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Journal of Biomedical Optics • Open Access

Performance analysis of optical coherence tomography in the context of a thickness estimation task
Author(s): Jinxin Huang; Jianing Yao; Nick Cirucci; Trevor Ivanov; Jannick P. Rolland

Paper Abstract

Thickness estimation is a common task in optical coherence tomography (OCT). This study discusses and quantifies the intensity noise of three commonly used broadband sources, such as a supercontinuum source, a superluminescent diode (SLD), and a swept source. The performance of the three optical sources was evaluated for a thickness estimation task using both the fast Fourier transform (FFT) and maximum-likelihood (ML) estimators. We find that the source intensity noise has less impact on a thickness estimation task compared to the width of the axial point-spread function (PSF) and the trigger jittering noise of a swept source. Findings further show that the FFT estimator yields biased estimates, which can be as large as 10% of the thickness under test in the worst case. The ML estimator is by construction asymptotically unbiased and displays a <inline-formula< <mml:math display="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML"< <mml:mrow< <mml:mn<10</mml:mn< <mml:mo form="postfix"<×</mml:mo< </mml:mrow< </mml:math< </inline-formula< improvement in precision for both the supercontinuum and SLD sources. The ML estimator also shows the ability to estimate thickness that is at least <inline-formula< <mml:math display="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML"< <mml:mrow< <mml:mn<10</mml:mn< <mml:mo form="postfix"<×</mml:mo< </mml:mrow< </mml:math< </inline-formula< thinner compared to the FFT estimator. Finally, findings show that a supercontinuum source combined with the ML estimator enables unbiased nanometer-class thickness estimation with nanometer-scale precision.

Paper Details

Date Published: 17 September 2015
PDF: 8 pages
J. Biomed. Opt. 20(12) 121306 doi: 10.1117/1.JBO.20.12.121306
Published in: Journal of Biomedical Optics Volume 20, Issue 12
Show Author Affiliations
Jinxin Huang, Univ. of Rochester (United States)
Jianing Yao, Univ. of Rochester (United States)
Nick Cirucci, Univ. of Rochester (United States)
Trevor Ivanov, Univ. of Rochester (United States)
Jannick P. Rolland, Univ. of Rochester (United States)


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