Digital refocusing of optical coherence tomography (OCT) restores the spatial resolution degraded by defocusing. To apply this to imaging of biological tissue with point-scan Fourier domain OCT (FD-OCT), fast volume acquisition is needed to prevent motion disturbance. This study demonstrates the Lissajous-cycle-wise (LCW) digital refocusing algorithm applied to in vivo imaging at acquisition rates typical of FD-OCT. Blurring along and perpendicular to the scanning direction are compensated by inverse filtering and compositing A-lines of different scanning directions after motion correction, respectively. Ex vivo and in vivo biological sample experiments are applied for the proof of concept. The blurred images due to defocusing are sharpened by the LCW digital refocus algorithm.
A new deep-learning-based scatterer density estimator (SDE) is demonstrated. The SDE is trained by pairs of numerically simulated OCT images and its background parameters including the scatterer density, resolutions, and signal-to-noise ratio. For this simulation, we introduced a new noise model that accurately accounts for the spatial properties of three noise types: shot, relative-intensity, and detector noise. This SDE was experimentally validated by phantom and in-vitro tumor spheroid measurements. Significantly improved accuracy was found in comparison to our old SDE being trained with a naïve noise model that does not account for the spatial noise property.
We propose a new multi-focus average method for optical coherence tomography, to reduce the multiple scattering signals and improve the visibility of the sample structure in the deep region. It combines computational refocusing, complex averaging, and multiple acquisitions with focal shifting. A scattering phantom was measured to validate the suppression of multiple scattering signals and the contrast improvement at the deep region.
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