and frontal range of motion (ROM). PIG: generic Plug-in-Gait model;

CTM: CT-based subject-specific model. Outlying values are in circles (mild) and asterisks (extreme).


34

3.2.3.2 Comparison of the positions of anatomical landmarks

3.2.3.2.1 Hip joint centres

To detect the underlying differences in kinematics between the PIG model and CTM, four trials

per assessment were picked out and, of each of them, the marker coordinates of a full right gait

cycle were exported and analysed. The distances between the two hip joint centres were

calculated for every point in the gait cycle, both for the CTM as for the PIG model. The average

distances and their standard deviations were determined (see Figure 7). When analyzing the

three patients, the PIG model located the two hip joint centres on average 247.0 mm (SD=8.5)

apart, whereas the CT model only separated the two by 182.5mm and moreover with a lower

variation (SD=5.7). These differences (n=24) were highly significant when tested in pairs (t-

value=-5.482, p<0.001).

Interestingly, when subjects were split based on their BMI, the results dramatically changed.

For the one non-obese subject, the average difference only differed 2.85mm (197.68mm for

the PIG model and 194.83mm for the CTM), although the variation in the PIG, mainly due to

the post-surgery data, was higher (SD of 17.60mm versus 0.55mm). The paired T-test didn’t

reveal any significant changes in this patient (p=0.650). In the two obese patients, the difference

in distance between the hip joint centres increased to almost 10 centimeters between the models

(difference of 95.27mm, corresponding to p<0.001).

Figure 7: Average distances between the left and right hip joint centres (HJC) for each model (PIG: generic Plug-in-Gait;

CTM: CT-based subject-specific model)

Furthermore, the position of the hip joint centres (HJC) were determined for both models (see

Table 11). The average distance between the two was 82.52mm (78.36mm for the left and

86.68mm for the right HJC), although these distances were notably smaller in the non-obese


35

patient (37.58mm and 39.29mm) and went higher than 10cm in the other two. The standard

deviations in DBM also were smaller (around 6mm versus up to triple that value in the others).

The general trend was a CTM-HJC medially, posteriorly and downwards relative to the PIG-

HJCs. Exceptions were the pre-surgery assessment of DBM (although differences were also

smallest here), which was located laterally instead of medially, and the post-surgery trials of

DRM (where it appears some problems arose affecting the tilt of the sacral plate’s position in

the scanner – see 3.3.1), which projected the CTM-HJC anteriorly and upwards relative to the

PIG-HJC.

ID & moment Left CTM-HJC to left PIG-HJC Right CTM-HJC to right PIG-HJC

Distance (mm) Direction Distance (mm) Direction

BMJ pre 75.34 med post down 112.99 med post Down

BMJ post 129.60 med post down 129.76 med post Down

DBM pre 42.73 lat post down 30.90 lat post Down

DBM post 32.42 med post down 47.68 med post Down

DRM pre 120.93 med post down 135.40 med post Down

DRM post 69.16 med ant up 63.35 med ant Up

average 78.36 86.68

Table 11: Distances between the generic hip joint centres (PIG-HJC) and the CT-derived HJCs (CTM-HJC). Direction:

position of the CTM-HJC relative to the PIG-HJC (med: medially; lat: laterally; ant: anteriorly; post: posteriorly; down:

downwards; up: upwards)

Obesity did not only increase the differences but also affected variation with gait pattern (see

Figure 8). As can be seen in the graphs (plotted for a right gait cycle), the distances between

the centres derived from each model varied with gait, mainly in the two obese patients (left and

right graphs). Most likely, in the PIG model, soft tissue movement increased the inaccuracy in

determining the position of the hip joint centres.

Pre-surgery left Pre-surgery right Post-surgery left Post-surgery right

Figure 8: Average differences in distance between the two hip joint centres derived from each model (PIG and CTM). Time in x-axis

(right gait cylce), distance in mm in y-axis.

0

50

100

150

200

BMJ

0

50

100

150

200

DBM

0

50

100

150

200

DRM


36

The relative position of the hip joint centres was also evaluated as a percentage of the interASIS

distance (a la Bell et al.) The values, both for the PIG model as compared to the CTM, are

outlined in the graphs in Figure 9.

An average anteroposterior (AP) location of 15.9% was found for the PIG model, although the

individual with a normal BMI scored higher (18.7%) than the other two, who were obese

(14.6% and 14.2%). This is in line with the findings of Bell et al., who already concluded that

error in the AP direction was greatest, due to the inaccuracy of locating the bony ASIS

structures from skin markers. The mediolateral (on average 11.1%) and distal (30.3%)

positioning of the PIG-derived hip joint centres was more accurate. Nevertheless, in one of the

obese patients (BMJ) an average mediolateral (ML) position of only 4.4% was found with the

PIG model.

Especially on the difficult AP positioning, the CTM more closely approached the predicted

positions by Bell. In the two obese patients the AP distance in the CTM on average rose by

3.3% and 9.7% (respectively to 17.9% for BMJ and 23.9% for DRM) with respect to the PIG-

positions. With an average ML position of 10.4% and distal position of 32.0% of the interASIS

distance the CTM performed in the same ranges as the PIG model.

Figure 9: Bar charts for the PIG- (left) and CTM- (right) derived distances of the hip joint centres as a percentage of the

distances between the anterior superior iliac spines (ASIS) (according to the Bell model). Percentages of ASIS distances

in left y-axis, hip centres and timing (pre or post-surgery assessment) in right axis. Data lable values are in percentages

(rounded). Y-reference lines are based on Bell’s prediction values (22% posteriorly, 14% medially and 30% distally) for

a normal adult.. AP: anteroposterior; ML: mediolateral


37

InterASIS distances were clearly larger in the PIG model than when they were measured on

the CT images. The manually measured distances, done before every assessment, were even

greater on average (see also Table 12). In the obese, PIG interASIS distances (as a percentage

of the CTM distances) were 141% to 178% of the CTM-interASIS, whereas in the individual

with a normal BMI, these distances were just 103% to 119% of the CTM.

Manually measured PIG CTM Distance % of CTM Distance % of CTM Distance

BMJ pre 355 161% 322.71 146% 221.06

BMJ post 390 169% 323.94 141% 230.15 DBM pre 265 109% 250.22 103% 243.68

DBM post 295 119% 294.49 119% 247.38 DRM pre 295 134% 315.38 143% 219.95

DRM post 360 162% 395.52 178% 222.23 Table 12: Distances between the anterior superior iliac spines for every model and when manually measured. Distances in

mm. PIG: generic Plug-in-Gait model; CTM: CT-based subject-specific model.

3.2.3.2.2 Knee joint centres

The same procedure as in the hip joint centres was followed to determine the relative position

of the knee joint centres (KJC) (see Table 13). The average distance between the KJCs of each

model was 25.94mm (28.76mm for the left and 23.12mm for the right KJC). In contrast to the

HJCs, these distances weren’t remarkably elevated in the two obese patients. In fact, the

average differences in distances in BMJ (14.70mm) were half those of the other two (30.85mm

for DBM and 32.27mm for DRM). The relative position of the CTM-KJCs was less consistent

than the hip joint centre positions, i.e. not in every trial of each assessment the same directions

were always found (see double quotations of directions in Table 13).

ID & moment Left CTM-KJC to left PIG-KJC Right CTM-KJC to right PIG-KJC

Distance (mm) Direction Distance (mm) Direction

BMJ pre 19.79 lat post Down 12.72 lat ant down

BMJ post 16.51 lat ant Down 9.79 lat ant/post down

DBM pre 27.37 lat ant Up 25.10 lat ant down

DBM post 33.40 med ant down 37.55 med ant down

DRM pre 39.33 med/lat ant down/up 42.90 med/lat ant up

DRM post 36.18 med ant Up 10.65 med ant down

Average 28.76 23.12

Table 13: Distances between the generic knee joint centres (PIG-KJC) and the CT-derived KJCs (CTM-KJC). Direction:

position of the CTM-KJC relative to the PIG-KJC (med: medially; lat: laterally; ant: anteriorly; post: posteriorly; down:

downwards; up: upwards)

3.2.4 Comparison of the pre and post-surgical assessments

When comparing pre- and post-surgery data, it must be noted that one patient, BMJ, although

also already 6 months post-surgery, still walked with some pain. The other two patients walked

pain free.


38

3.2.4.1 Comparison of the temporal gait parameters

When comparing the temporal parameters (walking speed, step length and width, stride length

and cadence) no clear differences were found between the two models, nor between the left

and right or the operated versus the non-operated side (see 0 for the full table). This would be

expected given that the spatial temporal parameters are believed to be, in the case of PIG,

derived from the ankle markers alone in both models.

For both the PIG model and CTM, the cadence and the step and stride length post-surgery

increased for the two pain free patients, therefore their walking speed improved considerably

(both p<0.001). Moreover, for DRM the walking speed more than doubled (from 0.45m/s to

0.96m/s – p<0.001). In the patient who, post-surgically, walked with pain, walking speed even

decreased slightly (from 0.77m/s to 0.72m/s).

Differences in step width were minimal. An average difference of only 0.01m was found.

Nevertheless, these differences were significant in two of the three patients (both p<0.05). In

one of those patient (who was pain free) the step width post-surgery was on average 0.03m

smaller. In the other patient (who walked with pain), however, the step width was 0.03m wider

in the post-surgical assessment.

Figure 10: Box-and-whisker plot for the walking speed (m/s) compared between the pre- (blue) and post- (green) surgical

assessment. (Mild) outliers in circles.


39

3.2.4.2 Comparison of the kinematics

3.2.4.2.1 PIG

Comparing the pre- and post-surgery data of the PIG model, some differences between the hip

and knee parameters were noticeable (see Appendix 12).

For two patients, BMJ and DBM, post-surgically peak hip extension was larger on both legs

with a mean difference of respectively 11.9° and 7.3° (p<0.05). On the other hand, peak hip

extension in DRM was significantly (p<0.01) smaller on both legs (-9.7°). Post-surgically,

sagittal hip ROM increased with an average 10.4° in the pain free patients (p<0.001). In the

patient who walked with pain this difference was only 1.5°.

In the pain free patients there was an increase in sagittal knee ROM (9.4° for DBM and 6.1°

for DRM) and peak knee flexion in swing (4.6° for DBM and 10.8° for DRM) (p<0.05). In the

patient who walked with pain both these values were larger in the pre-surgical assessment

(mean differences of 5.4° for sagittal knee ROM and 15.0° for the peak knee flexion in swing).

3.2.4.2.2 CTM

When comparing the pre- and post-surgery kinematics of the CTM, the post-surgery data of

DRM was left out. Her kinematics were altered in a surprisingly large way, probably too big

to be correct. A large, unexplainable gap in especially the pelvic and hip sagittal and frontal

kinematics was found. Every step in the process was redone, but no errors could be found. An

altered relative position of the sacral plate in the CT versus its position in the gait lab was the

only possible explanation. The positioning of the sacral plate is therefore one of the major

elements in the Discussion-section of this thesis.

Leaving DRM out, in both other patients peak hip flexion and sagittal hip ROM post-surgically

increased on both sides, with respectively an average difference of 8.4° and 10.8° (p<0.05). In

the patient who walked with pain the peak hip extension post-surgically was 6.0° less (p<0.05).

On the operated side in DBM all sagittal knee summary parameters increased post-surgically,

i.e. mean differences of 6.8° for the peak knee extension, 16.3° for the peak knee flexion in

swing, 10.7° for peak knee flexion in stance (all p<0.001) and 9.5° for the ROM (p<0.05). In

BMJ however, those four values were smaller post-surgically, i.e. respective mean differences

of 4.1°, 10.6°, 9.6° and 6.5° (all p<0.05).

