Use of a statistical model of the whole femur in a large scale, multi-model study of femoral neck fracture risk
Introduction
The vast majority of orthopaedic computational studies are performed using a single bone model. The derived results are then extrapolated to try to draw conclusions for the population as a whole, overlooking the inherent and significant interpatient variability found in both bone geometry and bone quality (Prendergast, 1997, Viceconti et al., 1998, Keyak et al., 1990). With reference to the performance of orthopaedic implants, such intersubject variations have been shown to make a dramatic difference to the success of otherwise comparable joint replacement procedures (Kobayashi et al., 2000, Wong et al., 2005). In daily activity, intersubject variability has been seen to dominate intertask variability in a computational study of bone-implant micromotion driven by in vivo data from an instrumented femoral prosthesis (Pancanti et al., 2003). In reaction to this shortcoming, patient specific modelling techniques have begun to be developed. These use high level imaging modalities such as computer tomography (CT) to build computational models of the set of patient or cadaveric anatomies being assessed, often then validating finite element analyses of these with experimental tests (Testi et al., 1999, Cody et al., 1999, Keyak et al., 1990, Viceconti et al., 2004, Radcliffe and Taylor, 2007). In this way it is possible to gain an understanding of whether the results seen are down to the tests being performed or the anatomy of the subject. However, a major barrier preventing multi-subject finite element studies from becoming commonplace is the task of creating multiple models from sources such as CT scans. Without robust and reliable automated model generation techniques this is a time consuming, laborious task and relies on access to high quality image data, which is often scarce (Viceconti et al., 1998, Radcliffe and Taylor, 2007). This work proposes the use of statistical modelling as a source of FE bone models to provide a potential solution to this problem.
Statistical models aim to capture the variation possible within a class of shapes by analysing a set of training data. The principles of shape modelling using principal component analysis (PCA) were illustrated by Cootes and Taylor (Cootes et al., 1995). It was shown how a model could be trained on a set of possible shapes, analysed using PCA and its outputs used in two ways; firstly to investigate the main modes of variation in the training data and secondly to generate new, realistic instances of that shape. Further work incorporated texture, described by greylevel, into the model (Cootes and Taylor, 2001). Originally these techniques were developed within computer vision and therefore used with two-dimensional images, applying them to three-dimensional shapes vastly increases the computational complexity. Any methods relying on construction through manual landmarking become highly inefficient and impractical to apply. Rueckert et al., 1999, Rueckert et al., 2003 solved the problem of matching three-dimensional shapes using free form deformation of B-splines. This technique has been applied to a variety of biomedical problems from modelling bones such as the proximal femur and humorous (Querol et al., 2006, Couteau et al., 2000, Yang et al., 2004) to tracking soft tissue changes in breast and brain MRIs (Rueckert et al., 1999, Rueckert et al., 2003).
The aim of this study was to apply the statistical femur model to the problem of proximal femoral fracture, and asses its ability to produce meaningful results. Meaningful, being that the results show comparable trends to existing published investigations. A femoral neck fracture risk (FNFR) investigation was chosen for the present study as a well investigated problem from computational, experimental and clinical perspectives. The majority of FNF occur in elderly women and are the result of a fall (Lotz et al., 1991, Koval and Zuckerman, 1994), with around 250–300,000 cases reported in the US each year (Cummings and Nevitt, 1989, Cooper et al., 1992). The injury is potentially devastating for this age group, in many cases leading to reduced mobility, long term disability and reduced capacity for independent living (Marks et al., 2003). Mortality rates are significant at 15–25% within 6 months of injury, rising to 30–40% at 1 year (Cummings et al., 1985, Keene et al., 1993). Many studies, mainly based on clinical data, have investigated fracture risk in relation to femur geometry and bone quality (Theobald et al., 1998, Peacock et al., 1998, Bergot et al., 2002, Michelotti and Clark, 1999, Gnudi et al., 1999). Several computational studies, often in conjunction with experimental work, have also tried to predict fracture loads and location (Lotz et al., 1991, Keyak et al., 1997, Keyak et al., 2001a, Cody et al., 1999, Cheng et al., 1997a, Majumder et al., 2007, Bessho et al., 2007). However, these have often been limited to investigating a single bone or at best a small set of between 15 and 20 examples. This study conducted a FNFR study using 1000 generated femurs created from a statistical model, then compared the results to femur and fracture characteristics found by previous fracture risk studies.
Section snippets
Methods
The first stage of the study was the creation and sampling of a statistical model of the whole human femur using PCA, a detailed explanation of which is available in Appendix I. The model was trained on femurs generated from CT scans of 8 female and 13 male subjects with a mean age of 68, ranging from 43 to 84 years. Each femur was extracted by semi-automated segmentation of bone with grey level thresholding tools and manual slice-by-slice corrections using (Mercury Computer Systems,
Results
By the conservative failure criteria defined in this study 28 of the 1000 femurs tested were identified as being at risk of failure. These 28 models were grouped together and their geometric and material property characteristics compared against the 972 femurs which survived the simulation. The strain distributions are clearly different, with the low risk group on average showing almost no bone exceeding 0.4% strain, where the at risk group show notable percentages above this level (Fig. 3).
Discussions
The current study was able to elegantly run a large scale, multi-bone model, finite element analysis for the first time without significant manual intervention. High mesh quality was ensured by incorporating element distortion checks, allowing direct use of the models in an FE solver without risk of failure or poor results. This allowed the whole analysis to be completely automated, requiring no manual intervention to generate 1000 FE femurs models with individual material properties, apply
Conflict of interest statement
Rebecca Bryan and Prasanth Nair have no conflicts. Mark Taylor is a retained consultant to Finsbury Orthopaedics and DePuy International.
Acknowledgements
This research has been possible thanks to CT data kindly provided by DePuy International and East Sussex Hospital Trust, and funding received from Technology Strategy Board (UK). Thanks also to Andrew Hopkins for the use of material property extraction software.
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