The fast development of swept-source optical coherence tomography (SS-OCT) and swept-source optical coherence tomography angiography (SS-OCTA) enables both anterior and posterior imaging of the eye. These techniques have evolved from a research tool to an essential clinical imaging modality. The longer wavelength and faster speed of SS-OCT and SS-OCTA facilitate better visualization of structure and vasculature below pigmented tissue with a larger field of view of the posterior segment and 360-degree visualization of the anterior segment. In the past 10 years, algorithms dealing with OCT and OCTA data also vastly improved the image quality and enabled the automated quantification of OCT- and OCTA-derived metrics. This technology has enriched our current understanding of healthy and diseased eyes. Even though the high cost of the systems currently limited the widespread use of SS-OCT and SS-OCTA at the first beginning, the gap between research and clinic practice got obviously shortened in the past few years. SS-OCT and SS-OCTA will continue to evolve rapidly, contributing to a paradigm shift toward more widespread adoption of new imaging technology in clinical practice.

Glaucoma is a blinding disease for which intraocular pressure (IOP) is the only treatable risk factor. The mean IOP is regulated through the aqueous outflow system, which contains the trabecular meshwork (TM). Considerable evidence indicates that trabecular tissue movement regulates the aqueous outflow and becomes abnormal during glaucoma; however, such motion has thus far escaped detection. The purpose of this study is to describe anovel use of a phase-sensitive optical coherence tomography (PhS-OCT) method to assess pulse-dependent TM movement. For this study, we used enucleated monkey eyes, each mounted in an anterior segment holder. A perfusion system was used to control the mean IOP as well as to provide IOP sinusoidal transients (amplitude 3 mmHg, frequency 1 pulse/second) in all experiments. Measurements were carried out at seven graded mean IOPs (5, 8, 10, 20, 30, 40, and 50 mm Hg). We demonstrate that PhS-OCT is sensitive enough to image/visualize TM movement synchronous with the pulse-induced IOP transients, providing quantitative measurements of dynamic parameters such as velocity, displacement, and strain rate that are important for assessing the biomechanical compliance of the TM. We find that the largest TM displacement is in the area closest to Schlemm’s canal (SC) endothelium. While maintaining constant ocular pulse amplitude, an increase of mean IOP results in a decrease of TM displacement and mean size of the SC. These results demonstrate that the PhS-OCT is a useful imaging technique capable of assessing functional properties necessary to maintain IOP in a healthy range, offering a new diagnostic alternative for glaucoma.

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to temporally interpret traffic scenes. Autonomous vehicles clearly benefit from the ultra-wide Field of View (FoV) offered by $360^\circ$ panoramic sensors. However, due to the unique imaging process of panoramic cameras, models designed for pinhole images do not directly generalize satisfactorily to $360^\circ$ panoramic images. In this paper, we put forward a novel network framework——PanoFlow, to learn optical flow for panoramic images. To overcome the distortions introduced by equirectangular projection in panoramic transformation, we design a Flow Distortion Augmentation (FDA) method, which contains radial flow distortion (FDA-R) or equirectangular flow distortion (FDA-E) We further look into the definition and properties of cyclic optical flow for panoramic videos, and hereby propose a Cyclic Flow Estimation (CFE) method by leveraging the cyclicity of spherical images to infer $360^\circ$ optical flow and converting large displacement to relatively small displacement. PanoFlow is applicable to any existing flow estimation method and benefits from the progress of narrow-FoV flow estimation. In addition, we create and release a synthetic panoramic dataset FlowScape based on CARLA to facilitate training and quantitative analysis. PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the fresh established FlowScape benchmarks. Our proposed approach reduces the End-Point-Error (EPE) on FlowScape by $27.3\%$ . On OmniFlowNet, PanoFlow achieves an EPE of $3.17$ pixels, a $55.5\%$ error reduction from the best published result ( $7.12$ pixels). We also qualitatively validate our method via an outdoor collection vehicle and a public real-world OmniPhotos dataset, indicating strong potential and robustness for real-world navigation applications. Code and dataset are publicly available at PanoFlow.

We demonstrate a 1050-nm spectral domain optical coherence tomography (OCT) system with a 12 mm imaging depth in air, a 120 kHz A-scan rate and a 10 μm axial resolution for anterior-segment imaging of human eye, in which a new prototype InGaAs linescan camera with 2048 active-pixel photodiodes is employed to record OCT spectral interferograms in parallel. Combined with the full-range complex technique, we show that the system delivers comparable imaging performance to that of a swept-source OCT with similar system specifications.

In OCT angiography (OCTA), decorrelation computation has been widely used as a local motion index to identify dynamic flow from static tissues, but its dependence on SNR severely degrades the vascular visibility, particularly in low-SNR regions. To mathematically characterize the decorrelation-SNR dependence of OCT signals, we developed a multi-variate time series (MVTS) model. Based on the model, we derived a universal asymptotic linear relation of decorrelation to inverse SNR (iSNR), with the variance in static and noise regions determined by the average kernel size. Accordingly, with the population distribution of static and noise voxels being explicitly calculated in the iSNR and decorrelation (ID) space, a linear classifier is developed by removing static and noise voxels at all SNR, to generate a SNR-adaptive OCTA, termed as ID-OCTA. Then, flow phantom and human skin experiments were performed to validate the proposed ID-OCTA. Both qualitative and quantitative assessments demonstrated that the ID-OCTA offers a superior visibility of blood vessels, particularly in the deep layer. Finally, the implications of this work on both system design and hemodynamic quantification are further discussed.