It could be concluded that the post-surgical outcomes between the models were visually

different, although these differences are not clearly defined as the outcomes strongly vary

Experimental evaluation: Discussion

40

between the different patients. Pain however, proved to have a major impact on various

kinematic variables.

3.3 Discussion

3.3.1 Methodological considerations

The study has a number of limitations. First of all, the sample size was very small. Initially, it

was hoped to include as many as ten patients, but in the current setting, in- and exclusion

criteria were perhaps too strict to be compatible with finding ten appropriate patients since the

number of patients scheduled for knee replacement wasn’t enormous (some 2-3 patients every

week, over all age groups and pathologies). Age was the main reason for exclusion, but also

other pathologies that could affect gait were common, especially in the elder age group.

However, in this way, it was possible to focus solely on unilateral knee OA patients. For the

purpose of this pilot study and for the development and rating of the subject-specific model,

this might perhaps not have been necessary after all. Nevertheless, in this way, patient data was

more or less comparable between subjects and each subject offered a control knee against

his/her own operated knee. Pain, although found to have a major impact on kinematic variables,

was not assessed in a quantitative way.

Apart from the small study group, sampling wasn’t random nor blindly controlled. Although

probably not yet applicable for this pilot study, this should be taken into account for further

research as this limits generalizability.

Furthermore, the positioning of the patient in the CT could be questioned. CT imaging was

carried out with the patient in the supine position and this is likely to affect posture (e.g. the

effect of body weight on joint spaces and position). No conversion ratio are available to date.

Therefore, when transferring the processed imaging to the gait lab no transformation of data

was applied.

Also, there wasn’t any control to check if the position of the plate (especially the sacral one) in

the scanner was the same as in the gait lab. Because of the supine position of the patient, it was

plausible that the plate could e.g. be tilted and thus induce an error in alignment of the bony

structures in the CTM. This was exactly what probably happened in the post-surgery

assessment of DRM (see 3.2.4.2.2). Besides, it should be noted that the smaller the plate, the

less tilt in the CT was enough to produce a larger error in the gait lab. The sacral plate and its

position and dimensions should thus be carefully thought through in light of the results of this

study.


41

3.3.2 Health economics

Gait analysis is time consuming and relatively expensive [55]. Even with an experienced

clinician a standard, but thoroughly performed gait assessment and analysis takes several hours.

Adequate clinical scores are definitely more economically reasonable for the general

population [55]. Nevertheless, post-operatively no significant correlation was found between

the already widely used questionnaires and gait parameters, so abandonment of gait analysis is

not yet recommended [55]. Another advantage is that gait analysis is more objective and not

dependent on patient experience [14].

The use of acceleration-based gait analysis (simplified gait analysis outside the lab) was

proposed and could diminish costs drastically. However, no correlation has been found with

clinical scores [95]. In any case, the value of directly measure kinematics and kinetics in a

controlled environment to assess and provide feedback to the surgeon on the knee alignment

was the underlying clinical drive behind this research. The community base assessment tools

are better suited to assessing functional outcome from the patient perspective which is related

to a number of other factors than just the surgical alignment.

The proposed technique in this pilot study (to combine CT data with gait data) is definitely

more expensive - and (especially) time-consuming. Manually segmenting and further

processing of the medical images took about two to three hours per scan. Processing software,

like Mimics, are very convenient when high resolution scans (with high detail and large

contrasts between tissues) are available. This however, requires an immense radiation dose,

which could be possible in cadaveric studies, but because of ethical considerations isn’t

conceivable in living patients (see also next point). Hence, in this study, a low dose scanning

protocol was used, which meant the processing mainly had to be done by hand. Just because

of time management issues, the current means of work are therefore difficult to implement in

everyday practice. Nevertheless, more practically feasible and more attractive methods have

already been described [96] which offer some hope of reducing costs in this respect in future.

3.3.3 Radiation dose

Radiation dose per person from medical X-ray imaging has drastically increased the last few

decades. Every unnecessary exposure to (medical imaging) radiation is undoubtedly to be

avoided, mainly because of issues with carcinogenicity (not to mention on grounds of health

economics). The use of CT in gait analysis in this pilot study is therefore restricted to older

patients (hence the exclusion criteria of age) in whom imaging was planned anyway. Strict


42

monitoring of received radiation dose carried out at all times and unnecessary testing should

always be avoided. A similar approach to combining imaging data and gait analysis has been

described, but with MRI, instead of more harmful CT and this could provide a solution [19, 28,

81], although problems of availability of machinery could arise alongside other contra-

indications (pacemakers, orthopedical pins or plates, cranial clips,..).

3.3.4 Conclusions and future work

In conclusion, it was found that a replica model of the original kinematic gait model could

successfully be developed, accurate enough to be used as a basis for a subject-specific CT-

based model. The absolute differences were clinically negligible and nearly all kinematic

summary parameters were highly correlated and showed good agreement. Building upon this

replica, subject-specific CT data was imported and the kinematics derived from this new model

were compared with the original, generic kinematic data.

In each patient, over half of the kinematic summary variables were significantly different

between the models. A quarter of these variables were even altered in all three of them.

Especially hip (sagittal and transverse) and knee (sagittal ROM, frontal and transverse)

kinematics were clearly unequal between the models. In the obese, also pelvic differences stood

out. Moreover, nearly all kinematic summary variables had no to moderate correlation between

the models, although large individual differences were found (in an albeit small sample). Those

that did agree at least moderately in each patient individually, were not found correlated in the

other patients. Even more, no single summary parameter was found with correlations values

larger than zero in each of the three patients.

It should be noted that the difference between the obese and non-obese patients was striking.

In the subject with a normal BMI, ankle and foot kinematics showed the greatest difference

between the models, but the other joint kinematics (i.e. pelvis, hip and knee) differed less than

in the obese patients. Overall, in the non-obese patient, the absolute differences in joint angles

were half those of the obese patients. Differences in peak kinematic values were more than

double the differences in range of motion, except again for the non-obese patient where this

difference was smaller. Comparing the knee kinematics of the CT-model with the generic knee

kinematics, an increase in sagittal range of motion and decline in frontal range of motion

suggested minimized crosstalk, a feature of improved accuracy.


43

Furthermore, distances between left and right hip joint centres were on average 25% less based

on the CT measurements compared to those in the currently used PIG model. In general, the

CT-derived hip joint centres were located 8.25cm medially, posteriorly and downwards relative

to their generic counterparts. Again, obesity did increase these differences and it even affected

variation with gait pattern. Comparing these findings with the predictions provided by Bell et

al., showed that the CTM was not just different, but furthermore (especially in the obese) more

accurate and correct in its positioning of the hip joint centres (mainly in its anterioposterior

positioning). In the generic model, distances between the two anterior superior iliac spines were

more than one and a half times those based on the CT measures. CT-derived knee joint centres

were mostly located anteriorly and downwards when compared to the generic centres, but this

was more variable. On average the knee joint centres of the two models were 2.59cm out of

each other. The positions of the knee joint centres do not appear to be related to BMI.

Between the pre- and post-surgical assessment it was noted that the presence of pain had a

major impact on both temporal and kinematic parameters. Walking speed drastically increased

in the pain free patients, but also step width seemed to be directly proportional with pain. This

could be a reflection of a subconscious attempt to reduce the knee adducting moments in the

presence of pain. Post-surgically, sagittal hip and knee ROM and peak knee flexion in swing

increased (in both models) in the patients free of pain. Moreover; using the CTM, all sagittal

knee kinematics increased after surgery. They didn’t significantly rise in the patient who

walked with pain.

In conclusion, it was found that the PIG gait model is probably incapable of accurately

describing kinematic patterns in elderly knee patients. Especially when obesity is present, the

differences between the models mount up and this disagreement undermines the credibility of

PIG in the presence of the analysis of joint centre locations and variation between the models.

Medical imaging-based models, it seems, could provide a practical answer to improving hip

and knee kinematics accuracy. Whilst this was only a pilot study with acknowledged

methodological limitations the flaws of the current PIG gait model were, in any case, clearly

exposed. The methods described above provide a basic framework for further research in the

use of medical imaging in constructing subject-specific gait models. It is now up to coming

research to further elaborate this potential.

Suggestions for future work include examining the use of MR in place of CT, the use of

larger, randomised sample sizes, to fully optimise the potential combined use of medical

imaging and gait analysis. Feasibility of using this subject-specific approach in daily practice


44

should be checked, as well as boosting simplification, ease of use and cost-reduction. It should

be closely determined under what circumstances and for which indications this framework, in

an economic and efficient way, could or could not be used.

List of figures

45

List of figures Figure 1: Positions of the plates attached to the patients' body ............................................... 21

Figure 2: Dimensions of the tibial plate ................................................................................... 22

Figure 3: Dimensions of the sacral plate .................................................................................. 22

Figure 4: Average absolute differences between the generic PIG-model (Plug-in-Gait) and the

replica ....................................................................................................................................... 28

Figure 5: Pearson and intraclass correlations for the summary variables when comparing the

generic PIG-model with the CT-based model (CTM) ............................................................. 32

Figure 6: Box-and-whisker plot for knee sagittal and frontal range of motion (ROM) .......... 33

Figure 7: Average distances between the left and right hip joint centres (HJC) for each model

.................................................................................................................................................. 34

Figure 8: Average differences in distance between the two hip joint centres derived from each

model (PIG and CTM). ............................................................................................................ 35

Figure 9: Bar charts for the PIG- (left) and CTM- (right) derived distances of the hip joint

centres as a percentage of the distances between the anterior superior iliac spines (ASIS)

(according to the Bell model) .................................................................................................. 36

Figure 10: Box-and-whisker plot for the walking speed (m/s) compared between the pre- (blue)

and post- (green) surgical assessment. ..................................................................................... 38

Figure 11: Example of how data was trimmed for extreme end values ................................... 57

Figure 12: Inter- and intra-observer variation in analysis in 3-matic ...................................... 59

List of tables Table 1: Endogenous and exogenous risk factors for osteoarthritis of the knee ....................... 8

Table 2: Etiologies of secondary osteoarthritis of the knee ....................................................... 8

Table 3: In- and exclusion criteria for gait study ..................................................................... 19

Table 4: Anthropometric data collected before gait assessment .............................................. 20

Table 5: Standard set of reflective markers used in lower body gait assessment .................... 22

Table 6: Extra sets of reflective markers screwed in holes of plates ....................................... 22

Table 7: General characteristics of the three included patients ............................................... 27

Table 8: Subject measurements before the pre- and post-surgery gait assessment ................. 27

Table 9: Interpretation of correlation scores (PIG model versus CT-based model) for each

kinematic summary variable .................................................................................................... 31

List of tables

46

Table 10: Average absolute differences between the generic model (PIG) and the CT-based

subject-specific model (CTM) per joint and for peak and range of motion (ROM) values .... 33

Table 11: Distances between the generic hip joint centres (PIG-HJC) and the CT-derived HJCs

(CTM-HJC) .............................................................................................................................. 35

Table 12: Distances between the anterior superior iliac spines for every model and when

manually measured .................................................................................................................. 37

Table 13: Distances between the generic knee joint centres (PIG-KJC) and the CT-derived

KJCs (CTM-KJC) .................................................................................................................... 37

Table 14: Percent variation of gait analysis in healthy subjects and knee OA patients ........... 58

Table 15: Mixed-design ANOVA to test the effect of the observer on the measurements done

in 3-matic ................................................................................................................................. 60

Table 16: Summary kinematic variables and the comparison between the generic PIG-model

and the replica model ............................................................................................................... 67


and the CT-based subject-specific model (CTM): Systematic differences by means of T-testing

.................................................................................................................................................. 71


and the CT-based subject-specific model (CTM): Correlation and agreement testing ........... 72

Table 19: Temporal parameters before and after surgery compared by means of T-testing ... 73

Table 20: Summary kinematic variables and the comparison between the pre- and post-surgical

assessment and between the generic PIG-model and the CT-based subject-specific model

(CTM): Systematic differences by paired samples T-testing (pre-post). ................................. 74

References

47

References

1. Guillemin F, Rat AC, Mazieres B, Pouchot J, Fautrel

B, Euller-Ziegler L, et al. Prevalence of symptomatic

hip and knee osteoarthritis: a two-phase population-

based survey. Osteoarthritis and cartilage / OARS,

Osteoarthritis Research Society. 2011;19(11):1314-22.

