Introduction

An understanding of anatomic variability can help guide the surgeon on intervention strategies. Well-functioning thumb metacarpophalangeal joints (MCPJ) are essential for carrying out typical daily activities. However, current options for arthroplasty are limited. This is further hindered by the lack of a precise understanding of the geometric variation present in the population. In this paper, we offer new insight into the major modes of geometric variation in the thumb MCP using Statistical Shape Modelling.

Introduction

Unicompartmental Knee Replacement Arthroplasty (UKA) is a treatment option for early knee OA that appears under-utilised, partly because of a lack of clear guidance on how to best restore lasting knee function using such devices. Computational tools can help consider inherent uncertainty in patient anatomy, implant positioning and loading when predicting the performance of any implant. In the present research an approach for creating patient-specific finite element models (FEM) incorporating joint and muscle loads was developed to assess the response of the underlying bone to UKA implantation.

There is a critical need for safe innovation in total joint replacements to address the demands of an ageing yet increasingly active population. The development of robust implant designs requires consideration of uncertainties including patient related factors such as bone morphology but also activity related loads and the variability in the surgical procedure itself. Here we present an integrated framework considering these sources of variability and its application to assess the performance of the femoral component of a total hip replacement (THR).

The framework offers four key features. To consider variability in bone properties, an automated workflow for establishing statistical shape and intensity models (SSIM) was developed. Here, the inherent relationship between shape and bone density is captured and new meshes of the target bone structures are generated with specific morphology and density distributions. The second key feature is a virtual implantation capability including implant positioning, and bone resection. Implant positioning is performed using automatically identified bone features and flexibly defined rules reflecting surgical variability. Bone resection is performed according to manufacturer guidelines. Virtual implantation then occurs through Boolean operations to remove bone elements contained within the implant's volume. The third feature is the automatic application of loads at muscle attachment points or on the joint contact surfaces defined on the SSIM. The magnitude and orientation of the forces are derived from models of similar morphology for a range of activities from a database of musculoskeletal (MS) loads. The connection to this MS loading model allows the intricate link between morphology and muscle forces to be captured. Importantly, this model of the internal forces provides access to the spectrum of loading conditions across a patient population rather than just typical or average values. The final feature is an environment that allows finite element simulations to be run to assess the mechanics of the bone-implant construct and extract results for e.g. bone strains, interface mechanics and implant stresses. Results are automatically processed and mapped in an anatomically consistent manner and can be further exploited to establish surrogate models for efficient subsequent design optimization. To demonstrate the capability of the framework, it has been applied to the femoral component of a THR.

An SSIM was created from 102 segmented femurs capturing the heterogeneous bone density distributions. Cementless femoral stems were positioned such that for the optimal implantation the proximal shaft axis of the femurs coincided with the distal stem axis and the position of the native femoral head centre was restored. Here, the resection did not affect the greater trochanter and the implantations were clinically acceptable for 10000 virtual implantations performed to simulate variability in patient morphology and surgical variation. The MS database was established from musculoskeletal analyses run for a cohort of 17 THR subjects obtaining over 100,000 individual samples of 3D muscle and joint forces. An initial analysis of the mechanical performance in 7 bone-implant constructs showed levels of bone strains and implant stresses in general agreement with the literature.

Inter-subject variability is inherently present in patient anatomy and is apparent in differences in shape, size and relative alignment of the bony structures. Understanding the variability in patient anatomy is useful for distinguishing between pathologies and to assist in surgical planning. With the aim of supporting the development of stratified orthopaedic interventions, this work introduces an Articulated Statistical Shape Model (ASSM) of the lower limb. The model captures inter-subject variability and allows reconstructing ‘virtual’ knee joints of the lower limb shape while considering pose.

A training dataset consisting of 173 lower limbs from CT scans of 110 subjects (77 male, 33 female) was used to construct the ASSM of the lower limb. Each bone of the lower limb was segmented using ScanIP (Simpleware Ltd., UK), reconstructed into 3D surface meshes, and a SSM of each bone was created. A series of sizing and positioning procedures were carried out to ensure all the lower limbs were in full extension, had the same femoral length and that the femora were aligned with a coincident centre. All articulated lower limbs were represented as: (femur scale factor) × (full extension articulated lower limb + relative transformation of tibia, fibula and patella to femur). Articulated lower limbs were in full extension were used to construct a statistical shape model, representing the variance of lower limb morphology. Relative transformations of the tibia, fibula and patella versus the femur were used to form a statistical pose model. Principal component analysis (PCA) was used to extract the modes of changes in the model.

