June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
TEAR FILM THICKNESS ESTIMATION USING OPTICAL COHERENCE TOMOGRAPHY AND MAXIMUM-LIKELIHOOD ESTIMATION
Author Affiliations & Notes
  • Jinxin Huang
    Department of Physics and Astronomy, University of Rochester, Rochester, NY
  • Patrice Tankam
    The Institute of Optics, University of Rochester, Rochester, NY
    Center for Visual Science, University of Rochester, Rochester, NY
  • James V Aquavella
    Flaum Eye Institute, University of Rochester, Rochester, NY
  • Holly Butler Hindman
    Flaum Eye Institute, University of Rochester, Rochester, NY
  • Eric Clarkson
    Department of Radiology, University of Arizona, Tucson, AZ
  • Matthew Kupinski
    College of Optical Sciences, University of Arizona, Tucson, AZ
  • Jannick Rolland-Thompson
    The Institute of Optics, University of Rochester, Rochester, NY
    Center for Visual Science, University of Rochester, Rochester, NY
  • Footnotes
    Commercial Relationships Jinxin Huang, University of Rochester (P); Patrice Tankam, None; James Aquavella, None; Holly Hindman, None; Eric Clarkson, University of Rochester (P); Matthew Kupinski, University of Rochester (P); Jannick Rolland-Thompson, University of Rochester (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 351. doi:
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      Jinxin Huang, Patrice Tankam, James V Aquavella, Holly Butler Hindman, Eric Clarkson, Matthew Kupinski, Jannick Rolland-Thompson; TEAR FILM THICKNESS ESTIMATION USING OPTICAL COHERENCE TOMOGRAPHY AND MAXIMUM-LIKELIHOOD ESTIMATION. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):351.

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

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Abstract
 
Purpose
 

The purpose of this study is to extend the understanding of tear film dynamics for the management of Dry Eye disease. For this purpose, an advanced nanometer-class imager was developed, which can optically sense the tear film and simultaneously estimate the thickness of the lipid and aqueous/mucin layers.

 
Methods
 

A tear film imager was developed, which combines state-of-the-art optical coherence tomography (OCT) and a robust estimator based on statistical decision theory. The customized OCT hardware, which operates in the spectral window from 600 to 1000 nm, achieves one micron axial point spread function (FWHM) in corneal tissue. Combined with the maximum-likelihood (ML) estimator, the system is able to estimate thickness of the nanometer-scale lipid and micron-scale aqueous layers of the tear film, simultaneously, with nanometer precision. The system was first validated with a physical phantom that consists of two layers of optical coatings that mimic the lipid and aqueous layers of the tear film. The system was then applied in humans, in vivo, to perform in vivo tear film thickness measurements.

 
Results
 

The validation of the imager performed with the physical phantom, yielded thickness estimation precisely consistent with the ground truth. The thickness estimation was repeated multiple times and the precision was quantified to be on the order of the nanometer. A thickness map across a 3mm by 3mm area on the phantom was shown in Figure 1. First in vivo measurements in normal eye human subjects yield an aqueous layer of 5.199 micrometers and a lipid layer of 68 nm, which is consistent with expected normal tear films.

 
Conclusions
 

The customized tear film imager was shown to be unbiased and efficient for the simultaneous estimation of the tear film lipid and aqueous thickness. It was validated in physical phantoms and applied in vivo in humans. Future work will integrate a new scanning lens that will allow the perpendicular incidence of the scanning beam along the corneal surface, enabling measurements over an area up to 9 mm in diameter. The associated optical aberrations induced by the tear film dynamic-thickness maps between blinks will be also studied and their impact on visual performance will be investigated.  

 
Figure 1 (a) Structure of a phantom; Thickness maps of (b) the lipid and (c) the aqueous layers; Repeatability distribution of (d) the lipid layer and (e) the aqueous layer
 
Figure 1 (a) Structure of a phantom; Thickness maps of (b) the lipid and (c) the aqueous layers; Repeatability distribution of (d) the lipid layer and (e) the aqueous layer

 
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