2. Suri P, Morgenroth DC, Hunter DJ. Epidemiology of

osteoarthritis and associated comorbidities. PM & R :

the journal of injury, function, and rehabilitation.

2012;4(5 Suppl):S10-9.

3. Holt HL, Katz JN, Reichmann WM, Gerlovin H,

Wright EA, Hunter DJ, et al. Forecasting the burden of

advanced knee osteoarthritis over a 10-year period in a

cohort of 60-64 year-old US adults. Osteoarthritis and

cartilage / OARS, Osteoarthritis Research Society.

2011;19(1):44-50.

4. Merashly M, Uthman I. Management of knee

osteoarthritis: an evidence-based review of treatment

options. Le Journal medical libanais The Lebanese

medical journal. 2012;60(4):237-42.

5. Van Manen MD, Nace J, Mont MA. Management of

primary knee osteoarthritis and indications for total

knee arthroplasty for general practitioners. The Journal

of the American Osteopathic Association.

2012;112(11):709-15.

6. Hatfield GL, Hubley-Kozey CL, Astephen Wilson

JL, Dunbar MJ. The effect of total knee arthroplasty on

knee joint kinematics and kinetics during gait. The

Journal of arthroplasty. 2011;26(2):309-18.

7. McClelland JA, Webster KE, Feller JA, Menz HB.

Knee kinematics during walking at different speeds in

people who have undergone total knee replacement.

The Knee. 2011;18(3):151-5.

8. Fitzpatrick CK, Clary CW, Rullkoetter PJ. The role

of patient, surgical, and implant design variation in total

knee replacement performance. Journal of

biomechanics. 2012;45(12):2092-102.

9. McClelland JA, Webster KE, Feller JA, Menz HB.

Knee kinetics during walking at different speeds in

people who have undergone total knee replacement.

Gait & posture. 2010;32(2):205-10.

10. Asay JL, Boyer KA, Andriacchi TP. Repeatability

of gait analysis for measuring knee osteoarthritis pain

in patients with severe chronic pain. Journal of

orthopaedic research : official publication of the

Orthopaedic Research Society. 2013;31(7):1007-12.

11. Stief F, Bohm H, Michel K, Schwirtz A, Doderlein

L. Reliability and accuracy in three-dimensional gait

analysis: a comparison of two lower body protocols.

Journal of applied biomechanics. 2013;29(1):105-11.

12. McGinley JL, Baker R, Wolfe R, Morris ME. The

reliability of three-dimensional kinematic gait

measurements: a systematic review. Gait & posture.

2009;29(3):360-9.

13. Davis RB, Ounpuu S, Tyburski DT, Gage JR. A gait

analysis data collection and reduction technique. Hum

Move Sci. 1991;10:575-88.

14. Ornetti P, Maillefert JF, Laroche D, Morisset C,

Dougados M, Gossec L. Gait analysis as a quantifiable

outcome measure in hip or knee osteoarthritis: a

systematic review. Joint, bone, spine : revue du

rhumatisme. 2010;77(5):421-5.

15. Andriacchi TP, Alexander EJ. Studies of human

locomotion: past, present and future. Journal of

biomechanics. 2000;33(10):1217-24.

16. Lucchetti L, Cappozzo A, Cappello A, Della Croce

U. Skin movement artefact assessment and

compensation in the estimation of knee-joint

kinematics. Journal of biomechanics. 1998;31(11):977-

84.

17. Benoit DL, Ramsey DK, Lamontagne M, Xu L,

Wretenberg P, Renstrom P. Effect of skin movement

artifact on knee kinematics during gait and cutting

motions measured in vivo. Gait & posture.

2006;24(2):152-64.

18. Sutherland DH. The evolution of clinical gait

analysis. Part II kinematics. Gait & posture.

2002;16(2):159-79.

19. Scheys L, Desloovere K, Spaepen A, Suetens P,

Jonkers I. Calculating gait kinematics using MR-based

kinematic models. Gait & posture. 2011;33(2):158-64.

20. Meldrum D, Shouldice C, Conroy R, Jones K,

Forward M. Test-retest reliability of three dimensional

gait analysis: including a novel approach to visualising

agreement of gait cycle waveforms with Bland and

Altman plots. Gait & posture. 2014;39(1):265-71.

21. Barre A, Thiran JP, Jolles BM, Theumann N,

Aminian K. Soft tissue artifact assessment during

treadmill walking in subjects with total knee

arthroplasty. IEEE transactions on bio-medical

engineering. 2013;60(11):3131-40.

22. Baker R, Finney L, Orr J. A new approach to

determine the hip rotation profile from clinical gait

analysis data. Human Movement Science.

1999;18(5):655-67.

23. Schwartz MH, Rozumalski A. A new method for

estimating joint parameters from motion data. Journal

of biomechanics. 2005;38(1):107-16.

24. Benedetti MG, Merlo A, Leardini A. Inter-

laboratory consistency of gait analysis measurements.

Gait & posture. 2013.

25. Gorton GE, 3rd, Hebert DA, Gannotti ME.

Assessment of the kinematic variability among 12

motion analysis laboratories. Gait & posture.

2009;29(3):398-402.

26. Reinbolt JA, Schutte JF, Fregly BJ, Koh BI, Haftka

RT, George AD, et al. Determination of patient-specific

multi-joint kinematic models through two-level

optimization. Journal of biomechanics.

2005;38(3):621-6.

27. Scheys L, Spaepen A, Suetens P, Jonkers I.

Calculated moment-arm and muscle-tendon lengths

during gait differ substantially using MR based versus

References

48

rescaled generic lower-limb musculoskeletal models.

Gait & posture. 2008;28(4):640-8.

28. Scheys L, Van Campenhout A, Spaepen A, Suetens

P, Jonkers I. 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. 2008;28(3):358-

65.

29. Smith M, Curtis D, Bencke J, Stebbins J. Use of

wand markers on the pelvis in three dimensional gait

analysis. Gait & posture. 2013.

30. Lafortune MA, Cavanagh PR, Sommer HJ, 3rd,

Kalenak A. Three-dimensional kinematics of the human

knee during walking. Journal of biomechanics.

1992;25(4):347-57.

31. Brand RA, Crowninshield RD. Comment on criteria

for patient evaluation tools. Journal of biomechanics.

1981;14(9):655.

32. Michael JW, Schluter-Brust KU, Eysel P. The

epidemiology, etiology, diagnosis, and treatment of

osteoarthritis of the knee. Deutsches Arzteblatt

international. 2010;107(9):152-62.

33. Liddle AD, Pegg EC, Pandit H. Knee replacement

for osteoarthritis. Maturitas. 2013;75(2):131-6.

34. de Rezende MU, de Campos GC, Pailo AF. Current

concepts in osteoarthritis. Acta ortopedica brasileira.

2013;21(2):120-2.

35. Sun R, Zhang B, Chen L, Sun J. Role of nuclear

factor of activated T cells 1 in the pathogenesis of

osteoarthritis. Experimental and therapeutic medicine.

2014;7(1):195-8.

36. Krasnokutsky S, Attur M, Palmer G, Samuels J,

Abramson SB. Current concepts in the pathogenesis of

osteoarthritis. Osteoarthritis and cartilage / OARS,

Osteoarthritis Research Society. 2008;16 Suppl 3:S1-3.

37. Hochberg MC. Epidemiology of osteoarthritis:

current concepts and new insights. The Journal of

rheumatology Supplement. 1991;27:4-6.

38. Metcalfe AJ, Stewart C, Postans N, Dodds AL, Holt

CA, Roberts AP. The effect of osteoarthritis of the knee

on the biomechanics of other joints in the lower limbs.

The bone & joint journal. 2013;95-B(3):348-53.

39. Cerejo R, Dunlop DD, Cahue S, Channin D, Song

J, Sharma L. The influence of alignment on risk of knee

osteoarthritis progression according to baseline stage of

disease. Arthritis and rheumatism. 2002;46(10):2632-6.

40. Smink AJ, Bierma-Zeinstra SM, Dekker J, Vliet

Vlieland TP, Bijlsma JW, Swierstra BA, et al.

Agreement of general practitioners with the guideline-

based stepped-care strategy for patients with

osteoarthritis of the hip or knee: a cross-sectional study.

BMC family practice. 2013;14:33.

41. McAlindon TE, Bannuru RR, Sullivan MC, Arden

NK, Berenbaum F, Bierma-Zeinstra SM, et al. OARSI

guidelines for the non-surgical management of knee


Osteoarthritis Research Society. 2014.

42. Satku K. Unicompartmental knee arthroplasty: is it

a step in the right direction?--Surgical options for

osteoarthritis of the knee. Singapore medical journal.

2003;44(11):554-6.

43. Choong PF, Dowsey MM. Update in surgery for

osteoarthritis of the knee. International journal of

rheumatic diseases. 2011;14(2):167-74.

44. Cappozzo A. Gait analysis methodology. Human

Movement Science. 1984;3(1–2):27-50.

45. Kirtley C. Clinical gait analysis : theory and

practice. Edinburgh ; New York: Elsevier; 2005. p. p.

46. Cappozzo A. Considerations on clinical gait

evaluation. Journal of biomechanics. 1983;16(4):302.

47. Gage JR. Gait analysis. An essential tool in the

treatment of cerebral palsy. Clinical orthopaedics and

related research. 1993(288):126-34.

48. Whittle MW. Clinical gait analysis: A review.

Human Movement Science. 1996;15(3):369-87.

49. Roislien J, Rennie L, Skaaret I. Functional limits of

agreement: a method for assessing agreement between

measurements of gait curves. Gait & posture.

2012;36(3):495-9.

50. Noonan KJ, Halliday S, Browne R, O'Brien S,

Kayes K, Feinberg J. Interobserver variability of gait

analysis in patients with cerebral palsy. Journal of

pediatric orthopedics. 2003;23(3):279-87; discussion

88-91.

51. McDermott A, Bolger C, Keating L, McEvoy L,

Meldrum D. Reliability of three-dimensional gait

analysis in cervical spondylotic myelopathy. Gait &

posture. 2010;32(4):552-8.

52. McClelland JA, Webster KE, Feller JA. Gait

analysis of patients following total knee replacement: a

systematic review. The Knee. 2007;14(4):253-63.

53. Moisio KC, Sumner DR, Shott S, Hurwitz DE.

Normalization of joint moments during gait: a

comparison of two techniques. Journal of

biomechanics. 2003;36(4):599-603.

54. Mather RC, 3rd, Hug KT, Orlando LA, Watters TS,

Koenig L, Nunley RM, et al. Economic evaluation of

access to musculoskeletal care: the case of waiting for

total knee arthroplasty. BMC musculoskeletal

disorders. 2014;15:22.

55. Liebensteiner MC, Herten A, Gstoettner M, Thaler

M, Krismer M, Bach CM. Correlation between

objective gait parameters and subjective score

measurements before and after total knee arthroplasty.

The Knee. 2008;15(6):461-6.

56. Andriacchi TP. Dynamics of knee malalignment.

The Orthopedic clinics of North America.

1994;25(3):395-403.