The first 30 modes of the shape model covered 90% of the variance in shape and the first 10 modes of the pose model covered 90% of the pose variance. The first mode captures changes of the femoral CCD angle and the varus/valgus alignment of the knee. The second mode represents the changes in the ratio of femur to tibia length. The third mode reflects change of femoral shaft diameter and patella size. The first mode characterising pose captures the medial/lateral translation between femur and tibia. The second mode represents variation in knee flexion. The third mode reflects variation in tibio-femoral joint space.

An articulated statistical modelling approach was developed to characterize inter-subject variability in lower limb morphology for a set of training specimens. This model can generate large sets of lower limbs to systematically study the effect of anatomical variability on joint replacement performance. Moreover, if a series of images of the lower limb during a dynamic activity are used as training data, this method can be applied to analyse variance of lower limb motion across a population.

Polymer foams have been used extensively in the testing and development of orthopaedic devices and for verification of computational models. Their use is often preferred over cadaver and animal models due to being relatively inexpensive and their consistent material properties. Successful validation of such models requires accurate material/mechanical data. The assumed range of compressive moduli, provided in the sawbones technical sheet, is 16 MPa to 1.15 GPa depending on the density of foam. In this investigation, we apply two non-contact measurement techniques (digital volume correlation (DVC) and optical surface extensometry) to assess the validity of these reported values. It is thought that such non-contact methods remove mechanical extensometer errors (slippage, misalignment) and restrict the effect of test-machine end-artifacts (friction, non-uniform loading, platen flexibility). This is because measurement is taken directly from the sample, and hence material property assessment should be more accurate. Use of DVC is advantageous as full field strain measurement is possible, however test time and cost is significantly higher than extensometry. Hence, the study also sought to assess the viability of optical extensometry for characterising porous materials.

Testing was conducted on five 20 mm cubic samples of 0.32g/cc (20 pcf) solid rigid polyurethane foam (SAWBONES^TM). The strain behaviour was characterised by incremental loading via an in situ loading rig. Loading was performed in 0.1 mm increments for 8 load steps with scans between loading steps. Full field strain measurement was performed on one sample by micro focus tomography (muvis centre, Southampton) and subsequent DVC (DaVis, Lavision). Calculation of Young's modulus and Poisson's ratio was then preformed through use of the virtual fields method. These results were subsequently corroborated by use of optical extensometry (MatchID). To account for heterogeneities, axial strain measurements were averaged from six points on the front and rear surfaces. A computationally derived correction factor was then applied to account for through volume strain variations. In each test compressive displacement was applied to 900N (∼2MPa) to remain within the linear elastic region.

Significant variability of individual strain measurements were observed from extensometry measurements on the same sample, indicating non-uniform loading did occur in all samples. However by averaging across multiple points linear loading profiles were identified. For all non-contact methods the calculated elastic moduli were found to range between 331–428 MPa whilst the approximated modulus based on cross head displacement was ∼210 MPa. The optical-extensometry gave a considerably higher modulus (p = 0.047) than the DVC results as only surface measurements were made. However, following computational based correction values converged within 6% of one another. Both the DVC and point-tracking results (p = 0.001) indicated substantially higher compressive modulus (137%) than the manufacturer provided properties.

This study demonstrates that methods of measuring displacement data on of cellular foams must be carefully considered, as artefacts can lead to significant errors of up to 137%, and such errors may falsely influence the design and validation of tested devices.

Polymer foams have been extensively used in the testing and development of orthopaedic devices and computational models. Often these foams are used in preference to cadaver and animal models due to being relatively inexpensive and their consistent material properties. Successful validation of such models requires accurate material/mechanical data. The assumed range of compressive moduli, provided in the sawbones technical sheet, is 16 MPa to 1.15 GPa depending on the density of foam. In this investigation, we apply two non-contact measurement techniques (digital volume correlation (DVC) and optical surface extensometry/point-tracking) to assess the validity of these reported values. It is thought that such non-contact methods remove mechanical extensometer errors (slippage, misalignment) and are less sensitive to test-machine end-artifacts (friction, non-uniform loading, platen flexibility). This is because measurement is taken directly from the sample, and hence material property assessment should be more accurate. Use of DVC is advantageous as full field strain measurement is possible, however test time and cost is significantly higher than extensometry. Hence, the study also sought to assess the viability of optical extensometry for characterising porous materials.

Testing was conducted on five 20 mm cubic samples of 0.32g/cc (20 pcf) solid rigid polyurethane foam (SAWBONESTM). The strain behaviour was characterised by incremental loading via an in situ loading rig. Loading was performed in 0.1 mm increments for 8 load steps with scans between loading steps. Full field strain measurement was performed on one sample by micro focus tomography (muvis centre, Southampton) and subsequent DVC (DaVis, Lavision). Average strains in each direction were then calculated to enable modulus and Poisson's ratio calculation. These results were subsequently corroborated by use of optical point-tracking (MatchID). To account for heterogeneities, axial strain measurements were averaged from six points on the front and rear surfaces (fig.2). In each test compressive displacement was applied to 900N (∼2MPa) to remain within the linear elastic region.

