Elsevier

Journal of Biomechanics

Volume 44, Issue 9, 3 June 2011, Pages 1666-1672
Journal of Biomechanics

Patient-specific finite element analysis of the human femur—A double-blinded biomechanical validation

https://doi.org/10.1016/j.jbiomech.2011.03.024Get rights and content

Abstract

Patient-specific finite element (PSFE) models based on quantitative computer tomography (qCT) are generally used to “predict” the biomechanical response of human bones with the future goal to be applied in clinical decision-making. However, clinical applications require a well validated tool that is free of numerical errors and furthermore match closely experimental findings. In previous studies, not all measurable data (strains and displacements) were considered for validation. Furthermore, the same research group performed both the experiments and PSFE analyses; thus, the validation may have been biased. The aim of the present study was therefore to validate PSFE models with biomechanical experiments, and to address the above-mentioned issues of measurable data and validation bias. A PSFE model (p-method) of each cadaver femur (n=12) was generated based on qCT scans of the specimens. The models were validated by biomechanical in-vitro experiments, which determined strains and local displacements on the bone surface and the axial stiffness of the specimens. The validation was performed in a double-blinded manner by two different research institutes to avoid any bias. Inspecting all measurements (155 values), the numerical results correlated well with the experimental results (R2=0.93, slope 1.0093, mean of absolute deviations 22%). In conclusion, a method to generate PSFE models from qCT scans was used in this study on a sample size not yet considered in the past, and compared to experiments in a douple-blinded manner. The results demonstrate that the presented method is in an advanced stage, and can be used in clinical computer-aided decision-making.

Introduction

Patient-specific finite element (PSFE) modeling based on quantitative computer tomography (qCT) is used to “predict” the biomechanical response of human bones. The computed data (usually displacements, strains and stresses) is interpreted, for example, to “predict” bone fracture risk, optimize implants and diagnose the severity of osteoporosis. These PSFE models are advocated for use in clinical practice (Keaveny, 2010).

Only a limited number of studies were dedicated to the systematic validation of PSFE models of femoral bones by comparison to in-vitro experiments, and not all possible measurable data (strains and displacements) were considered. In most studies conventional h-version FE methods (h-FEMs) were used (see e.g. (Keyak et al., 1990; Cody et al., 1999; Helgason et al., 2008b; Schileo et al., 2007)) having mostly inhomogeneous distribution of isotropic material properties obtained by assigning constant distinct values to different elements, causing the material properties to become mesh dependent (Taddei et al., 2007). Furthermore, usually only strains were reported and usually on a small cohort of bones. For example, in Helgason et al. (2008b) a single femur was investigated, which did not show a satisfactory correlation between computed and measured strains (stresses were though well correlated). Good predictions for strains are reported by Schileo et al. (2007) and latter improved in Schileo et al. (2008) on a larger cohort (8 femurs, showing a correspondence between PSFE and experiments of R2=0.91, 0.95 and slope 1.01, 0.97) and by Bessho et al. (2007) on a cohort of 11 femurs.

Recent studies on qCT based patient-specific high-order FEs (PSHOFE) with inhomogeneous material properties were shown to predict very well both strains and displacements on a cohort of three femurs and various loading scenarios (Yosibash et al., 2007a, Trabelsi et al., 2009). In all previous studies both the experiments and PSFE/PSHOFE analyses were performed by the same group, thus the validation may have been biased. However, clinical applications require a well validated tool, i.e. it is required to demonstrate (first in-vitro) that the PSFE results are free of numerical errors and furthermore match closely experimental findings—a requirement which to our opinion has not yet been fully met.

The aim of the present study was therefore to validate PSHOFE models with biomechanical experiments, by comparing all measurable quantities—strains, displacement magnitudes and overall bone stiffness. Furthermore, the validation was accomplished with a sufficiently large sample of fresh-frozen bone specimens (n=12) by two different research institutes in a double-blinded process to avoid any bias.

Section snippets

Materials and methods

A PSHOFE model of each human cadaver femur was generated based on qCT scans of the specimens. The reliability of these models was validated by in-vitro biomechanical experiments, which determined strains and local displacements on the bone surface and the axial stiffness of the specimens. The validation was performed in a double-blinded manner by two different research institutes. The first institute (BGU) generated the PSHOFE models and performed the FEAs, and was blinded to the experimental

Experimental results

All tests resulted in repeatable strain values with standard deviations of 0–3% (mean 2%) in-between the three test cycles of each load level. All strain–load-curves showed a linear correlation by at least 98%. All tests resulted in repeatable local displacement values with standard deviations of 2–19% (mean 8%) in-between the three test cycles of each load level. All displacement–load-curves showed a linear correlation by at least 75%. Therefore, the experimental results were analyzed only for

Discussion

The aim of this study was to demonstrate the reliability of PSHOFE models. It has been shown that:

  • (a)

    qCT-based PSHOFE models are capable of predicting very well all measured quantities, i.e. displacement magnitudes and strains for a sufficiently sized sample of femurs taken from donors of both genders and a diversity of ages.

  • (b)

    The predictions are bias-free, as neither the group that performed the FEAs nor the group who performed the experiments knew of each other's results until both activities were

Conflict of interest statement

None declared.

Acknowledgments

The first two authors acknowledge the generous support of the Technical University of Munich—Institute for Advanced Study, funded by the German Excellence Initiative.

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