57. Sharma L, Hurwitz DE, Thonar EJ, Sum JA, Lenz

ME, Dunlop DD, et al. Knee adduction moment, serum

hyaluronan level, and disease severity in medial

tibiofemoral osteoarthritis. Arthritis and rheumatism.

1998;41(7):1233-40.

58. Sagawa Jr Y, Armand S, Lubbeke A, Hoffmeyer P,

Fritschy D, Suva D, et al. Associations between gait and

clinical parameters in patients with severe knee

osteoarthritis: A multiple correspondence analysis.

Clinical Biomechanics. 2013;28(3):299-305.

59. Kumar D, Manal KT, Rudolph KS. Knee joint

loading during gait in healthy controls and individuals

with knee osteoarthritis. Osteoarthritis and Cartilage.

2013;21(2):298-305.

References

49

60. Baliunas AJ, Hurwitz DE, Ryals AB, Karrar A,

Case JP, Block JA, et al. Increased knee joint loads

during walking are present in subjects with knee


Osteoarthritis Research Society. 2002;10(7):573-9.

61. Mundermann A, Dyrby CO, Andriacchi TP.

Secondary gait changes in patients with medial

compartment knee osteoarthritis: increased load at the

ankle, knee, and hip during walking. Arthritis and

rheumatism. 2005;52(9):2835-44.

62. Orishimo KF, Kremenic IJ, Deshmukh AJ, Nicholas

SJ, Rodriguez JA. Does total knee arthroplasty change

frontal plane knee biomechanics during gait? Clinical

orthopaedics and related research. 2012;470(4):1171-6.

63. Miyazaki T, Wada M, Kawahara H, Sato M, Baba

H, Shimada S. Dynamic load at baseline can predict

radiographic disease progression in medial

compartment knee osteoarthritis. Annals of the

rheumatic diseases. 2002;61(7):617-22.

64. Duffell LD, Southgate DFL, Gulati V, McGregor

AH. Balance and Gait Adaptations in Patients with

Early Knee Osteoarthritis. Gait & posture. (0).

65. Baert IAC, Jonkers I, Staes F, Luyten FP, Truijen S,

Verschueren SMP. Gait characteristics and lower limb

muscle strength in women with early and established

knee osteoarthritis. Clinical Biomechanics.

2013;28(1):40-7.

66. Kean CO, Hinman RS, Bowles KA, Cicuttini F,

Davies-Tuck M, Bennell KL. Comparison of peak knee

adduction moment and knee adduction moment impulse

in distinguishing between severities of knee

osteoarthritis. Clin Biomech (Bristol, Avon).

2012;27(5):520-3.

67. Meyer AJ, D'Lima DD, Besier TF, Lloyd DG,

Colwell CW, Jr., Fregly BJ. Are external knee load and

EMG measures accurate indicators of internal knee

contact forces during gait? Journal of orthopaedic

research : official publication of the Orthopaedic

Research Society. 2013;31(6):921-9.

68. Otsuki T, Nawata K, Okuno M. Quantitative

evaluation of gait pattern in patients with osteoarthrosis

of the knee before and after total knee arthroplasty. Gait

analysis using a pressure measuring system. Journal of

orthopaedic science : official journal of the Japanese

Orthopaedic Association. 1999;4(2):99-105.

69. Turcot K, Armand S, Lübbeke A, Fritschy D,

Hoffmeyer P, Suvà D. Does knee alignment influence

gait in patients with severe knee osteoarthritis? Clinical

Biomechanics. 2013;28(1):34-9.

70. Turcot K, Suvà D, Lübbeke A, Fritschy D,

Hoffmeyer P, Armand S. Influence of knee alignment

on gait in patients with knee osteoarthitis. Gait &

posture. 2013;38, Supplement 1(0):S8.

71. Catani F, Ensini A, Belvedere C, Feliciangeli A,

Benedetti MG, Leardini A, et al. In vivo kinematics and

kinetics of a bi-cruciate substituting total knee

arthroplasty: a combined fluoroscopic and gait analysis

study. Journal of orthopaedic research : official

publication of the Orthopaedic Research Society.

2009;27(12):1569-75.

72. Victor J, Bellemans J. Physiologic kinematics as a

concept for better flexion in TKA. Clinical orthopaedics

and related research. 2006;452:53-8.

73. Li K, Ackland DC, McClelland JA, Webster KE,

Feller JA, de Steiger R, et al. Trunk muscle action

compensates for reduced quadriceps force during

walking after total knee arthroplasty. Gait & posture.

2013;38(1):79-85.

74. Kiss RM, Bejek Z, Szendroi M. Variability of gait

parameters in patients with total knee arthroplasty.

Knee surgery, sports traumatology, arthroscopy :

official journal of the ESSKA. 2012;20(7):1252-60.

75. Hilding MB, Lanshammar H, Ryd L. Knee joint

loading and tibial component loosening. RSA and gait

analysis in 45 osteoarthritic patients before and after

TKA. The Journal of bone and joint surgery British

volume. 1996;78(1):66-73.

76. Benedetti MG, Catani F, Bilotta TW, Marcacci M,

Mariani E, Giannini S. Muscle activation pattern and

gait biomechanics after total knee replacement. Clin

Biomech (Bristol, Avon). 2003;18(9):871-6.

77. Metcalfe A, Stewart C, Postans N, Barlow D, Dodds

A, Holt C, et al. Abnormal loading of the major joints

in knee osteoarthritis and the response to knee

replacement. Gait & posture. 2013;37(1):32-6.

78. Levinger P, Menz HB, Morrow AD, Feller JA,

Bartlett JR, Bergman NR. Lower limb biomechanics in

individuals with knee osteoarthritis before and after

total knee arthroplasty surgery. The Journal of

arthroplasty. 2013;28(6):994-9.

79. Tibesku CO, Daniilidis K, Skwara A, Dierkes T,

Rosenbaum D, Fuchs-Winkelmann S. Gait analysis and

electromyography in fixed- and mobile-bearing total

knee replacement: a prospective, comparative study.

Knee surgery, sports traumatology, arthroscopy :

official journal of the ESSKA. 2011;19(12):2052-9.

80. Sikorski JM. Alignment in total knee replacement.

The Journal of bone and joint surgery British volume.

2008;90(9):1121-7.

81. Scheys L, Jonkers I, Schutyser F, Pans S, Spaepen

A, Suetens P. Image based methods to generate subject-

specific musculoskeletal models for gait analysis.

International Congress Series. 2005;1281(0):62-7.

82. Merolla G, Nastrucci G, Porcellini G. Shoulder

arthroplasty in osteoarthritis: current concepts in

biomechanics and surgical technique. Translational

medicine @ UniSa. 2013;6:16-28.

83. Arnold AS, Salinas S, Asakawa DJ, Delp SL.

Accuracy of muscle moment arms estimated from MRI-

based musculoskeletal models of the lower extremity.

Computer aided surgery : official journal of the

International Society for Computer Aided Surgery.

2000;5(2):108-19.

84. Smith DK, Berquist TH, An KN, Robb RA, Chao

EY. Validation of three-dimensional reconstructions of

knee anatomy: CT vs MR imaging. Journal of computer

assisted tomography. 1989;13(2):294-301.

85. Fregly BJ, Boninger ML, Reinkensmeyer DJ.

Personalized neuromusculoskeletal modeling to

improve treatment of mobility impairments: a

References

50

perspective from European research sites. Journal of

neuroengineering and rehabilitation. 2012;9:18.

86. Bouffard V, Begon M, Champagne A, Farhadnia P,

Vendittoli PA, Lavigne M, et al. Hip joint center

localisation: A biomechanical application to hip

arthroplasty population. World journal of orthopedics.

2012;3(8):131-6.

87. Kia M, Stylianou AP, Guess TM. Evaluation of a

musculoskeletal model with prosthetic knee through six

experimental gait trials. Medical engineering &

physics. 2014.

88. Taddei F, Martelli S, Valente G, Leardini A,

Benedetti MG, Manfrini M, et al. Femoral loads during

gait in a patient with massive skeletal reconstruction.

Clin Biomech (Bristol, Avon). 2012;27(3):273-80.

89. Oosterwaal M, Telfer S, Torholm S, Carbes S, van

Rhijn LW, Macduff R, et al. Generation of subject-

specific, dynamic, multisegment ankle and foot models

to improve orthotic design: a feasibility study. BMC

musculoskeletal disorders. 2011;12:256.

90. Dao TT, Marin F, Ho Ba Tho MC. Sensitivity of the

anthropometrical and geometrical parameters of the

bones and muscles on a musculoskeletal model of the

lower limbs. Conference proceedings : Annual

International Conference of the IEEE Engineering in

Medicine and Biology Society IEEE Engineering in

Medicine and Biology Society Conference.

2009;2009:5251-4.

91. Jonkers I, Sauwen N, Lenaerts G, Mulier M, Van

der Perre G, Jaecques S. Relation between subject-

specific hip joint loading, stress distribution in the

proximal femur and bone mineral density changes after

total hip replacement. Journal of biomechanics.

2008;41(16):3405-13.

92. Lenaerts G, Bartels W, Gelaude F, Mulier M,

Spaepen A, Van der Perre G, et al. Subject-specific hip

geometry and hip joint centre location affects calculated

contact forces at the hip during gait. Journal of

biomechanics. 2009;42(9):1246-51.

93. Fregly BJ, Besier TF, Lloyd DG, Delp SL, Banks

SA, Pandy MG, et al. Grand challenge competition to

predict in vivo knee loads. Journal of orthopaedic

research : official publication of the Orthopaedic

Research Society. 2012;30(4):503-13.

94. Bell AL, Pedersen DR, Brand RA. A comparison of

the accuracy of several hip center location prediction

methods. Journal of biomechanics. 1990;23(6):617-21.

95. Senden R, Grimm B, Meijer K, Savelberg H,

Heyligers IC. The importance to including objective

functional outcomes in the clinical follow up of total

knee arthroplasty patients. The Knee. 2011;18(5):306-

11.

96. Scheys L, Jonkers I, Loeckx D, Maes F, Spaepen A,

Suetens P. Image Based Musculoskeletal Modeling

Allows Personalized Biomechanical Analysis of Gait.

In: Harders M, Székely G, editors. Biomedical

Simulation. Lecture Notes in Computer Science. 4072:

Springer Berlin Heidelberg; 2006. p. 58-66.

Appendices

51

Appendices

Appendix 1 : Kellgren-Lawrence Grading Scale

The Kellgren-Lawrence Scale is an osteoarthritis grading scale. It is based on X-ray imaging.

The grade is dependent on the presence of three typical features of osteoarthritis (joint space

narrowing, osteophytes and sclerosis).

Grade 1: doubtful narrowing of joint space and possible osteophytic lipping

Grade 2: definite osteophytes, definite narrowing of joint space

Grade 3: moderate multiple osteophytes, definite narrowing of joints space, some

sclerosis and possible deformity of bone contour

Grade 4: large osteophytes, marked narrowing of joint space, severe sclerosis and

definite deformity of bone contour

Appendix 2 : Mimics and 3-matic manual

In handling the CT images, the software modules of Mimics (v16.0 – ©Materialise, Leuven,

Belgium) and 3-matic (v8.0 – ©Materialise, Leuven, Belgium) were used. In the following, a

brief overview of the method of work will be given. The used workflow was largely based on

previous work and trial-and-error. Getting to know the right tools and using them accordingly,

is quite challenging when having no experience with the software. The description below is

therefore quite technically and mainly directed to researchers in the future, as they could benefit

from a similar working method.