Significant variability of individual strain measurements were observed from point couples on the same sample, indicating non-uniform loading did occur in all samples. However, by averaging across multiple points, linear loading profiles were ascertained (fig.2). For all non-contact methods the calculated elastic moduli were found to range between 331–428 MPa whilst the approximated modulus based on cross head displacement was ∼210 MPa, similar to the manufacturer's quoted value (220MPa). The point-tracking gave a significantly higher modulus (p = 0.047) than the DVC results as only surface measurements were made. It is thought that a correction factor may be ascertained from the finite element method to correct this. Both the DVC and point-tracking results (p = 0.001) indicated a substantially higher compressive modulus than the manufacturer provided properties.

This study demonstrates that methods of measuring displacement data on cellular foams must be carefully considered, as artefacts can lead to errors of up to 70% compared to optical and x-ray based techniques.

Increased incidence of obesity and longer life expectancies will place increased demands on load bearing joints. In the present work, a method of pre-clinical evaluation to assess the condition of the joint and potentially inform on cases of joint deterioration, is described. Acoustic emission (AE) is a non-destructive test methodology that has been used extensively in engineering for condition monitoring of machinery and structures. It is a passive technique that uses piezoelectric sensors to detect energy released from internal structural defects as they deform and grow. The technique has been used with some success in the past to identify characteristic signals generated from the knee joint during activities such as standing and sitting, in candidate arthroplasty patients (1,2). In this study, 40 asymptomatic subjects had AE data generated from their knee joints analysed. Subject characteristics such as age, gender, and lifestyle were disclosed and evaluated against the AE data.

Each subject was invited to take a seated position and a piezoelectric AE sensor (Pancom P15, 150kHz resonance, 19mm diameter) was attached to the subject's knee using a wax couplant and tape as close to the articulating surface and on a bony prominence to avoid signal attenuation in the soft tissue.

Subjects were invited to sit and stand 3 times. AE data were collected and processed using an AMSY5 AE processor (Vallen, Germany). Tests were repeated on a separate occasion and selected subjects were invited to participate on a third occasion. The AE data of particular interest were the peak amplitudes and the frequency power spectrum of the waveform.

Post-test inspection of subject characteristics allowed them to be separated into three broad categories: no previous history (group A), some instances of pain in the knee (group B), and those who have had previous minor surgery on the knee (group C). The corresponding AE results were grouped separately. It was found that groups A and B demonstrated similar signal amplitude characteristics while group C produced much higher, significantly different (p<0.05) amplitudes and amplitude distributions. Typical results are shown in figure 1.

At present, broad trends could be identified and relationships emerged between the data and subject history (prior surgery, typical daily activity). Further work will continue with asymptomatic subjects and the work will be extended to pre-operative patients to identify whether certain trends are amplified in this population.

Introduction

Periprosthetic bone remodelling after Total Knee Arthroplasty (TKA) may be attributed to local changes in the mechanical strain field of the bone as a result of the stiffness mismatch between high modulus metallic implant materials and the supporting bone. This can lead to significant loss of periprosthetic bone density, which may promote implant loosening, and complicate revision surgery. A novel polyetheretherketone (PEEK) implant with a modulus similar to bone has the potential to reduce stress shielding whilst eliminating metal ion release. Numerical modelling can estimate the remodelling stimulus but rigorous validation is required for use as a predictive tool. In this study, a finite element (FE) model investigating the local biomechanical changes with different TKA materials was verified experimentally using Digital Image Correlation (DIC). DIC is increasingly used in biomechanics for strain measurement on complex, heterogeneous anisotropic material structures.

