June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Best Practices for Estimating Lens Mechanical Properties Using a Compression Test
Author Affiliations & Notes
  • Matthew Aaron Reilly
    Biomedical Engineering, The Ohio State University, Columbus, Ohio, United States
    Ophthalmology and Visual Science, The Ohio State University, Columbus, Ohio, United States
  • Andre Cleaver
    Mechanical Engineering, Tufts University, Medford, Massachusetts, United States
  • Archit Rede
    Biomedical Engineering, The Ohio State University, Columbus, Ohio, United States
  • Luis Rodriguez
    Biomedical Engineering, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
  • Gabriela Rice
    Biology, University of Texas at San Antonio, San Antonio, Texas, United States
  • Footnotes
    Commercial Relationships   Matthew Reilly, None; Andre Cleaver, None; Archit Rede, None; Luis Rodriguez, None; Gabriela Rice, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 3632. doi:
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      Matthew Aaron Reilly, Andre Cleaver, Archit Rede, Luis Rodriguez, Gabriela Rice; Best Practices for Estimating Lens Mechanical Properties Using a Compression Test. Invest. Ophthalmol. Vis. Sci. 2017;58(8):3632.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : The lens compression test is rapidly gaining in popularity for determining the relative stiffness of mouse lenses. The objective of this study was to determine whether the lens compression test could be used to estimate the intrinsic mechanical properties of the lens.

Methods : A custom lens compression apparatus was developed to allow simultaneous measurements of compressive displacement, force, and lens shape. A mechanical model of lens compression was developed and solved using an inverse finite element method. This model simulated contact between the compression plates and the lens, as well as adhesive and friction effects. This allowed the estimation of the Young’s modulus distribution within the lens. Mechanically homogeneous hydrogels were evaluated to validate the experimental and theoretical approaches. Encapsulated and decapsulated porcine lenses were also evaluated for comparison against published modulus values. Stiffness and resilience metrics were also computed according to published methods to evaluate their correlation with intrinsic mechanical properties.

Results : The inverse finite element method was successfully used to extract the modulus of homogeneous hydrogels from a compression test within 5% of the value obtained from a spinning test. Results for the porcine lenses were mixed: it was discovered that matching the force-displacement curve with the model yielded non-unique solutions (Fig. 1). Matching the experimentally-determined geometry overcame this difficulty.

Conclusions : The compression test coupled with the inverse model is useful for estimating the intrinsic mechanical properties of lenses. Stiffness and resilience as calculated in previous studies depend on lens size, shape, and adhesivity and may therefore not strictly correlate with the Young’s modulus.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Contour map showing the sum of squared errors between the experimental and model-predicted force-displacement curves at various combinations of Young's modulus for the cortex and nucleus of a 17-week-old mouse lens. Note that a minimum error is not achieved at any one unique pairing of modulus values; rather, a curve corresponding to infinitely many combinations of modulus values yielding minimized errors. White spaces in the figure correspond to modulus pairs for which the model was unable to achieve a solution.

Contour map showing the sum of squared errors between the experimental and model-predicted force-displacement curves at various combinations of Young's modulus for the cortex and nucleus of a 17-week-old mouse lens. Note that a minimum error is not achieved at any one unique pairing of modulus values; rather, a curve corresponding to infinitely many combinations of modulus values yielding minimized errors. White spaces in the figure correspond to modulus pairs for which the model was unable to achieve a solution.

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