We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65%, 3.58%, 3.79%, and 6.15% for SD, lateral and axial resolutions, SNR, and ENS, respectively. The experimental validation using scattering phantoms (Intralipid emulsion) shows reasonable estimations. Namely, the estimated SDs were proportional to the Intralipid concentrations, and the average estimation errors of lateral and axial resolutions were 1.36% and 0.68%, respectively. The scatterer density estimator was also applied to an in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found.

We present a numerical phase stabilization method for phase-sensitive signal processing of optical coherence tomography (OCT). This method removes the bulk phase error caused by the axial bulk motion of the sample and the environmental perturbation during volumetric acquisition. In this method, the partial derivatives of the phase error are computed along both fast and slow scanning directions, so that the vectorial gradient field of the phase error is given. Then, the phase error is estimated from the vectorial gradient field by a newly developed line integration method; a smart integration path method. The performance of this method was evaluated by analyzing the spatial frequency spectra of en face OCT images, and it objectively shows the significant phase-error-correction ability of the method. The performance was also evaluated by observing computationally refocused en face images of ex vivo tissue samples, and it was found that the image quality was improved by the phase-error correction.

We demonstrate computational multi-directional optical coherence tomography (OCT) to assess the directional property of tissue microstructure. This method is the combination of phase-sensitive volumetric OCT imaging and post-signal processing. The latter comprises of two steps. The first step is an intensity-directional analysis, which determines the dominant en face fiber orientations. The second step is the phase-directional imaging, which reveals the sub-resolution depth-orientation of the microstructure. The feasibility of the method was tested by assessing muscle and tendon samples. Stripe patterns with several sizes were visualized in the phase-directional images. In order to interpret these images, the muscle and tendon structures were numerically modeled, and the phase-directional images were generated from the numerical model. The similarity of the experimental and numerical results suggested that the stripe patterns correspond to the muscle fiber bundle and its crimping.

Here we demonstrate a long-depth-of-focus imaging method using polarization sensitive optical coherence tomography (PS-OCT). This method involves a combination of Fresnel-diffraction-model-based phase sensitive computational refocusing and Jones-matrix based PS-OCT (JM-OCT). JM-OCT measures four complex OCT images corresponding to four polarization channels. These OCT images are computationally refocused as preserving the mutual phase consistency. This method is validated using a static phantom, postmortem zebrafish, and ex vivo porcine muscle samples. All the samples demonstrated successful computationally-refocused birefringence and degree-of-polarization-uniformity (DOPU) images. We found that defocusing induces polarization artifacts, i.e., incorrectly high birefringence values and low DOPU values, which are substantially mitigated by computational refocusing.

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