There is a large variability associated with hip stem designs, patient anatomy, bone mechanical property, surgical procedure, loading, etc. Designers and orthopaedists aim at improving the performance of hip stems and reducing their sensitivity to this variability. This study focuses on the primary stability of a cementless short stem across the spectrum of patient morphology using a total of 109 femoral reconstructions, based on segmentation of patient CT scan data. A statistical approach is proposed for assessing the variability in bone shape and density [Blanc, 2012]. For each gender, a thousand new femur geometries were generated using a subset of principal components required to capture 95% of the variance in both female and male training datasets [Bah, 2013]. A computational tool (Figure 1) is then developed that automatically selects and positions the most suitable implant (distal diameter 6–17 mm, low and high offset, 126° and 133° CCD angle) to best match each CT-based 3D femur model (75 males and 34 females), following detailed measurements of key anatomical parameters. Finite Element contact models of reconstructed hips, subjected to physiologically-based boundary constraints and peak loads of walking mode [Speirs, 2007] were simulated using a coefficient of fricition of 0.4 and an interference-fit of 50μm [Abdul-Kadir, 2008]. Results showed that the maximum and average implant micromotions across the subpopulation were 100±7μm and 7±5μm with ranges [15μm, 350μm] and [1μm, 25μm], respectively. The computed percentage of implant area with micromotions greater than reported critical values of 50μm, 100μm and 150μm never exceeded 14%, 8% and 7%, respectively. To explore the possible correlations between anatomy and implant performance, response surface models for micromotion metrics were constructed using the so-called Kriging regression methodology, based on Gaussian processes. A clear nonlinear decreasing trend was revealed between implant average micromotion and the metaphyseal canal flare indexes (MCFI) measured in the medial-lateral (ML), anterio-posterior (AP) and femoral neck-oriented directions but also the average bone density in each Gruen zone. In contrast, no clear influence of the remaining clinically important parameters (neck length and offsets, femoral anteversion and CCD angle, standard canal flares, patient BMI and weight or stem size) to implant average micromotion was found. In conclusion, the present study demonstrates that the primary stability and tolerance of the short stem to variability in patient anatomy were high, suggesting no need for patient stratification. The developed methodology, based on detailed morphological analysis, accurate implant selection and positioning, prediction of implant micromotion and primary stability, is a novel and valuable tool to support implant design and planning of femoral reconstructive surgery.

This work was motivated by the need to capture the spectrum of anatomical shape variability rather than relying on analyses of single bones. A novel tool was developed that combines image-based modelling with statistical shape analysis to automatically generate new femur geometries and measure anatomical parameters to capture the variability across the population. To demonstrate the feasibility of the approach, the study used data from 62 Caucasian subjects (31 female and 31 male) aged between 43 and 106 years, with CT voxel size ranging 0.488 × 0.488 × 1.5 mm to 0.7422 × 0.7422 × 0.97 mm.

The scans were divided into female and male subgroups and high-quality subject-specific tetrahedral finite element (FE) meshes resulting from segmented femurs formed the so-called training samples. A source mesh of a segmented femur (25580 nodes, 51156 triangles) from the Visible Human dataset [Spitzer, 1996] was used for elastic surface registration of each considered target male and female subjects, followed by applying a mesh morphing strategy.

To represent the variations in bone morphology across the population, gender-based Statistical Shape Models (SSM) were developed, using Principal Component Analysis. These were then sampled using the principal components required to capture 95% of the variance in each training dataset to generate 1000 new anatomical shapes [Bryan, 2010; Blanc, 2012] and to automatically measure key anatomical parameters known to critically influence the biomechanics after hip replacement (Figure 1).

Analysis of the female and male training datasets revealed the following data for the five considered anatomical parameters: anteversion angle (12.6 ± 6.4° vs. 6.2 ± 7.5°), CCD angle (124.8 ± 4.7° vs. 126.3 ± 4.6°), femoral neck length (48.7 ± 3.8 mm vs. 52 ± 5 mm), femoral head radius (21.5 ± 1.3 mm vs. 24.9 ± 1.5 mm) and femur length (431.0 ± 17.6 mm vs. 474.5 ± 26.3 mm). However, using the SSM generated pool of 1000 femurs, the following data were computed for females against males: anteversion angle (10.5 ± 14.3° vs. 7.6 ± 7.2°), CCD angle (123.9 ± 5.8° vs. 126.7 ± 4°), femoral neck length (46.7 ± 7.7 mm vs. 51.5 ± 4.4 mm), femoral head radius (21.4 ± 1.2 mm vs. 24.9 ± 1.4 mm) and femur length (430.2 ± 16.1 mm vs. 473.9 ± 25.9 mm).

The highest variability was found in the anteversion of the females where the standard deviation in the SSM-based sample was increased to 14.3° from 6.4° in the original training dataset (Figures 2 & 3). The mean values for both females (10.5°) and males (7.6 °) were found close to the values of 10° and 7° reported in [Mishra, 2009] in 31 females and 112 males with a [2°, 25°] and [2°, 35°] range, respectively.

Femoral neck length of the female (male) subjects was 47.3 ± 6.2 mm (51.8 ± 4.1 mm) compared to 48.7 ± 3.8 mm (52 ± 5 mm) in the training dataset and 63.65 ± 5.15 mm in [Blanc, 2012] with n = 142, 54% female, 46% male and a [50.32–75.50 mm] range. For the measured CCD angle in both female (123.9 ± 5.8°) and male (126.7 ± 4°) subjects, a good correlation was found with reported values of 128.4 ± 4.75° [Atilla, 2007], 124.7 ± 7.4° [Noble, 1988] and 129.82 + 5.37° [Blanc, 2012].

