Performance analysis of optical coherence tomography in the context of a thickness estimation task

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 10× improvement in precision for both the supercontinuum and SLD sources. The ML estimator also shows the ability to estimate thickness that is at least 10× 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.