Probabilistic volumetric speckle suppression in OCT using deep learning.

We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode- to generate training data.

Probabilistic volumetric speckle suppression in OCT using deep learning.

We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode to generate training data.

Computational refocusing in phase-unstable polarization-sensitive optical coherence tomography.

We present computational refocusing in polarization-sensitive optical coherence tomography (PS-OCT) to improve spatial resolution in the calculated polarimetric parameters and extend the depth-of-field in phase-unstable, fiber-based PS-OCT systems. To achieve this, we successfully adapted short A-line range phase-stability adaptive optics (SHARP), a computational aberration correction technique compatible with phase-unstable systems, into a Stokes-based PS-OCT system with inter-A-line polarization modulation. Together with the spectral binning technique to mitigate system-induced chromatic polarization effects, we show that computational refocusing improves image quality in tissue polarimetry of swine eye anterior segment ex vivo with PS-OCT.

Computational adaptive optics in phase-unstable optical coherence tomography.

We present a scheme for correction of --separable aberrations in optical coherence tomography (OCT) designed to work with phase unstable systems with no hardware modifications. Our approach, termed SHARP, is based on computational adaptive optics and numerical phase correction and follows from the fact that local phase stability is sufficient for the deconvolution of optical aberrations. We demonstrate its applicability in a raster-scan polygon-laser OCT system with strong phase-jitter noise, achieving successful refocusing at depths up to 4 times the Rayleigh range.

Noise and bias in optical coherence tomography intensity signal decorrelation.

Functional optical coherence tomography (OCT) imaging based on the decorrelation of the intensity signal has been used extensively in angiography and is finding use in flowmetry and therapy monitoring. In this work, we present a rigorous analysis of the autocorrelation function, introduce the concepts of contrast bias, statistical bias and variability, and identify the optimal definition of the second-order autocorrelation function (ACF) to improve its estimation from limited data. We benchmark different averaging strategies in reducing statistical bias and variability.