In conclusion, the present study demonstrates that the proposed methodology based on gender-specific statistical shape modelling can be a valuable tool for automatically generating a large specific population of femurs to support implant design and planning of femoral reconstructive surgery.

Introduction

Implant-cement debonding at the knee has been reported previously [1]. The strength of the mechanical interlock of bone cement on to an implant surface can be associated with both bone cement and implant related factors. In addition to implant surface profile, sub-optimal mixing temperatures and waiting times prior to cement application may weaken the strength of the interlock.

Introduction

Fatigue and wear at the head/stem modular junction of large diameter total hip replacements can be exacerbated as a result of the increase in frictional torque. In vivo, a “toggling,” anterior-posterior (A-P) movement of the head taper on the trunnion may facilitate corrosion in the presence of physiological fluids, leading to increased metal ion release. Clinically, metal ion release has been linked to the formation of pseudo tumours and tissue necrosis [1].

Introduction:

Novel biomaterials may offer alternatives to metal arthroplasty bearings. To employ these materials in thin, bone conserving implants would require direct fixation to bone, using Titanium/HA coatings. Standard tests are used to evaluate the adhesion strength of coatings to metal substrates [1], versus FDA pass criteria [2]. In tensile adhesion testing, a disc is coated and uniform, uniaxial tension is exerted upon the coating-substrate interface; the strength is calculated from the failure load and surface area. Rapid failure occurs when the peak interface stress exceeds the adhesion strength, as local failure will propagate into an increasing tensile stress field.

Ceramics and reinforced polymers (e.g. carbon-fibre-reinforced PEEK), have considerably different stiffness (E) and Poisson's Ratio (ν) from the coating and implant metals. We hypothesised that this substrate-coating stiffness mismatch would produce stress concentrations at the interface edge, well in excess of the uniform stress experienced with coatings on similar stiffness metals.

Representative pre-clinical analysis is essential to ensure that novel prosthesis concepts offer an improvement over the state-of-the-art. Proposed designs must, fundamentally, be assessed against cyclic loads representing common daily activities [Bergmann 2001] to ensure that they will withstand conceivable in-vivo loading conditions. Fatigue assessment involves:

–

cyclic mechanical testing, representing worst-case peak loads encountered in-vivo, typically for 10 million cycles, or

Cyclic stresses from gait loading are super-imposed upon residual assembly stresses. In thick walled devices, the residual component is small in comparison to the cyclic component, but in thin section, bone preserving devices, residual assembly stresses may be a multiple of the cyclic stresses, so a different approach to fatigue assessment is required.

Modular devices provide intraoperative flexibility with minimal inventories. Components are assembled in surgery with taper interfaces, but resulting residual stresses are variable due to differing assembly forces and potential misalignment or interface contamination. Incorrect assembly can lead to incomplete seating and dissociation [Langdown 2007], or fracture due to excessive press-fit stress or point loading [Hamilton 2010]. Pre-assembly in clean conditions, with reproducible force and alignment, gives close control of assembly stresses. Clinical results indicate that this is only a concern with thick sectioned devices in a small percentage of cases [Hamilton 2010], but it may be critical for thin walled devices.

A pre-clinical analysis method is proposed for this new scenario, with a case study example: a thin modular cup featuring a ceramic bearing insert and a Ti-6Al-4V shell (Fig. 1). The design was assessed using FE predictions, and manufacturing variability from tolerances, surface finish effects and residual stresses was assessed, in addition to loading variability, to ensure physical testing is performed at worst case:

–

assembly loads were applied, predicting assembly residual stress, verified by strain gauging, and a range of service loads were superimposed.

The predicted worst-case stress conditions were analysed against three ‘constant life’ limits [Gerber, 1874, Goodman 1899, Soderberg 1930], a common aerospace approach, giving predicted safety factors. Finally, equivalent fatigue tests were conducted on ten prototype implants.

Taking a worst-case size (thinnest-walled 48 mm inner/58 mm outer), under assembly loading the peak tensile stress in the titanium shell was 274 MPa (Fig. 2). With 5kN superimposed jogging loading, at an extreme 75° inclination, 29 MPa additional tensile stress was predicted. This gave mean fatigue stress of 288.5 MPa and stress amplitude of 14.5 MPa (R=0.9). Against the most conservative infinite life limit (Soderberg), the predicted safety factor was 2.40 for machined material, and 2.03 for forged material, or if a stress-concentrating surface scratch occurs during manufacturing or implantation (Fig. 3). All cups survived 10,000,000 fatigue cycles.

