Thickness estimation, which has a broad range of applications, plays an important role in the field of optical metrology. In this study, we investigate a new approach—combining optical coherence tomography (OCT) and statistical decision theory—for thickness estimation. We first discussed and quantified the intensity noise of three commonly used broadband sources, a super-continuum source, a super-luminescent diode (SLD), and a swept source. Furthermore, a maximum-likelihood (ML) estimator was implemented to interpret the OCT raw data. Based on the mathematical model and the ML estimator, simulations were set up to investigate the impact of different broadband sources in OCT for a thickness estimation task. We then validated the theoretical framework with physical phantoms. Results demonstrate unbiased nanometer-class thickness estimates with the ML estimator. The framework can be potentially used for film and surface shape metrology.
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.
We have developed a cellular resolution imaging modality, Gabor-Domain Optical Coherence Microscopy, which combines the high lateral resolution of confocal microscopy with the high sectioning capability of optical coherence tomography to image deep layers in tissues with high-contrast and volumetric resolution of 2 μm. A novelty of the custom microscope is the biomimetics that incorporates a liquid lens, as in whales’s eyes, for robust and rapid acquisition of volumetric imaging of deep layers in tissue down to 2 mm, thus overcoming the tradeoff between lateral resolution and depth of focus. The system incorporates a handheld scanning optical imaging head and fits on a movable cart that offers the flexibility in different biomedical applications and clinical settings, including ophthalmology. In the later, the microscope has successfully revealed micro-structures within the cornea and in particular the endothelial cells microenvironment, an important step in understanding the mechanisms of Fuchs’ dystrophy, a leading cause of the loss of corneal transparency. Also, the system was able to provide high definition of the edge of soft contact lenses, which is important for the fitting of the lens and the comfort of the patient. Overall, the imaging modality provides the opportunity to observe the three-dimensional features of different structures with micrometer resolution, which opens a wide range of future applications.
We study experimentally the scanning functions of galvanometer-based scanners (GSs) in order to optimize them for biomedical imaging in general, and for Optical Coherence Tomography (OCT) in particular. The main scanning parameters of the scanning process are taken into account: theoretical duty cycle (of the input signal of the GS), scan frequency (fs), and scan amplitude (θm). Triangular to sawtooth scanning regimes are thus considered. We demonstrate that when increasing the scan frequency and amplitude, the scanning function (i.e., the angular position of the galvomirror) is not able to follow anymore the input signal. Furthermore, as the theoretical duty cycle is increased, the result is unexpected: the effective duty cycle actually decreases – for high fs and θm. A saturation of the device therefore occurs. The practical limits of this phenomenon are discussed. GS users are thus provided with a multi-parameter analysis that allows them for optimizing their scanning regimes and to avoid pushing the devices to their limit – when that actually results in decreasing the quality of the images obtained, by example in OCT. Gabor Domain Optical Coherence Microscopy (GD-OCM) images are made to show effects of this phenomenon.
In biophotonics imaging, one important and quantitative task is layer-thickness estimation. In this study, we investigate
the approach of combining optical coherence tomography and a maximum-likelihood (ML) estimator for layer thickness
estimation in the context of tear film imaging. The motivation of this study is to extend our understanding of tear film
dynamics, which is the prerequisite to advance the management of Dry Eye Disease, through the simultaneous
estimation of the thickness of the tear film lipid and aqueous layers. The estimator takes into account the different
statistical processes associated with the imaging chain. We theoretically investigated the impact of key system
parameters, such as the axial point spread functions (PSF) and various sources of noise on measurement uncertainty.
Simulations show that an OCT system with a 1 μm axial PSF (FWHM) allows unbiased estimates down to nanometers
with nanometer precision. In implementation, we built a customized Fourier domain OCT system that operates in the
600 to 1000 nm spectral window and achieves 0.93 micron axial PSF in corneal epithelium. We then validated the
theoretical framework with physical phantoms made of custom optical coatings, with layer thicknesses from tens of
nanometers to microns. Results demonstrate unbiased nanometer-class thickness estimates in three different physical
phantoms.
The prevalence of Dry Eye Disease (DED) in the USA is approximately 40 million in aging adults with about $3.8
billion economic burden. However, a comprehensive understanding of tear film dynamics, which is the prerequisite to
advance the management of DED, is yet to be realized. To extend our understanding of tear film dynamics, we
investigate the simultaneous estimation of the lipid and aqueous layers thicknesses with the combination of optical
coherence tomography (OCT) and statistical decision theory.
In specific, we develop a mathematical model for Fourier-domain OCT where we take into account the different
statistical processes associated with the imaging chain. We formulate the first-order and second-order statistical
quantities of the output of the OCT system, which can generate some simulated OCT spectra. A tear film model, which
includes a lipid and aqueous layer on top of a rough corneal surface, is the object being imaged. Then we further
implement a Maximum-likelihood (ML) estimator to interpret the simulated OCT data to estimate the thicknesses of
both layers of the tear film. Results show that an axial resolution of 1 μm allows estimates down to nanometers scale.
We use the root mean square error of the estimates as a metric to evaluate the system parameters, such as the tradeoff
between the imaging speed and the precision of estimation. This framework further provides the theoretical basics to
optimize the imaging setup for a specific thickness estimation task.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.