Dispersion compensation is an important topic in optical coherence tomography (OCT) since the system- and sample-induced dispersion can often blur the image and degrade the axial resolution. Common numerical compensation methods rely on manual selection of the parameters and there is no universally accepted standard to determine the dispersion-free state. In this work, we propose a method that can automatically compensate the dispersion using fractional Fourier transform (FrFT) and provide a new insight on defining the sharpness metric. We exploit the sparsity of the image in the FrFT domain and thus find the optimal order of FrFT by minimizing the corresponding L1-norm. The effectiveness and robustness of the proposed method is confirmed in both numerical simulation and human skin and retina experiments.
In the measurement of tissues using Fourier-domain optical coherence tomography (FD-OCT), speckle patterns from dynamic and static components often exhibit distinct characteristics: the former can be reduced through incoherent averaging, while the latter cannot. However, in the conventional Monte Carlo (MC) based simulations of FD-OCT, the speckle patterns of dynamic medium and static regions cannot be distinguished due to the random spatial distribution of scattering events across the entire simulated phantom. To tackle this issue, we propose a hybrid phantom model for MC-based realistic simulation of speckles in FD-OCT. In the simulation using the proposed model, static tissue within the 3D structure is modeled as a swarm of fixed particles loosely packed in the background medium. Once a photon is emitted into the static tissue model, it keeps moving until encountering a fixed particle and undergoing scattering. On the other hand, the spatial distribution of scattering points in the dynamic medium is still assumed random, which makes the photon’s step size sampled based on the wavelength-dependent scattering coefficient. Compared to conventional MC simulations, speckles simulated with the proposed model at different time points exhibit a higher spatial correlation in the static structures, which allows them to remain after incoherent averaging. In contrast, speckles in the dynamic component manifest de-correlation across multiple simulations. Future works involve leveraging this method to simulate dynamic OCT and linking structural information with speckle patterns to solve inverse problems.
In previous Monte Carlo (MC) studies of modeling Fourier-domain optical coherence tomography (FD-OCT), the results obtained at single wavelength are often used to reconstruct the image despite of FD-OCT’s broadband nature. Here, we propose a novel image simulator for full-wavelength MC simulation of FD-OCT based on Mie theory, which combines the inverse discrete Fourier transform (IDFT) with a probability distribution-based signal pre-processing to eliminate the excessive noises in image reconstruction via IDFT caused by missing certain wavelength’s signals in some scattering events. Compared with the conventional method, the proposed simulator is more accurate and could better preserve the wavelength-dependent features.
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.