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Maximum-likelihood estimation in Optical Coherence Tomography in the context of the tear film dynamics

Year: 2013
Type: article
Abstract: Understanding tear film dynamics is a prerequisite for advancing the management of Dry Eye Disease (DED). In this paper, we discuss the use of optical coherence tomography (OCT) and statistical decision theory to analyze the tear film dynamics of a digital phantom. We implement a maximum-likelihood (ML) estimator to interpret OCT data based on mathematical models of Fourier-Domain OCT and the tear film. With the methodology of task-based assessment, we quantify the tradeoffs among key imaging system parameters. We find, on the assumption that the broadband light source is characterized by circular Gaussian statistics, ML estimates of 40 nm +/- 4 nm for an axial resolution of 1 μm and an integration time of 5 μs. Finally, the estimator is validated with a digital phantom of tear film dynamics, which reveals estimates of nanometer precision.
DOI: 10.1364/boe.4.001806
Authors: Jinxin Huang, Eric Clarkson, Matthew Kupinski, Kye-sung Lee, Kara L. Maki, David S. Ross, James V. Aquavella, Jannick P. Rolland
Institutions: University of Rochester, University of Arizona, Institute for Basic Science, Korea Basic Science Institute, Rochester Institute of Technology, University of Rochester Medicine
Language: English
Open Access status: gold
Estimated APC paid: $1,913
OpenAlex ID: W2009468994
FWCI: 2.564
Cites: 14
Cited by: 16
Related to: 10