Beyond the Airbrush: Applications of Digital Image Correlation in Vascular Biomechanics

  • Susan M. LessnerEmail author
  • John F. Eberth
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Before digital image correlation (DIC) could find widespread application for strain measurement in biological soft tissues, there were a number of technical challenges that had to be addressed. The mechanical behavior of soft tissues depends significantly on hydration state; therefore, both application of a high-contrast speckle pattern and imaging must be achieved while maintaining the specimen in a fully hydrated state. Furthermore, soft tissues such as arteries typically undergo finite deformations under a physiologically relevant loading range. While hydration can be achieved by submerging the sample in a medium having appropriate osmolarity, pH, and ionic strength, imaging submerged objects introduces its own set of challenges due to refractive index changes. The issue of sample hydration also requires consideration of alternative approaches to speckle pattern creation, beyond the classic “airbrush method,” since ideally the specimen must not be allowed to dry out during pattern application. For samples that are submerged, the speckle pattern must be firmly bonded to the specimen and water-resistant, in addition to deforming with the specimen, often outside the small-strain regime. In some specific instances, nature provides a hand through the presence of intrinsic fine-scale structure in the specimen that, with innovative staining or imaging techniques, can serve as a satisfactory “speckle pattern.” Added to these issues is the difficulty of imaging small, often irregularly shaped specimens that can degrade rapidly over time. In collaboration with Dr. Sutton’s group, my colleagues and I have developed a number of approaches to address the issues of hydrating and speckling soft tissues for measurement of local strains in blood vessels at multiple length scales, with a particular focus on the mouse aorta and carotid artery.


Digital image correlation Soft tissues Speckle pattern Artery Nuclear stain 


  1. 1.
    Lecompte, D., Smits, A., Bossuyt, S., Sol, H., Vantomme, J., Van Hemelrijck, D., Habraken, A.: Quality assessment of speckle patterns for digital image correlation. Opt. Lasers Eng. 44, 1132–1145 (2006)CrossRefGoogle Scholar
  2. 2.
    Pan, B., Lu, Z., Xie, H.: Mean intensity gradient: an effective global parameter for quality assessment of the speckle patterns used in digital image correlation. Opt. Lasers Eng. 48, 469–477 (2010)CrossRefGoogle Scholar
  3. 3.
    Reu, P.L., Miller, T.J., Sutton, M., Wang, Y.: Uncertainty Quantification for Digital Image Correlation. Sandia National Laboratories (SNL-NM), Albuquerque, NM (2009)Google Scholar
  4. 4.
    Wang, Y.Q., Sutton, M.A., Bruck, H.A., Schreier, H.W.: Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements. Strain. 45, 160–178 (2009)CrossRefGoogle Scholar
  5. 5.
    Sutton, M.A., Orteu, J.J., Schreier, H.: Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications. Springer, New York, NY (2009)Google Scholar
  6. 6.
    Forbes, R., Cooper, A., Mitchell, H.: The composition of the adult human body as determined by chemical analysis. J. Biol. Chem. 203, 359–366 (1953)Google Scholar
  7. 7.
    Zhang, D., Arola, D.D.: Applications of digital image correlation to biological tissues. J. Biomed. Opt. 9, 691–699 (2004)CrossRefGoogle Scholar
  8. 8.
    Zhang, D.S., Eggleton, C.D., Arola, D.D.: Evaluating the mechanical behavior of arterial tissue using digital image correlation. Exp. Mech. 42, 409–416 (2002)CrossRefGoogle Scholar
  9. 9.
    Sutton, M.A., Ke, X., Lessner, S.M., Goldbach, M., Yost, M., Zhao, F., Schreier, H.W.: Strain field measurements on mouse carotid arteries using microscopic three-dimensional digital image correlation. J. Biomed. Mater. Res. A. 84, 178–190 (2008)CrossRefGoogle Scholar
  10. 10.
    Avril, S., Badel, P., Duprey, A.: Anisotropic and hyperelastic identification of in vitro human arteries from full-field optical measurements. J. Biomech. 43, 2978–2985 (2010)CrossRefGoogle Scholar
  11. 11.
    Stone, J.W., Sisco, P.N., Goldsmith, E.C., Baxter, S.C., Murphy, C.J.: Using gold nanorods to probe cell-induced collagen deformation. Nano Lett. 7, 116–119 (2007)CrossRefGoogle Scholar
  12. 12.
    Aaron, J., de La Rosa, E., Travis, K., Harrison, N., Burt, J., José-Yacamán, M., Sokolov, K.: Polarization microscopy with stellated gold nanoparticles for robust, in-situ monitoring of biomolecules. Opt. Express. 16, 2153–2167 (2008)CrossRefGoogle Scholar
  13. 13.
    Sokolov, K., Follen, M., Aaron, J., Pavlova, I., Malpica, A., Lotan, R., Richards-Kortum, R.: Real-time vital optical imaging of precancer using anti-epidermal growth factor receptor antibodies conjugated to gold nanoparticles. Cancer Res. 63, 1999 (2003)Google Scholar
  14. 14.
    Ning, J., Braxton, V.G., Wang, Y., Sutton, M.A., Wang, Y., Lessner, S.M.: Speckle patterning of soft tissues for strain field measurement using digital image correlation: preliminary quality assessment of patterns. Microsc. Microanal. 17, 81–90 (2011)CrossRefGoogle Scholar
  15. 15.
    Ning, J., Xu, S., Wang, Y., Lessner, S.M., Sutton, M.A., Anderson, K., Bischoff, J.E.: Deformation measurements and material property estimation of mouse carotid artery using a microstructure-based constitutive model. J. Biomech. Eng. 132, 121010 (2010)CrossRefGoogle Scholar
  16. 16.
    Watson, S.R., Lessner, S.M.: (Second) harmonic disharmony: nonlinear microscopy shines new light on the pathology of atherosclerosis. Microsc. Microanal. 22, 589–598 (2016)CrossRefGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  1. 1.Department of Cell Biology and AnatomyUniversity of South CarolinaColumbiaUSA

Personalised recommendations