1. Segmenting 2D CT-images to 3D objects (Mimics)

a. Loading of CD

i. File > New project wizard > Search for DICOMDAT folder on CD drive > Select

Target Folder > Next…

ii. Select all + Merge selected studies (Compression: CT) > Convert > Open > Same table

position: OK > Orientation: OK

b. Segmenting images

i. 3D Livewire > Segment > Indicate contours of bony structures in two of three planes

(=> third dimension will be calculated).

ii. Contours should be indicated every few slices (you can leave out some of them in

between)

iii. If done > Segment (and wait) > Calculate 3D

iv. If necessary (split structures e.g. hip and femur): Segmentation > Region growing

(clicking a structure will add all points connected with it to a new mask)

Appendices

52

v. Adaptations are best done with Livewire in the axial plane (3D objects should be

recalculated afterwords)

2. Defining landmarks and markers in 3-matic

a. Loading of 3D-objects from Mimics into 3-matic

i. In Mimics: 3-matic > Model

ii. Select relevant 3D objects > OK > 3-matic file will be created

b. Create anatomical marks in 3-matic

i. Pelvis

1. Anterior superior iliac spines (LASI and RASI)

a. Primitives > Create point

b. Manually place point on most anterior point of left and right superior

iliac spine

2. Posterior superior iliac spines (LPSI and RPSI)

a. Primitives > Create point

b. Manually place point on most posterior point of left and right

superior iliac spine

ii. Hip

1. Femoral hip centre (FHC)

a. Mark > Wave Brush Mark > Mark surface of femoral caput (don’t

mark the collum)

b. Primitives > Create analytical sphere

c. Operations window > Fit sphere (based on Marked Triangles as a

Fitting Entity)

d. Centre of the sphere created is the FHC

iii. Knee

1. Centres of the femoral condyles (FMCC and FLCC)

a. Mark > Wave Brush Mark > Mark surface of (medial or lateral) joint

surface



Fitting Entity)

d. Centre of the sphere created is the FMCC or FLCC

2. Femoral knee centre (FKC)

a. Primitives > Create point > Manually place point as the most anterior

point in the middle of the fossa intercondylaris (femoral notch) on a

caudo-cranial view of the femur.

3. A similar approach could be used in the post surgery situation

iv. Tibia

1. Centres of the tibial condyles (TMCC and TLCC)

a. Primitives > Create arc

b. Mark three points on the most ventral, dorsal and lateral/medial side

of each condyle

c. Centre of the arc created is the TMCC or TLCC

2. Tibial knee centre (TKC)

Appendices

53

a. Primitives > Create point > Manually place point in the middle of

the two tuberculi intercondylare

3. A similar approach could be used in the post surgery situation, although an

adapted protocol was used for the TKC. The TKC was defined as the midpoint

between the midpoints of the lines connecting the most ventral and dorsal part

of each condyle

v. Ankle

1. Tibial ankle centre (TAC)

a. Mark > Wave Brush Mark > Mark bottom surface of tibiofibulotalar

joint space



Fitting Entity)

d. Centre of the sphere created is the TAC

2. Ankle medial and lateral epicondyles (LALE, LAME, RALE and RAME)

a. Primitives > Create point > Manually place point on most lateral and

medial point of the ankle epicondyles

c. Create markers of the plates

i. Create arc

1. Primitives > Create arc

2. Method: 3 points

3. Select three points at inner lining of hole in plate

4. OR (if hole is closed and resolution is too low): create point in centre of

closed hole

ii. Create plane

1. Mark plane

a. Mark > Brush Mark > Wave Brush Mark: Mark one side of the

plate fully

2. Create plane

a. Primitives > Create plane

b. Method: fit plane

c. Fitting entity: marked triangles (created in 1.)

3. Create cone

a. Primitives > Create cone

b. Method: Axis

c. Direction: one of the axis of the plane created in 2.): mostly Z-axis

- If necessary: inverse direction via button on the right (two arrows

pointing in different direction): ↔

d. Origin: centre of arc created in 1) or the point in the centre of the

closed hole

e. Height: 8mm

f. Bottom/Top radius: N/A

4. Create marker

a. Primitives > Create Analytical Sphere

Appendices

54

b. Make sure right cone is selected in Scene Tree

c. Centre point: Click on top of cone

d. Radius: 8mm

iii. Repeat for each hole (most accurate) or copy and translate cone

d. Save as 3-matic file

3. Exporting coordinates in text file

a. Re-importing 3-matic file in Mimics-file

i. In Mimics: File > Import project > Select 3-matic file and chose the relevant objects

and points

b. In Mimics: Export > Txt… > Medcad tab > Select all > Ok

c. Text file can be used in other software (e.g. gait analysis software modules)

Appendices

55

Appendix 3 : Dimensions of the tibial plate

Measurements are in millimeter or degrees.

Appendices

56

Appendix 4 : Dimensions of the sacral plate

Measurements are in millimeter or degrees.

Appendices

57

Appendix 5 : Assessment of reliability of gait lab data

Both the reliability of gait data and CT image collection was estimated by repeated

measurements by different observers. The results were necessary to interpret forthcoming

results.

The reliability of the gait lab procedure, as outlined in 3.1.2, was rated by repeated gait

assessments on the same two subjects on four different days. Also, the assessments of the three

patients, included for this pilot study, before surgery was used. For every assessment the three-

dimensional coordinates of each marker (120 times per second) of one trial were exported to

the statistical software package of SPSS Statistics (v21.0 - © IBM, Armonk, New York, U.S.).

The three-dimensional position of the centre of gravity of each plate was determined (it was

believed that calculating the average x-, y- and z-coordinates for each three plate-markers was

sufficiently accurate as an estimation). The distances of these centres of gravity to various

points of interest were calculated and plotted in a line graph. Data was trimmed for extreme

values, such as sometimes occurred at the ends of the graphs (see Figure 11 below). These

extreme values mainly arose at the outside borders of the observed zone, mainly because of

reduced reliability in camera capture.

BEFORE AFTER

Figure 11: Example of how data was trimmed for extreme end values (graphs before and after trimming)

After trimming for these extreme values, the graphs had a cyclic pattern. Thus, most probably,

the remaining variation could be assigned to the gait pattern. The percent variation, defined by

Appendices

58

the range of this variation divided by the mean, was calculated for all the trials. On average,

this percent variation was 6.68% (minimum 3.79%, maximum 9.58%). This meant that a

similar effect-size (~6%) at least would be necessary to determine a significant difference

between e.g. pre- and post-surgery. Largest values of percent variation were found for the

distances from (1) sacral plate to the posterior iliac spine markers and (2) the tibial plates to

the calculated knee centres and tibial markers. Worth the thought could be that, although the

percent variation for the distance sacral plate-posterior iliac spines was the largest, its range

was not. Even more, with 8.06mm and 8.08mm, respectively to the left and right posterior iliac

spine, it was below the overall average range of 10.49mm. The surprising percent variation

could largely be due to its relative short distance from plate to marker (~4-6mm instead of e.g.

~24-26cm from tibial plate to ankle markers).

There were small differences to be noted between healthy subjects and knee OA patients, but

the average percent variation was nearly the same (6.68% versus 6.69%). Moreover, the

Wilcoxon matched-pair signed-ranks test was not significant, both for the range (p=0.124) as

well as the percent variation (p=0.363). Hence, it could be concluded that both (small) groups

were more or less comparable. More detail can be found in the table below.

Healthy subjects Knee OA patients Total

From To Range

(mm) Percent

variation Range

(mm) Percent

variation Range

(mm) Percent

variation

Sacral plate

LFEP 7.51 3.36% 14.40 5.61% 9.81 4.11%

RFEP 7.06 3.17% 11.27 4.37% 8.46 3.57%

LPSI 9.85 14.42% 4.49 8.18% 8.06 12.34%

RPSI 9.81 13.64% 4.61 7.93% 8.08 11.74%

LASI 10.44 4.35% 16.07 5.59% 12.32 4.76%

RASI 10.57 4.46% 12.68 4.38% 11.27 4.43%

Left tibial

plate

LFEO 12.36 6.48% 15.21 7.94% 13.31 6.97%

LKNE 9.55 5.01% 11.31 5.77% 10.13 5.27%

LTIB 9.24 8.76% 7.05 6.56% 8.51 8.02%

LANK 8.77 3.78% 10.73 5.78% 9.42 4.44%

Right tibial

plate

RFEO 17.22 10.06% 20.23 10.97% 18.22 10.36%

RKNE 8.86 5.13% 12.48 6.59% 10.06 5.62%

RTIB 8.15 6.74% 8.48 7.11% 8.26 6.86%

RANK 9.47 4.14% 13.82 6.89% 10.92 5.06%

AVERAGE (average - SD,

average + SD) 9.92 (7.43,

12.40)

6.68%

(2.98%,

10.38%)

11.63

(7.22,

16.04)

6.69%

(4.97%,

8.42%)

10.49

(7.74,

13.24)

6.68%

(3.79%,

9.58%) Table 14: Percent variation of gait analysis in healthy subjects and knee OA patients

Appendices

59

In the same way, also the reliability of the analysis in 3-matic was estimated. In total, two

observers each individually did 6 measurements on the same 3D surface objects (those of the

first patient included). The inter- and intra-observer reliability was defined by the average

distances in space the calculated points varied across the different analyses. The overall average

distance of variation was 2.22 mm. This distance was slightly larger between observers

(2.56mm) than intra-observer (2.07mm). Variation was highest in the pelvic area (LPSI, RPSI,

LASI and RASI). The different distances of variation for the plates, pelvic -, femoral - and

lower leg region can be found in the figure below.

Figure 12: Inter- and intra-observer variation in analysis in 3-matic

Comparison of the observations made by the two observers was done using a mixed-design

ANOVA. Mauchly’s Test of Sphericity did not allow to accept the condition of sphericity

(p<0.001). This meant that the variances of the various measurements of the anatomical

landmarks were significantly different. Subsequently, the Greenhouse-Geiser Correction

TOTAL:

0,90 1,08

3,61 4,97

2,13 2,20

2,54 3,37

2,07 2,56

Inter-observer variation

Intra-observer variation

Appendices

60

(ε=0.049) had to be used5, which increased the p-value from p<0.001 to p=0.074 for the

combined effect of anatomical landmark and observer. The p-value of the error that was

dependent on the landmark stayed smaller than 0.001. Already here, it could be assumed that

the landmark itself introduced more error than the person who made the observation.

Although the Levene’s Test of Equality of Error Variances was highly significant (p<0.001)

for eight of the 87 coordinates (three dimensions for 29 landmarks), the overall F-ratio was

2.628. Using the Test of Between-Subject Effects this corresponded to a p-value of 0.136. Thus,

the null hypothesis (there is no significant effect of rater) could be accepted.

Mauchly’s Test of Sphericity

Within Subjects Effect Mauchly's Test

Epsilon

(Greenhouse-

Geisser)

Anatomical landmark p<0.001 0.049

Tests of Within-Subjects Effects

Source F-ratio Significance

Anatomical landmark Sphericity Assumed 1115309.094 p<0.001

Greenhouse-Geisser 1115309.094 p<0.001

Anatomical landmark *

Observer

Sphericity Assumed 2.276 p<0.001

Greenhouse-Geisser 2.276 p=0.074

Table 15: Mixed-design ANOVA to test the effect of the observer on the measurements done in 3-matic

5 The Greenhouse-Geiser Correction should be used when ε < 0.75. Otherwise, the Huynh-Feldt Correction is

more appropriate. These correction factors reduce the number of degrees of freedom. In this way, the F-ratio’s are

made more conservative, which means they have to be larger to be as significant.

Appendices

61

Appendix 6 : Kinematic graphs comparing the Plug-in-Gait with the replica model

Gait cycle in x-axis, joint angles in degrees in y-axis. Normal values in gray.

