Huang Jinxin, Clarkson Eric, Kupinski Matthew, Lee Kye-Sung, Maki Kara L, Ross David S, Aquavella James V, Rolland Jannick P
Department of Physics and Astronomy, University of Rochester, Rochester, New York 14627, USA.
Biomed Opt Express. 2013 Aug 29;4(10):1806-16. doi: 10.1364/BOE.4.001806. eCollection 2013.
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.
了解泪膜动力学是推进干眼症（DED）管理的先决条件。在本文中，我们讨论了使用光学相干断层扫描（OCT）和统计决策理论来分析数字模型的泪膜动力学。我们基于傅里叶域OCT和泪膜的数学模型，实现了最大似然（ML）估计器来解释OCT数据。通过基于任务的评估方法，我们量化了关键成像系统参数之间的权衡。我们发现，假设宽带光源具有圆形高斯统计特性，对于1μm的轴向分辨率和5μs的积分时间，ML估计为40nm±4nm。最后，该估计器通过泪膜动力学数字模型进行了验证，其显示出纳米级精度的估计。
