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

Jinxin Huang, Eric Clarkson, Matthew Kupinski, Kye Sung Lee, Kara L. Maki, David S. Ross, James V. Aquavella, Jannick P. Rolland

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1806-1816
Number of pages11
JournalBiomedical Optics Express
Issue number10
StatePublished - 2013

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Maximum-likelihood estimation in Optical Coherence Tomography in the context of the tear film dynamics'. Together they form a unique fingerprint.

Cite this