title: Maximum-likelihood estimation in Optical Coherence Tomography in the context of the tear film dynamics.
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.
authors.affiliation: Department of Physics and Astronomy, University of Rochester, Rochester, New York 14627, USA.
authors.forename: Jinxin
authors.initials: J
authors.lastname: Huang
authors.forename: Eric
authors.initials: E
authors.lastname: Clarkson
authors.forename: Matthew
authors.initials: M
authors.lastname: Kupinski
authors.forename: Kye-Sung
authors.initials: KS
authors.lastname: Lee
authors.forename: Kara L
authors.initials: KL
authors.lastname: Maki
authors.forename: David S
authors.initials: DS
authors.lastname: Ross
authors.forename: James V
authors.initials: JV
authors.lastname: Aquavella
authors.forename: Jannick P
authors.initials: JP
authors.lastname: Rolland
keywords: (030.0030) Coherence and statistical optics; (110.3000) Image quality assessment