This study employed computational modelling and physical testing to verify the strength of a joint prosthesis concept, under worst case static and fatigue loading conditions. The analysis technique represents an improvement in the state of the art where testing standards refer to conventional prostheses; similar methods could be applied to a wide range of novel prosthesis designs.

INTRODUCTION

Resurfacing prostheses are implanted by impaction onto the prepared femoral head. Ceramic resurfacings can be proposed as an alternative to metal implants, combining bone conservation with mitigation of sensitivity reaction risks. With low wall-thickness required for bone conservation, their strength must be verified. This study aimed to assess a ceramic resurfacing prosthesis' strength under surgical loads using a computational model, tuned and verified with physical tests.

Implant positioning is a critical factor in assuring the primary stability of cementless Total Hip Replacements (THRs). Although it is under the direct control of surgeons, finding the optimal implant position and achieving a perfect fit remain a challenge even with the advent of computer navigation. Placement of the femoral stem in an excessive ante/retroversion or varus/valgus orientation can be detrimental to the performance of THR. To determine the effect of such malalignment, finite element (FE) computer modelling is often used. However, this can be time consuming since FE meshes must be repeatedly generated and solved each time for a range of defined implant positions. In the present study, a mesh morphing technique is developed for the automatic generation of FE models of the implanted femur; in this way, many implant orientations can be investigated in a single analysis.

An average femur geometry generated from a CT scan population of 13 male and 8 female patients aged between 43 and 84 years was considered. The femur was virtually implanted with the Furlong HAC titanium alloy stem (JRI Ltd, Sheffield, UK) and placed in the medullary canal in a baseline neutral nominal position. The head of the femur was then removed and both femur and implant volumes were joined together to form a single piece that was exported into ANSYS11 ICEM CFD (ANSYS Inc., 2008) for meshing. To adequately replicate implant ante/retroversion, varus/valgus or anterior/posterior orientations, the rigid body displacement of the implant was controlled by three rotations with respect to a local coordinate system. One hundred different implant positions were analysed and the quality of the morphed meshes analysed for consistency.

To check the morphed meshes, corresponding models were generated individually by re-positioning the implant in the femur. Selected models were solved to predict the strain distribution in the bone and the boneimplant relative micromovements under joint and muscle loading. A good agreement was found for bone strains and implant micromotions between the morphed models and their individually run counterparts. In the postprocessing stage further metrics were analysed to corroborate the findings of the morphed and individually run models. These included: average and maximum strains in bone interface area and its entire volume, percentage of bone interface area and its volume strained up to and beyond 0.7%; implant average and maximum micromotions and finally percentages of implant area undergoing reported critical micromotions of 50 μm, 100 μm and 150 μm for bone in growth. Excellent correlation was observed in all cases.

In conclusion, the proposed technique allowed an automatic generation of FE meshes of the implanted femur as the implant position varies; the required computational resources were considerably reduced and the biomechanical response was evaluated. This model forms a good basis for the development of a tool for multiple statistical analyses of the effects of implant orientation in pre-clinical studies.

Excessive implant migration and micromotion have been related to eventual implant loosening. The aim of this project is to develop a computational tool that will be able to predict the mechanical performance of a cementless implant in the presence of uncertainty, for example through variations in implant alignment or bone quality. To achieve this aim, a computational model has to be developed and implemented. However, to gain confidence in the model, it should be verified experimentally. To this end, the present work investigated the behavior of a cementless implant experimentally, and compared the results with a computational model of the same test setup.

A synthetic bone (item 3406, Sawbones Europe AB, Sweden) was surgically implanted with a Furlong cementless stem (JRI, Sheffield, UK) in a neutral position and subjected to a compression fatigue test of −200 N to −1.6 kN at a frequency of 0.5 Hz for 50000 cycles. Measurements of the micromotion and migration were carried out using two linear variable differential transducers and the strain on the cortex of the femur was measured by a digital image correlation system (Limess Messtechnik & Software Gmbh).

