The advent of Elastic Stable Intramedullary Nailing has revolutionised the conservative treatment of long human bone fractures in children (Metaizeau, 1988; Metaizeau et al., 2004). Unfortunately, failures still occur due to excessive bending and fatigue (Linhart et al., 1999; Lascombes et al., 2006), bone refracture or nail failure (Bråten et al., 1993; Weinberg et al., 2003). Ideally, during surgery, nail insertion into the diaphyseal medullary canal should not interrupt or injure cartilage growth; nails should provide an improved rigidity and fracture stabilisation.

This study aims at comparing deflections and stiffnesses of nail-bone assemblies: standard cylindrically-shaped nails (MI) vs. new cylindrical nails (MII) with a flattened face across the entire length allowing more inertia and a curved tip allowing better penetration into the cancellous bone of the metaphysis (Figure 1). MII exhibits a section with two parameters: a diameter C providing nail stiffness and a height C' providing practical dimension when both nails are crossed at the isthmus of the diaphysis: C/C' is set to 1.25 for all MII nails.

A CT scan of a patient aged 22 years was used to segment a 3D model of a 471mm-long right femur model. The medullary canal diameters at the isthmus are 10.8mm and 11.4mm in the ML and AP direction, respectively. Titanium-made CAD models of MI (Ø=4mm) and MII (flat face: Ø=5mm) were pre-curved to maintain their flat face and carefully placed and positioned according to surgeon's instructions. Both nails were inserted via lateral holes in the distal femur with their extremities either bumping against the cortex or lying in the trabecular bone.

Transverse and comminuted fractures were simulated (Figure 1). For each assembly, a Finite Element (FE) tetrahedral mesh was generated (∼100181 nodes and 424398 elements). Grey-scale levels were used to assign heterogeneous material properties to the bone (E=6850 ρ^1.49 (Morgan et al., 2003)). Two modes of loading were considered: 4-point bending (varus and recurvatum: F_max=6000N) and internal torsion (M_max=70kNmm). This led to the simulation of 15 FE models, including a reference intact femur.

Results show that in valgus, for the transverse (comminuted) fracture, the mean displacement of the assembly decreased by around 50%: from 15.24mm (27.49mm) to 8.15mm (13.85mm) for MI and MII, respectively, compared to 3.59mm for the intact bone. The assembly stiffness increased by 87% and 99% for transverse and comminuted fracture, respectively (Table 1). Similar trends were found in recurvatum with higher increases in assembly stiffness of 170% and 143% for transverse and comminuted fracture, respectively (Table 1). In torsion, for the transverse (comminuted) fracture, the measured angle of rotation decreased from: 0.43rad (0.66rad) to 0.22rad (0.43rad) for MI and MII, respectively, compared to 0.09rad for the intact bone. This corresponded to an increase of 95% and 55% in assembly stiffness for transverse and comminuted fracture, respectively.

In conclusion, using the 5mm-diameter new nails (MII) for the same intramedullar space, during either bending or torsion, assemblies were always stiffer than when using standard cylindrical nails.

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.

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.