BMJ pre-surgery PIG vs. PIGr

Generic Plug-in-Gait

model


replica model

Left Left

Right Right

Current vrs Previous

Lef t Current (23-08-2013 PIG) Right Current (23-08-2013 PIG) Lef t 470524 070A38 PIGr.pxd (23-08-2013 PIGr) Right 470524 070A38 PIGr.pxd (23-08-2013 PIGr)

Pelvic Tilt30

-30

Ant

Post

deg

Hip Flexion/Extension60

-30

Flex

Ext

deg

Knee Flexion/Extension80

-10

Flex

Ext

deg

Ankle Dorsi/Plantar60

-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg

Foot Progress Angles60

-60

Int

Ext

deg

Appendices

62

BMJ post-surgery PIG vs. PIGr


model


replica model

Left Left

Right Right


Lef t Current (28-02-2014 PIG) Right Current (28-02-2014 PIG) Lef t 470524 070A38 Post PIGr.pxd (28-02-2014 PIGr) Right 470524 070A38 Post PIGr.pxd (28-02-2014 PIGr)

Pelvic Tilt30

-30

Ant

Post

deg


-30

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg


-60

Int

Ext

deg

Appendices

63

DBM pre-surgery PIG vs. PIGr


model


replica model

Left Left

Right Right


Lef t 400914 034A61 18-10-2013 PIG.pxd (18-10-2013 PIG) Right 400914 034A61 18-10-2013 PIG.pxd (18-10-2013 PIG) Lef t 400914 034A61 18-10-2013 PIGr.pxd (18-10-2013 PIGr)

Right 400914 034A61 18-10-2013 PIGr.pxd (18-10-2013 PIGr)

Pelvic Tilt30

-30

Ant

Post

deg


-30

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg


-60

Int

Ext

deg

Appendices

64

DBM post-surgery PIG vs. PIGr


model


replica model

Left Left

Right Right


Lef t Current (07-03-2014 PIG) Right Current (07-03-2014 PIG) Lef t 400914 034A61 Post PIGr.pxd (07-03-2014 Post PIGr) Right 400914 034A61 Post PIGr.pxd (07-03-2014 Post PIGr)

Pelvic Tilt30

-30

Ant

Post

deg


-30

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg


-60

Int

Ext

deg

Appendices

65

DRM pre-surgery PIG vs. PIGr


model


replica model

Left Left

Right Right


Lef t Current (03-05-2013 PIG) Right Current (03-05-2013 PIG) Lef t 420820 018A29 03-05-2013 PIGr.pxd (03-05-2013 PIGr) Right 420820 018A29 03-05-2013 PIGr.pxd (03-05-2013 PIGr)

Pelvic Tilt30

-30

Ant

Post

deg


-30

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg


-60

Int

Ext

deg

Appendices

66

DBM post-surgery PIG vs. PIGr


model


replica model

Left Left

Right Right


Lef t Current (28-02-2014 PIG) Right Current (28-02-2014 PIG) Lef t 420820 018A29 Post PIGr.pxd (28-02-2014 PIGr) Right 420820 018A29 Post PIGr.pxd (28-02-2014 PIGr)

Pelvic Tilt30

-30

Ant

Post

deg


-30

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg


-60

Int

Ext

deg

Appendices

67

Appendix 7 : Summary kinematic variables and their comparison between the generic PIG-model and the replica model PIG - PIGr Paired samples T-test Pearson correlation ICC (95% CI) F-test Mean Mean D SD (DIFF) 95% LOA

n=86 T-value p-value Correlation p-value Lower Upper p-value Lower Upper

Peak pelvic tilt (°) 0.244 0.808 0.984 <0.001 0.984 0.975 0.989 <0.001 18.871 0.022 0.834 -0.156 0.200

Range pelvic tilt (°) -2.191 <0.05 0.010 0.927 0.005 -0.194 0.207 0.481 3.192 -0.916 3.899 -1.747 -0.085

Peak pelvic obliquity (°) -2.176 <0.05 0.483 <0.001 0.450 0.268 0.602 <0.001 3.097 -0.511 2.190 -0.978 -0.044

Range pelvic obliquity (°) -2.473 <0.05 0.486 <0.001 0.459 0.276 0.609 <0.001 5.967 -0.617 2.328 -1.113 -0.121

Peak pelvic rotation (°) -1.543 0.127 0.161 0.136 0.074 -0.134 0.277 0.244 4.318 -2.260 13.661 -5.171 0.652

Range pelvic rotation (°) -1.831 0.071 -0.210 0.051 -0.047 -0.247 0.160 0.673 7.769 -3.258 16.597 -6.795 0.279

Peak hip flexion (°) 0.255 0.800 0.954 <0.001 0.954 0.931 0.970 <0.001 41.851 0.073 2.655 -0.496 0.642

Peak hip extension (°) 2.340 <0.05 0.983 <0.001 0.982 0.972 0.988 <0.001 4.384 0.410 1.624 0.062 0.758

Range hip flexion/extension (°) -1.453 0.150 0.937 <0.001 0.936 0.903 0.958 <0.001 37.467 -0.337 2.151 -0.798 0.124

Peak hip abduction (°) 0.677 0.500 0.984 <0.001 0.984 0.976 0.990 <0.001 -0.601 0.055 0.751 -0.106 0.216

Peak hip adduction (°) -1.415 0.161 0.973 <0.001 0.973 0.959 0.982 <0.001 9.184 -0.170 1.114 -0.409 0.069

Range hip abduction/adduction (°) -1.946 0.055 0.882 <0.001 0.878 0.818 0.919 <0.001 9.785 -0.225 1.071 -0.454 0.005

Peak hip external rotation (°) -0.162 0.871 0.917 <0.001 0.918 0.876 0.945 <0.001 -16.515 -0.086 4.890 -1.134 0.963

Peak hip internal rotation (°) 0.936 0.352 0.813 <0.001 0.809 0.722 0.871 <0.001 10.839 0.479 4.746 -0.538 1.497

Range hip rotation (°) 1.946 0.055 0.958 <0.001 0.956 0.933 0.971 <0.001 27.353 0.565 2.692 -0.012 1.142

Peak knee extension (°) -0.630 0.530 0.955 <0.001 0.954 0.930 0.970 <0.001 5.985 -0.140 2.057 -0.581 0.301

Range knee flexion extension (°) -2.827 <0.05 0.948 <0.001 0.944 0.911 0.964 <0.001 43.284 -0.769 2.522 -1.310 -0.228

Peak knee varus (°) 3.307 <0.05 0.870 <0.001 0.856 0.770 0.909 <0.001 9.923 1.387 3.889 0.553 2.221

Peak knee valgus (°) 3.600 <0.05 0.975 <0.001 0.971 0.949 0.983 <0.001 -6.105 0.614 1.581 0.275 0.953

Range knee varus/valgus (°) 2.260 <0.05 0.910 <0.001 0.891 0.835 0.928 <0.001 16.028 0.773 3.170 0.093 1.453

Peak knee external rotation (°) -0.835 0.406 0.025 0.818 0.022 -0.191 0.232 0.421 -7.283 -0.764 8.487 -2.583 1.056

Peak knee internal rotation (°) -0.977 0.332 0.683 <0.001 0.626 0.479 0.739 <0.001 9.551 -0.739 7.019 -2.244 0.766

Range knee rotation (°) 0.062 0.951 0.887 <0.001 0.888 0.834 0.926 <0.001 16.834 0.025 3.713 -0.771 0.821

Peak ankle plantarflexion (°) -5.984 <0.001 0.890 <0.001 0.780 0.522 0.885 <0.001 -8.291 -2.716 4.234 -3.618 -1.814

Range of dorsi/plantar flexion (°) 1.269 0.208 0.929 <0.001 0.928 0.893 0.953 <0.001 21.729 0.289 2.123 -0.164 0.741

Peak foot progression angle (°) -1.087 0.280 0.533 <0.001 0.437 0.251 0.592 <0.001 -9.836 -1.554 13.332 -4.396 1.287

Peak ankle external rotation (°) 1.863 0.066 0.674 <0.001 0.619 0.469 0.734 <0.001 -6.805 1.147 5.711 -0.077 2.372

Peak ankle internal rotation (°) -0.085 0.933 0.405 <0.001 0.385 0.188 0.552 <0.001 8.685 -0.084 9.187 -2.054 1.886

Range ankle rotation (°) -2.275 <0.05 0.226 <0.05 0.216 0.014 0.404 <0.05 15.490 -1.231 5.018 -2.307 -0.155

Peak knee flexion in swing (°) -2.679 <0.05 0.928 <0.001 0.923 0.880 0.951 <0.001 49.269 -0.909 3.146 -1.583 -0.234

Peak ankle dorsiflexion in swing (°) -4.726 <0.001 0.596 <0.001 0.485 0.249 0.654 <0.001 6.021 -2.190 4.323 -3.112 -1.269

Peak knee flexion in stance (°) -0.953 0.343 0.941 <0.001 0.937 0.906 0.959 <0.001 16.781 -0.285 2.770 -0.878 0.309

Peak ankle dorsiflexion in stance (°) -6.254 <0.001 0.588 <0.001 0.410 0.113 0.617 <0.001 13.476 -2.324 3.467 -3.063 -1.586

Ankle position at initial contact (°) -4.569 <0.001 0.468 <0.001 0.404 0.179 0.581 <0.001 1.711 -2.083 4.253 -2.990 -1.177

Mean -0.892 0.240 0.698 0.058 0.676 0.565 0.764 0.054 10.688 -0.539

0.883*

4.447 -1.490 0.412

Table 16: Summary kinematic variables and the comparison between the generic PIG-model and the replica model. ICC: intraclass correlation (two-way mixed effect, absolute agreement, single measures);

95% CI: 95% confidence interval; Mean D: average difference between the two models for the various assessments; SD (DIFF): standard deviation of the differences between the models; 95% LOA: 95%

limits-of-agreement (confidence interval for the mean differences); SEM: standard error of measurement; MDC: minimally detectable change; *average of the absolute values

Appendices

68

Appendix 8 : Kinematic graphs comparing the Plug-in-Gait with the CT-model

Gait cycle in x-axis, joint angles in degrees in y-axis. Normal values in gray.

