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Jinxin Huang, Eric Clarkson, Matthew Kuppinski, Qun Yuan, Ke Xu, Patrice Tankam, Kara Maki, David Ross, James Aquavella, Jannick Rolland; INVESTIGATION OF TEAR FILM DYNAMICS—TOWARDS UNDERSTANDING ITS CLINICAL RELEVANCE. Invest. Ophthalmol. Vis. Sci. 2014;55(13):1980.
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© ARVO (1962-2015); The Authors (2016-present)
The purpose of this study is to understand the clinical relevance of tear film dynamics. For this purpose, we developed an advanced nanometer-class imager that can simultaneously estimate the thickness dynamics of the lipid and aqueous/mucin layers, as well as the tear volume.
The imager was conceived from combined advances in optical coherence tomography (OCT) hardware and a statistical decision theory based algorithm. The unique OCT hardware consists of a custom optical filter operating from 600 to 1000 nm and an astigmatism-corrected spectrometer. We also conceived a novel spiral scanner based on liquid lens technology. The image plane of the scanner matches the curvature of the cornea in a spiral pattern and the incidence beam is normal to the corneal surface over up to a 9 mm diameter area. In terms of the algorithm, we developed a benchmark of maximum-likelihood (ML) estimation. It includes the mathematical modeling of the imaging process, which takes into account the statistical noise associated with the imaging chain, and a ML estimator that can statistically interpret OCT data to extract the thicknesses of the lipid and aqueous layers and the tear volume. A digital phantom was used to provide ground truth for the development of the imager. Furthermore, a physical phantom was conceived that mimics the tear film to validate the system.
We quantified the tradeoff in temporal resolution of the tear film dynamics against the precision of thickness estimation. We further quantified the impact of different source noise, including that of a supercontinuum source, on the performance of the thickness estimation task. Results show that for a 10 volumes/second imaging speed, an OCT system with one micron axial resolution (Fig. 1), combined with the ML estimator, can estimate thickness down to 20 nanometers with nanometer precision (Fig. 2).
The ML estimator is shown to be an unbiased and efficient estimator for the simultaneous estimation of the tear film lipid and aqueous thickness. Future work will integrate the developed framework to support in vivo human studies. We will then also quantify the associated optical aberrations induced by the tear film dynamic-thickness maps between blinks and investigate their impact on visual performance.
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