Abstract
Measuring the mechanical properties of soft tissues presents three interlinked problems. First, we must carry out experimental measurements to quantify the actual behaviour of the tissue. Second, we need to represent this by some kind of mathematical model, which typically has to be solved using numerical techniques such as the finite element (FE) method. Third, we need to find the parameter values in the model that best match the experiment and to quantify the uncertainty in the resulting material properties. Experimental measurements present numerous difficulties in comparison with conventional engineering materials and care is needed in the choice of test method, sample selection and preparation, calibration and interpretation of the results. Typically an optical technique may be needed to measure the deformation, such as digital image correlation (DIC). FE models of soft tissues are inherently difficult to solve because of their extreme nonlinearity and the typical stiffening behaviour with increasing deformation which leads to numerical instabilities. Possible ways to reduce convergence problems and increase the reliability of these models are discussed. The most common method to find the parameter values that match an experiment is to use an optimisation algorithm to try to find the parameters that best match the experimental results. However this is slow and there is no way of knowing whether the best parameters have been found or what range of other values could also be compatible with the experiment. A better approach is to generate a statistical emulator that predicts the result of the model and then to evaluate a wide range of parameter values in order to find the range of values that could be compatible with the experiment. This gives revealing insights into the uncertainty of the procedure and the validity of the final results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Legerlotz, K., Riley, G. P., & Screen, H. R. C. (2010). Specimen dimensions influence the measurement of material properties in tendon fascicles. Journal of Biomechanics, 43(12), 2274–2280.
Hayes, W. C., Keer, L. M., Herrmann, G., & Mockros, L. F. (1972). A mathematical analysis for indentation tests of articular cartilage. Journal of Biomechanics, 5(5), 541–551.
Groves, R. B., Coulman, S. A., Birchall, J. C., & Evans, S. L. (2012). Quantifying the mechanical properties of human skin to optimise future microneedle device design. Computer Methods in Biomechanics and Biomedical Engineering, 15(1), 73–82.
Genovese, K., Montes, A., MartÃnez, A., & Evans, S. L. (2009). Full-surface deformation measurement of anisotropic tissues under indentation. Medical Engineering and Physics, 37(5), 484–493.
Hendriks, F. M., Brokken, D. V., Van Eemeren, J. T. W. M., Oomens, C. W. J., Baaijens, F. P. T., & Horsten, J. B. A. M. (2003). A numerical-experimental method to characterize the non-linear mechanical behaviour of human skin. Skin Research and Technology, 9(3), 274–283.
Romo, A., Badel, P., Duprey, A., Favre, J.-P., & Avril, S. (2014). In vitro analysis of localized aneurysm rupture. Journal of Biomechanics, 47(3), 607–616.
Screen, H. R. C., & Evans, S. L. (2015). Measuring strain distributions in the tendon using confocal microscopy and finite elements. The Journal of Strain Analysis for Engineering Design, 44(5), 327–335.
Mahmud, J., Holt, C. A., & Evans, S. L. (2010). An innovative application of a small-scale motion analysis technique to quantify human skin deformation in vivo. Journal of Biomechanics, 43(5), 1002–1006.
Moerman, K. M., Sprengers, A. M. J., Simms, C. K., Lamerichs, R. M., Stoker, J., & Nederveen, A. J. (2011). Validation of SPAMM tagged MRI based measurement of 3D soft tissue deformation. Medical Physics, 38(3), 1248–1260.
Hager, W. W., & Zhang, H. (2005). A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM Journal on Optimization, 16(1), 170–192.
Broyden, C. J. (1970). The convergence of a class of double-rank minimization algorithms 1. general considerations. IMA Journal of Applied Mathematics, 6(1), 76–90.
Evans, S. L., & Holt, C. A. (2009). Measuring the mechanical properties of human skin in vivo using digital image correlation and finite element modelling. The Journal of Strain Analysis for Engineering Design, 44(5), 337–345.
Kennedy, M. C., & O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3), 425–464.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 CISM International Centre for Mechanical Sciences
About this chapter
Cite this chapter
Evans, S. (2017). How Can We Measure the Mechanical Properties of Soft Tissues?. In: Avril, S., Evans, S. (eds) Material Parameter Identification and Inverse Problems in Soft Tissue Biomechanics. CISM International Centre for Mechanical Sciences, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-45071-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-45071-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45070-4
Online ISBN: 978-3-319-45071-1
eBook Packages: EngineeringEngineering (R0)