BMJ pre-surgery PIG vs. CTM

CT-based subject-

specific model


model

Left Left

Right Right


Lef t 470524 070A38 CTM.pxd (23-08-2013 CTM) Right 470524 070A38 CTM.pxd (23-08-2013 CTM) Lef t 470524 070A38 PIG.pxd (23-08-2013 PIG)

Right 470524 070A38 PIG.pxd (23-08-2013 PIG)

Pelvic Tilt30

-30

Ant

Post

deg


-31

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg


-60

Int

Ext

deg

Appendices

69

DBM pre-surgery PIG vs. CTM

CT-based subject-

specific model


model

Left Left

Right Right


Lef t Current (18-10-2013 CTM) Right Current (18-10-2013 CTM) Lef t 400914 034A61 18-10-2013 PIG.pxd (18-10-2013 PIG) Right 400914 034A61 18-10-2013 PIG.pxd (18-10-2013 PIG)

Pelvic Tilt30

-30

Ant

Post

deg


-30

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation30

-30

Int

Ext

deg


-60

Int

Ext

deg

Appendices

70

DRM pre-surgery PIG vs. CTM

CT-based subject-

specific model


model

Left Left

Right Right


Lef t 420820 018A29_03-05-2013 CTM.pxd (03-05-2013 CTM) Right 420820 018A29_03-05-2013 CTM.pxd (03-05-2013 CTM) Lef t 420820 018A29_03-05-2013 PIG.pxd (03-05-2013 PIG)

Right 420820 018A29_03-05-2013 PIG.pxd (03-05-2013 PIG)

Pelvic Tilt40

-30

Ant

Post

deg


-49

Flex

Ext

deg


-10

Flex

Ext

deg


-60

Dors

Plan

deg

Pelvic Obliquity30

-30

Up

Down

deg

Hip Ab/Adduction30

-30

Add

Abd

deg

Knee Ab/Adduction30

-30

Var

Val

deg

Ankle Rotation70

-70

Int

Ext

deg

Pelvic Rotation30

-30

Int

Ext

deg

Hip Rotation40

-40

Int

Ext

deg

Knee Rotation60

-60

Int

Ext

deg


-60

Int

Ext

deg

Appendices

71

Appendix 9 : Summary kinematic variables and the comparison between the PIG model and the CTM: Systematic differences by T-testing PIG - CTM (pre-surgery) BMJ (n=9) DBM (n=9) DRM (n=20) ALL (n=38)

Paired samples T-test Mean Mean D SD (of D) p-value Mean Mean D SD (of D) p-value Mean Mean D SD (of D) p-value Mean Mean D SD (of D) p-value

Peak pelvic tilt (°) 23.782 -12.982 1.180 <0.001 17.186 0.749 1.307 0.124 27.120 -12.688 2.973 <0.001 23.977 -9.575 6.261 <0.001

Range pelvic tilt (°) 5.137 -1.589 1.378 <0.05 2.638 -1.234 1.774 0.070 6.285 -7.458 2.984 <0.001 5.149 -4.594 3.878 <0.001

Peak pelvic obliquity (°) 3.614 0.322 8.602 0.913 1.297 0.148 2.763 0.877 1.705 0.613 3.111 0.389 2.060 0.434 4.760 0.578

Range pelvic obliquity (°) 4.214 5.226 1.002 <0.001 2.829 -0.218 1.391 0.651 3.280 0.936 1.577 <0.05 3.394 1.679 2.479 <0.001

Peak pelvic rotation (°) 3.548 0.674 4.912 0.691 1.632 -1.016 1.394 0.060 3.424 0.191 2.027 0.678 3.029 0.020 2.851 0.966

Range pelvic rotation (°) 6.436 -1.760 1.102 <0.05 3.517 -1.837 1.272 <0.05 6.972 0.171 1.100 0.494 6.027 -0.762 1.493 <0.05

Peak hip flexion (°) 53.407 -21.414 2.070 <0.001 41.277 -6.891 4.988 <0.05 51.971 -34.997 7.470 <0.001 49.778 -25.123 13.109 <0.001

Peak hip extension (°) 10.464 -18.198 3.777 <0.001 8.850 -4.093 1.727 <0.001 19.387 -26.558 8.158 <0.001 14.778 -19.257 11.087 <0.001

Range hip flexion/extension (°) 42.943 -3.217 3.712 <0.05 32.427 -2.798 4.314 0.088 32.584 -8.439 4.782 <0.001 35.000 -5.866 5.131 <0.001

Peak hip abduction (°) 0.384 4.901 11.532 0.238 -2.352 0.564 1.600 0.321 -3.641 2.655 3.125 <0.05 -2.383 2.692 6.051 <0.05

Peak hip adduction (°) 10.750 5.758 11.621 0.175 5.201 -0.657 1.040 0.095 4.641 0.466 3.942 0.604 6.220 1.453 6.598 0.183

Range hip abduction/adduction (°) 10.366 0.857 2.177 0.272 7.553 -1.221 1.613 0.053 8.282 -2.189 2.249 <0.001 8.603 -1.238 2.396 <0.05

Peak hip external rotation (°) -11.689 -15.624 4.983 <0.001 -0.565 -3.497 3.431 <0.05 -4.045 -22.561 14.372 <0.001 -5.031 -16.403 13.234 <0.001

Peak hip internal rotation (°) 9.702 -11.176 5.347 <0.001 14.236 -2.074 1.863 <0.05 13.151 -17.622 14.264 <0.001 12.591 -12.413 12.347 <0.001

Range hip rotation (°) 21.391 4.449 3.497 <0.05 14.801 1.422 4.447 0.365 17.197 4.939 4.133 <0.001 17.623 3.990 4.223 <0.001

Peak knee extension (°) 9.008 -4.252 2.970 <0.05 8.885 -4.710 6.271 0.054 6.028 -9.684 40.550 0.299 7.411 -7.219 29.355 0.138

Range knee flexion extension (°) 47.474 -12.388 3.398 <0.001 45.997 -7.838 3.938 <0.001 40.307 -7.434 7.222 <0.001 43.352 -8.703 6.082 <0.001

Peak knee varus (°) 6.017 -8.773 4.634 <0.001 13.456 3.792 2.564 <0.05 10.779 -9.300 5.476 <0.001 10.285 -6.074 7.248 <0.001

Peak knee valgus (°) -6.632 -12.483 5.441 <0.001 1.395 -1.561 3.225 0.184 2.173 -8.234 3.972 <0.001 -0.097 -7.660 5.625 <0.001

Range knee varus/valgus (°) 12.648 3.710 3.393 <0.05 12.061 5.353 1.628 <0.001 8.607 -1.066 3.279 0.162 10.382 1.586 4.115 <0.05

Peak knee external rotation (°) 8.628 -19.396 19.522 <0.05 -11.869 11.130 10.641 <0.05 -19.549 28.368 2.419 <0.001 -11.057 12.973 22.219 <0.001

Peak knee internal rotation (°) 19.099 -18.323 20.629 <0.05 -1.428 13.876 5.822 <0.001 -7.558 20.234 8.190 <0.001 0.208 9.596 19.723 <0.05

Range knee rotation (°) 10.472 1.072 4.109 0.456 10.442 2.746 5.183 0.151 11.991 -8.135 6.783 <0.001 11.264 -3.377 7.698 <0.05

Peak ankle plantarflexion (°) -0.104 -2.083 5.733 0.307 -6.909 1.668 6.489 0.463 -0.925 -4.979 18.621 0.246 -2.148 -2.719 14.206 0.246

Range of dorsi/plantar flexion (°) 16.618 -0.891 3.187 0.426 22.326 -1.768 5.309 0.347 18.488 0.034 4.853 0.976 18.954 -0.612 4.578 0.415

Peak foot progression angle (°) 0.217 -1.496 3.675 0.257 -9.219 1.439 1.700 <0.05 -19.629 0.521 7.398 0.756 -12.463 0.261 5.725 0.780

Peak ankle external rotation (°) -16.071 23.063 18.964 <0.05 3.257 -7.164 6.317 <0.05 -2.189 -6.841 10.977 <0.05 -4.187 0.165 17.759 0.955

Peak ankle internal rotation (°) -1.260 24.660 15.705 <0.05 14.801 -12.509 8.005 <0.05 12.356 -5.265 12.329 0.071 9.710 0.107 18.606 0.972

Range ankle rotation (°) 14.811 1.597 4.623 0.330 11.544 -5.344 4.768 <0.05 14.544 1.577 3.222 <0.05 13.897 -0.058 4.876 0.942

Peak knee flexion in swing (°) 56.482 -16.640 2.361 <0.001 54.882 -12.548 7.266 <0.001 46.335 -17.118 46.767 0.118 50.763 -15.922 33.755 <0.05

Peak ankle dorsiflexion in swing (°) 11.749 -1.471 5.226 0.423 3.958 1.179 3.714 0.369 9.842 -4.119 14.731 0.226 8.900 -2.237 11.190 0.226

Peak knee flexion in stance (°) 24.239 -9.323 2.169 <0.001 16.896 -5.142 5.765 <0.05 13.802 -9.572 41.272 0.313 17.007 -8.464 29.773 0.088

Peak ankle dorsiflexion in stance (°) 16.514 -2.974 7.283 0.255 15.417 -0.100 2.012 0.885 17.549 -4.917 14.204 0.138 16.799 -3.316 10.949 0.070

Ankle position at initial contact (°) 5.963 0.976 5.301 0.596 -0.462 3.399 3.445 <0.05 2.033 -2.797 17.376 0.480 2.373 -0.436 13.067 0.838

Mean -3.506 6.036 0.160 -1.081 3.794 0.156 -5.037 10.174 0.177 -3.737 10.660 0.219

Mean of the absolute values 8.051 3.873 8.608 5.794

Table 17: Summary kinematic variables and the comparison between the generic PIG-model and the CT-based subject-specific model (CTM): Systematic differences by means of T-testing. Mean D: average

difference between the two models (PIG-CTM); SD (of D): standard deviation of the differences between the models; p-value for the paired samples T-test (PIG-CTM)

Appendices

72

Appendix 10 : Summary kinematic variables and the comparison between the PIG model and the CTM: Correlation and agreement testing PIG - CTM (pre-surgery) BMJ (n=9) DBM (n=9) DRM (n=20) ALL (n=38)

Correlation Pearson p-value ICC p-value Pearson p-value ICC p-value Pearson p-value ICC p-value Pearson p-value ICC p-value

Peak pelvic tilt (°) -0.538 0.135 -0.003 0.936 0.701 <0.05 0.454 0.069 0.090 0.706 0.002 0.427 0.627 <0.001 0.113 <0.05

Range pelvic tilt (°) -0.265 0.491 -0.087 0.726 0.710 <0.05 0.375 0.095 0.109 0.646 0.010 0.390 0.064 0.703 0.016 0.411

Peak pelvic obliquity (°) -0.801 <0.05 -0.165 0.655 -0.676 <0.05 -0.647 0.946 0.902 <0.001 0.728 <0.001 0.506 <0.05 0.353 <0.05

Range pelvic obliquity (°) -0.573 0.107 -0.013 0.951 -0.564 0.113 -0.492 0.893 -0.320 0.168 -0.223 0.915 -0.674 <0.001 -0.318 1.000

Peak pelvic rotation (°) -0.855 <0.05 -1.031 0.999 0.204 0.599 0.142 0.299 0.962 <0.001 0.905 <0.001 0.727 <0.001 0.694 <0.001

Range pelvic rotation (°) 0.635 0.066 0.282 <0.05 0.238 0.537 0.085 0.274 0.688 <0.001 0.672 <0.001 0.767 <0.001 0.659 <0.001

Peak hip flexion (°) 0.780 <0.05 0.025 <0.05 -0.467 0.205 -0.126 0.908 -0.173 0.465 -0.003 0.624 -0.251 0.128 -0.029 0.823

Peak hip extension (°) 0.940 <0.001 0.146 <0.05 0.627 0.071 0.184 <0.05 0.125 0.600 0.006 0.383 0.284 0.084 0.044 0.171

Range hip flexion/extension (°) 0.629 0.070 0.411 0.056 -0.215 0.579 -0.139 0.700 0.458 <0.05 0.094 0.096 0.627 <0.001 0.424 <0.001

Peak hip abduction (°) -0.486 0.184 -0.061 0.571 -0.282 0.463 -0.270 0.779 0.896 <0.001 0.587 <0.001 0.379 <0.05 0.250 <0.05

Peak hip adduction (°) -0.933 <0.001 -0.410 0.926 -0.167 0.668 -0.124 0.677 0.838 <0.001 0.632 <0.05 0.253 0.126 0.235 0.072

Range hip abduction/adduction (°) 0.594 0.092 0.583 <0.05 -0.006 0.989 -0.004 0.506 -0.058 0.808 -0.029 0.597 0.338 <0.05 0.277 <0.05

Peak hip external rotation (°) 0.768 <0.05 0.142 <0.05 0.896 <0.05 0.772 <0.001 -0.267 0.255 -0.058 0.841 0.046 0.784 0.018 0.395

Peak hip internal rotation (°) 0.624 0.072 0.135 0.096 0.980 <0.001 0.958 <0.001 -0.751 <0.001 -0.209 1.000 -0.137 0.412 -0.064 0.797