A three-dimensional model was generated from computed tomography scans of the implanted Sawbone and converted to a finite element (FE) model using Simple-ware software (Simpleware Ltd, Exeter, UK). Face-to-face elements were used to generate a contact pair between the Sawbone and the implant. A contact stiffness of 6000 N/m and a friction coefficient of 0.3 were assigned. The analysis simulated a load of −1.6 kN applied to the head of the implant shortly post implantation. The motions and strains recorded in the experiment were compared with the predictions from the computational model. The micromotion (the vertical movement of the implant during a single load cycle), was measured at the proximal shoulder, at the distal tip of the implant and at the bone-implant interface. The maximum value calculated proximally using FE was 61.3 μm compared to the experimental value of 59.6 μm. At the distal end, the maximum micromotion from FE was 168.9 μm compared to 170 μm experimentally. As a point of reference, some authors have suggested that in vivo, fibrous tissue formation may take place at the bone-implant interface when the micromotion is above 150 μm. The maximum micromotion found computationally at this interface was 99 μm which is below the threshold value defined. The longitudinal strain over the surface of the bone was variable and reached values of up to 0.15% computationally and 0.4% experimentally; this may be related to the coordinate systems used. However, it was noted that digital image correlation identified qualitatively similar strain patterns, and has great potential for measuring low level surface strains on bone.

In conclusion, the good correlation between the computational modelling and experimental tests provides confidence in the model for further investigations using probabilistic analyses where more complex configurations (for example change in implant alignment) can be analyzed.

Although resurfacing hip replacement (RHR) is associated with a more demanding patient cohort, it has achieved survivorship approaching that of total hip replacement. Occasional failures from femoral neck fracture, or migration and loosening of the femoral head prosthesis have been observed, the causes of which are multifactorial, but predominately biomechanical in nature. Current surgical technique recommends valgus implant orientation and reduction of the femoral offset, reducing joint contact force and the femoral neck fracture risk. Radiographic changes including femoral neck narrowing and ‘pedestal lines’ around the implant stem are present in well performing hips, but more common in failing joints indicating that loosening may involve remodelling. The importance of prosthesis positioning on the biomechanics of the resurfaced joint was investigated using finite element analysis (FEA).

Seven FE models were generated from a CT scan of a male patient: the femur in its intact state, and the resurfaced femur with either a 50mm or 52mm prosthesis head in

neutral orientation,

The fracture risk during trauma was investigated for stumbling and a sideways fall onto the greater trochanter, by calculating the volume of yielding bone. Remodelling was quantified for normal gait, as the percentage volume of head and neck bone with over 75% post-operative change in strain energy density for an older patient, and 50% for a younger patient.

Resurfacing with the smaller, 50mm prosthesis reduced the femoral offset by 3.0mm, 4.3mm and 5.1mm in varus, neutral and valgus orientations. When the 52mm head was used, the natural joint centre could be recreated rrespective of orientation, without notching the femoral neck. The 50mm head reduced the volume of yielding femoral neck bone relative to the intact femur in a linear correlation with femoral offset. When the natural femoral offset was recreated with the 52mm prosthesis, the predicted neck fracture load in stumbling was decreased by 9% and 20% in neutral and varus orientations, but remained in line with the intact bone when implanted with valgus orientation. This agrees with clinical experience and justifies currently recommended techniques. In oblique falling, the neck fracture load was again improved slightly when the femoral offset was reduced, and never fell below 97% of the natural case for the larger implant in all orientations.

Predicted patterns of remodelling stimulus were consistent with radiographic clinical evidence. Stress shielding increased slightly from varus to valgus orientation, but was restricted to the superior femoral head in the older patient. Bone densification around the stem was predicted, indicating load transfer. Stress shielding only extended into the femoral neck in the young patient and where the femoral offset was reduced with the 50mm prosthesis. The increase in remodelling correlated with valgus orientation, or reduced femoral offset. The trend would become more marked if this were to reduce the joint contact force, but there was no such correlation for the 52mm prosthesis, when the natural femoral offset was recreated. Only in extreme cases would remodelling alone be sufficient to cause visible femoral neck narrowing, i.e. patients with a high metabolism and considerably reduced femoral offset, implying that other factors including damage from surgery or impingement, inflammatory response or retinacular blood supply interruption may also be involved in femoral neck adaptation.

The results of this FEA biomechanical study justify current surgical techniques, indicating improved femoral neck fracture strength in stumbling with valgus position. Fracture risk under oblique falling was less sensitive to resurfacing. Furthermore, the results imply that reduced femoral offset could be linked to narrowing of the femoral neck; however the effects of positioning alone on bone remodelling may be insufficient to account for this. The study suggests that surgical technique should attempt to recreate the natural head centre, but still aim primarily for valgus positioning of the prosthesis, to reduce the femoral neck fracture risk.

The Acoustic Emission (AE) technique has been described as possessing ‘many of the qualities of an ideal damage-monitoring technique’, and the technique has been used successfully in recent years to aid understanding of failure mechanisms and damage accumulation in bone cement during de-bonding of the cement-metal interface fatigue loading, pre-load cracking during polymerisation and to describe and locate damage within an entire stem construct. However, most investigations to date have been restricted to in-vitro testing using surface mounted sensors. Since acoustic signals are attenuated as they travel through a material and across interfaces, it is arguable that mounting the sensors on the bone surface to investigate damage mechanisms occurring within the bone cement layer is not ideal. However, since direct access to the bone cement layer is not readily available, the bone surface is often the only practical option for sensor positioning.