Range hip rotation (°) -0.014 0.972 -0.005 0.515 0.520 0.151 0.400 0.127 0.647 <0.05 0.431 <0.001 0.634 <0.001 0.461 <0.001

Peak knee extension (°) 0.935 <0.001 0.824 <0.001 -0.871 <0.05 -0.381 0.983 0.259 0.271 0.083 0.359 0.239 0.148 0.102 0.263

Range knee flexion extension (°) 0.720 <0.05 0.153 <0.05 0.350 0.355 0.094 0.172 0.685 <0.001 0.457 <0.05 0.713 <0.001 0.395 <0.001

Peak knee varus (°) 0.378 0.316 0.055 0.284 0.970 <0.001 0.830 <0.001 0.216 0.360 0.067 0.173 0.327 <0.05 0.223 <0.05

Peak knee valgus (°) -0.761 <0.05 -0.070 0.982 0.648 0.059 0.116 0.362 0.674 <0.05 0.281 <0.001 0.536 <0.001 0.277 <0.001

Range knee varus/valgus (°) 0.378 0.316 0.225 0.141 0.973 <0.001 0.665 <0.001 0.006 0.980 0.004 0.493 0.532 <0.001 0.486 <0.001

Peak knee external rotation (°) -0.868 <0.05 -0.145 0.809 -0.449 0.225 -0.047 0.605 0.069 0.771 0.000 0.397 0.428 <0.05 0.076 0.277

Peak knee internal rotation (°) -0.633 0.067 -0.075 0.643 -0.344 0.364 -0.014 0.609 0.534 <0.05 0.053 0.107 0.694 <0.001 0.196 0.082

Range knee rotation (°) 0.595 0.091 0.473 0.089 -0.059 0.881 -0.029 0.538 0.421 0.064 0.126 0.131 -0.020 0.907 -0.012 0.533

Peak ankle plantarflexion (°) 0.834 <0.05 0.405 0.119 -0.285 0.457 -0.302 0.788 -0.190 0.423 -0.067 0.615 0.025 0.881 0.012 0.471

Range of dorsi/plantar flexion (°) 0.059 0.879 0.052 0.445 -0.152 0.696 -0.149 0.659 0.139 0.558 0.108 0.327 0.320 0.050 0.299 <0.05

Peak foot progression angle (°) 0.762 <0.05 0.642 <0.05 0.500 0.171 0.269 0.145 0.510 <0.05 0.176 0.231 0.849 <0.001 0.824 <0.001

Peak ankle external rotation (°) 0.957 <0.001 0.101 0.279 -0.071 0.855 -0.010 0.524 -0.740 <0.001 -0.457 1.000 0.060 0.721 0.035 0.419

Peak ankle internal rotation (°) 0.919 <0.001 0.161 0.131 0.118 0.762 0.036 0.381 -0.588 <0.05 -0.485 0.997 -0.068 0.684 -0.045 0.604

Range ankle rotation (°) -0.575 0.105 -0.563 0.957 0.405 0.279 0.158 0.211 0.279 0.234 0.219 0.138 0.047 0.779 0.048 0.388

Peak knee flexion in swing (°) 0.825 <0.05 0.085 <0.05 -0.070 0.859 -0.011 0.546 -0.273 0.244 -0.018 0.534 0.068 0.684 0.020 0.444

Peak ankle dorsiflexion in swing (°) 0.473 0.198 0.178 0.316 -0.651 0.058 -0.660 0.979 0.559 <0.05 0.140 0.267 0.468 <0.05 0.207 0.101

Peak knee flexion in stance (°) 0.909 <0.001 0.336 <0.001 -0.706 <0.05 -0.232 0.912 0.263 0.263 0.076 0.371 0.273 0.097 0.110 0.243

Peak ankle dorsiflexion in stance (°) 0.735 <0.05 0.306 0.186 0.332 0.383 0.274 0.242 0.054 0.822 0.024 0.456 0.133 0.427 0.059 0.353

Ankle position at initial contact (°) 0.221 0.568 0.200 0.302 0.225 0.561 0.113 0.298 0.059 0.805 0.017 0.471 0.120 0.472 0.064 0.353

Mean 0.217 0.143 0.097 0.361 0.099 0.338 0.067 0.449 0.208 0.281 0.128 0.363 0.292 0.241 0.191 0.246

Table 18: Summary kinematic variables and the comparison between the generic PIG-model and the CT-based subject-specific model (CTM): Correlation and agreement testing. Pearson: Pearson

correlation coefficient; ICC: intraclass correlation coefficient (ICC(3,1): two-way mixed effect, absolute agreement, single measures

Appendices

73

Appendix 11 : Temporal parameters pre- and post-surgery

Temporal parameters BMJ DBM DRM ALL

PRE-POST Mean Mean D T-test Mean Mean D T-test Mean Mean D T-test Mean Mean D T-test

PIG

Cadence L 101.225 11.994 <0.001 118.550 -12.700 <0.001 96.945 -38.310 <0.001 106.369 -15.158 <0.001

Cadence R 101.142 11.383 <0.001 118.300 -11.400 <0.001 96.635 -36.130 <0.001 106.774 -17.186 <0.001

Step length L 0.451 0.037 0.583 0.500 -0.074 <0.001 0.418 -0.165 <0.001 0.459 -0.075 <0.05

Step length R 0.459 -0.018 0.081 0.503 -0.101 <0.001 0.417 -0.153 <0.001 0.461 -0.105 <0.001

Step width L 0.165 -0.041 <0.001 0.132 0.001 0.843 0.213 0.029 <0.001 0.170 -0.001 0.902

Step width R 0.167 -0.037 <0.001 0.136 0.007 0.311 0.216 0.036 <0.001 0.173 0.009 0.426

Stride length L 0.882 -0.038 <0.001 0.998 -0.185 <0.001 0.837 -0.312 <0.001 0.910 -0.190 <0.001

Stride length R 0.887 -0.021 <0.05 1.002 -0.175 <0.001 0.836 -0.318 <0.001 0.916 -0.204 <0.001

Walking speed L 0.744 0.057 <0.001 0.990 -0.287 <0.001 0.701 -0.516 <0.001 0.822 -0.276 <0.001

Walking speed L 0.746 0.072 <0.05 0.994 -0.268 <0.001 0.696 -0.510 <0.001 0.833 -0.304 <0.001

CTM

Cadence L 101.433 10.467 <0.05 119.583 -13.167 <0.001 97.068 -36.436 <0.001 106.204 -19.166 <0.001

Cadence R 100.613 8.775 <0.05 119.325 -12.150 <0.001 97.269 -36.263 <0.001 103.812 -19.362 <0.001

Step length L 0.430 -0.020 0.086 0.503 -0.078 <0.001 0.422 -0.150 <0.001 0.452 -0.102 <0.001

Step length R 0.459 -0.008 0.580 0.502 -0.124 <0.001 0.428 -0.159 <0.001 0.457 -0.117 <0.001

Step width L 0.164 -0.038 <0.05 0.130 0.008 0.360 0.213 0.026 <0.05 0.173 0.007 0.620

Step width R 0.161 -0.033 <0.05 0.136 -0.001 0.905 0.219 0.030 <0.05 0.180 0.015 0.339

Stride length L 0.884 -0.018 0.266 1.002 -0.187 <0.001 0.849 -0.297 <0.001 0.912 -0.204 <0.001

Stride length R 0.881 -0.043 <0.05 1.001 -0.211 <0.001 0.839 -0.314 <0.001 0.894 -0.229 <0.001

Walking speed L 0.748 0.062 <0.05 1.003 -0.294 <0.001 0.708 -0.496 <0.001 0.824 -0.319 <0.001

Walking speed L 0.739 0.033 0.132 1.001 -0.307 <0.001 0.704 -0.511 <0.001 0.793 -0.336 <0.001

Table 19: Temporal parameters before and after surgery compared by means of T-testing. PIG: generic Plug-in-Gait model; CTM: CT-based subject-specific model; Mean: average of pre- and post-

surgical values; Mean D: average difference between pre- and post-surgical values; T-test: p-values for the independent T-test; Cadence in steps/min, step length and width and stride length in metres,

walking speed in metres/second

Appendices

74

Appendix 12 : Summary kinematic variables and the comparison between pre- and post-surgically: Systematic differences by T-testing

PRE-POST BMJ DBM DRM ALL

Paired samples T-test Bad side - R Good side Bad side - R Good side Bad side - L Good side Bad side Good side

p-values PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM

Peak hip flexion (°) <0.001 <0.05 <0.001 <0.001 0.083 <0.001 <0.001 <0.05 <0.001 <0.001 <0.001 <0.001 <0.05 <0.05 <0.05 <0.05

Peak hip extension (°) <0.05 <0.05 <0.001 0.174 <0.001 0.824 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.319 <0.001 0.446 <0.001

Range hip flexion/extension (°) 0.729 <0.05 <0.05 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.05 <0.001 <0.05 <0.001 <0.001 <0.001 <0.001

Peak knee extension (°) <0.001 <0.05 <0.05 0.583 <0.001 <0.001 0.408 <0.001 <0.001 <0.001 <0.001 0.830 0.270 <0.05 0.001 0.824

Range knee flexion extension (°) <0.05 <0.05 <0.001 <0.001 <0.001 <0.05 <0.001 <0.001 <0.001 <0.001 <0.001 0.081 <0.05 <0.05 <0.001 <0.001

Peak knee flexion in swing (°) <0.001 <0.05 <0.05 <0.001 <0.05 <0.001 <0.001 <0.05 <0.001 <0.001 <0.001 0.644 <0.05 0.940 <0.001 0.330

Peak knee flexion in stance (°) <0.05 <0.05 <0.05 0.954 0.439 <0.001 <0.001 0.122 <0.001 <0.001 <0.001 0.459 0.243 <0.05 <0.001 0.413

PRE-POST BMJ DBM DRM ALL

Mean differences Bad side - R Good side Bad side - R Good side Bad side - L Good side Bad side Good side

PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM PIG CTM

Peak hip flexion (°) 12.220 -5.023 8.480 -10.937 1.588 -12.861 -4.942 -4.653 -19.199 43.390 -23.625 44.686 -5.944 16.133 -7.115 12.431

Peak hip extension (°) 11.490 6.027 12.250 -1.655 7.972 0.314 6.710 4.893 -7.950 48.636 -11.448 51.733 2.464 25.249 2.421 20.878

Range hip flexion/extension (°) 0.730 -11.050 -3.770 -9.282 -6.385 -13.175 -11.653 -9.547 -11.249 -5.246 -12.177 -7.047 -8.407 -9.116 -9.536 -8.447

Peak knee extension (°) 9.553 4.067 3.988 -0.698 4.790 -6.789 -0.583 6.058 -4.751 17.209 -18.107 -5.722 1.588 8.044 -6.764 -2.272

Range knee flexion extension (°) 5.427 6.490 -6.710 -12.443 -9.360 -9.486 -9.818 -7.952 -6.069 -7.881 -6.297 -8.623 -7.091 -7.844 -7.944 -9.416

Peak knee flexion in swing (°) 14.980 10.557 -2.722 -13.142 -4.570 -16.275 -10.401 -1.893 -10.820 9.327 -24.404 -14.345 -5.504 0.200 -14.708 -11.689

Peak knee flexion in stance (°) 12.133 9.573 4.300 -0.060 -5.628 -10.739 -4.629 1.423 -6.279 15.116 -22.867 -20.976 -2.846 6.539 -9.755 -8.976

Table 20: Summary kinematic variables and the comparison between the pre- and post-surgical assessment and between the generic PIG-model and the CT-based subject-specific model (CTM): Systematic

differences by paired samples T-testing (pre-post).

individualising kinematic modelling in the gait analysis ... · ganganalyse en het bestuderen van...

Documents