This study has investigated the potential for directly embedding AE sensors within the femoral stem itself. This enables a permanent bond between the sensor and structure of interest, allows closer proximity of the sensor to the region of interest, and eliminates potential complications and variability associated with fixing the sensor to the sample. Data is collected during in-vitro testing of nominal implanted constructs, and information from both embedded and externally mounted AE sensors are compared and corroborated by microComputed Tomography (micro-CT) images taken both before and after testing.

The use of multiple AE sensors permitted the location as well as the chronology of damage events to be obtained in real time and analysed without the need for test interruption or serial sectioning of the test samples. Parametric analysis of the AE signal characteristics enabled those events likely to be associated with cracking as opposed to interfacial rubbing or de-bonding to be differentiated and it was shown that the embedded sensors gave a closer corroboration to observed damage using micro-CT and were less affected by unwanted sources of noise.

The results of this study have significant implications for the use of AE in assessing the state of total hip replacement (THR) constructs both in-vitro and potentially in-vivo. Incorporating the sensors into the femoral stem during in-vitro testing allows for greater repeatability between tests since the sensors themselves do not need to be removed and re-attached to the specimen. To date, all in-vivo studies attempting to use the AE technique to monitor the condition of any replacement arthroplasty device have used externally mounted sensors and suffered from the attenuation of acoustic information through flesh and skin. It is hypothesised that the use of directly embedded AE sensors may provide the first steps towards an in-vivo, cost effective, user friendly, non-destructive system capable of continuously monitoring the condition of the implanted construct and locating the earliest incidences of damage initiation.

Uncemented hip implants commonly have porous coated surfaces that enhance the mechanical interlock with bone, encourage bone ingrowth and promote the formation of a stable interface between prosthesis and bone. However, the presence of tissue, either fibrous or with parts of osseous tissue, at the interface between the implant and the bone has been commonly observed after a few years in vivo. The exact mechanisms that govern the type of tissues formed at the interface are not fully understood and several theories have been proposed. This study aims to employ finite element analysis (FEA) to simulate tissue formation and differentiation around the AML (DePuy, Warsaw, USA) femoral implant by employing a tissue differentiation algorithm based on a mechanoregulatory hypothesis of fracture healing.

FE models of the femur were generated using computer tomography (CT) scans. The AML prosthesis was then implanted into the bone and a granulation tissue layer of 0.75mm was created around the implant. The mechanoregulatory hypothesis of Carter et al (J.Orthop, 1988) originally developed to explain fracture healing was used with selected modifications, most notably the addition of a quantitative module to the otherwise qualitative algorithm. The tendency of ossification in the original hypothesis was modified to simulate tissue differentiation to bone, cartilage or fibrous tissue. Normal walking and stair climbing loads were used for a specified number of cycles reflecting typical patient activity post surgery.

The transformation of granulation tissue to one of the three simulated tissue types was evident as the iterations progressed. The majority of the tissue type formed initially was cartilage and bone (~40% each), and occupied the mid to distal regions of the implant respectively. After tissue stabilisation, the prominent tissue type was bone (65%), occupying most of the mid-distal regions with a significant decline in cartilage tissue formed. This has been shown in clinical retrieval studies with the same implant, where maximum bone ingrowth is in the mid-distal regions of the implant, directly corresponding to the region where there is minimal micromotion. This would be the case with a diaphyseal fixation, which most AML prostheses employ for stability. Fibrous tissue formation was limited to the proximal-medial regions (~10%), with the remainder of the proximal regions filled with cartilage tissue. In addition, predicted bone formation was along the lines of the more stable cartilage tissue as opposed to directly replacing fibrous tissue. The formation of bone would require repeated periods of minimal micromotion and stress at the interface tissue; this was facilitated by the presence of cartilage tissue around the mid regions of the implant. The micromotion and interface stresses in the proximal regions of the implant were too high to encourage bone ingrowth, resulting in the presence of tissue that remained fibrous throughout the process.

The FE model, employing a very simple tissue differentiation hypothesis and algorithm was able to predict the formation of different tissues at the interface. Initial bone formation was rapid, occupying the distal regions of the implant, and then gradually occupying a larger portion of the mid-regions around the implant. The proximal regions were largely occupied by a combination of fibrous and cartilage tissue. Overall, the presence of bone and cartilage tissue accounted for nearly 85% of the tissue formed which would suggest a very stable interface as predicted by the Carter’s hypothesis.