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Complex differential variance (CDV) provides phase-sensitive angiographic imaging for optical coherence tomography (OCT) with immunity to phase-instabilities of the imaging system and small-scale axial bulk motion. However, like all angiographic methods, measurement noise can result in erroneous indications of blood flow that confuse the interpretation of angiographic images. In this paper, a modified CDV algorithm that corrects for this noise-bias is presented. This is achieved by normalizing the CDV signal by analytically derived upper and lower limits. The noise-bias corrected CDV algorithm was implemented into an experimental 1 μm wavelength OCT system for retinal imaging that used an eye tracking scanner laser ophthalmoscope at 815 nm for compensation of lateral eye motions. The noise-bias correction improved the CDV imaging of the blood flow in tissue layers with a low signal-to-noise ratio and suppressed false indications of blood flow outside the tissue. In addition, the CDV signal normalization suppressed noise induced by galvanometer scanning errors and small-scale lateral motion. High quality cross-section and motion-corrected angiograms of the retina and choroid are presented.

Abstract. Optical coherence tomography (OCT) provides structural information of laryngeal tissue which is comparable to histopathological analysis of biopsies taken under general anesthesia. In awake patients, movements impede clinically useful OCT acquisition. Therefore, an automatic compensation of movements was implemented into a swept source OCT-laryngoscope. Video and OCT beam path were combined in one tube of 10-mm diameter. Segmented OCT images served as distance sensor and a feedback control adjusted the working distance between 33 and 70 mm by synchronously translating the reference mirror and focusing lens. With this motion compensation, the tissue was properly visible in up to 88% of the acquisition time. During quiet respiration, OCT contrasted epithelium and lamina propria. Mean epithelial thickness was measured to be 109 and 135 μm in female and male, respectively. Furthermore, OCT of mucosal wave movements during phonation enabled estimation of the oscillation frequency and amplitude. Regarding clinical issues, the OCT-laryngoscope with automated working distance adjustment may support the estimation of the depth extent of epithelial lesions and contribute to establish an indication for a biopsy. Moreover, OCT of the vibrating vocal folds provides functional information, possibly giving further insight into mucosal behavior during the vibratory cycle.

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Many diseases of the eye are associated with alterations in the retinal vasculature that are possibly preceded by undetected changes in blood flow. In this work, a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning. The analysis used a forward signal model to simulate OCT blood flow data for training of a neural network (NN). The NN was combined with pre-and post-processing steps to create an analysis framework for measuring flow rates from individual blood vessels. The framework's accuracy was validated using both blood flow phantoms and human subject imaging, and across flow speed, vessel angle, hematocrit levels, and signal-to-noise ratio. The reported flow rate of the calibrated NN framework was measured to be largely independent of vessel angle, hematocrit levels, and measurement signal-to-noise ratio. In vivo retinal flow rate measurements were self-consistent across vascular branch points, and approximately followed a predicted power-law dependence on the vessel diameter. The presented OCT-based NN flow rate estimation framework addresses the need for a robust, deployable, and label-free quantitative retinal blood flow mapping technique. Many retinal diseases are associated with abnormalities in perfusion with primary examples including age-related macular degeneration, diabetic retinopathy, and glaucoma 1-6. Much of our current understanding in this area is derived from fluorescence angiography (FA) and, more recently, optical coherence tomography (OCT) angiography. These tools provide the morphology of the retinal vasculature, e.g., vessel diameter and capillary drop-out, but do not quantify retinal blood flow directly. OCT imaging is commonplace in ophthalmology and an OCT-based flow imaging technique could therefore be rapidly adopted in research and clinical settings. This is especially true if the technique can be compatible with the design of commercial OCT platforms. Traditionally, flow measurements in OCT have been based on Doppler (phase-based) techniques, which measure the axial component of the blood flow 7-13. The primary barrier to the use of Doppler OCT in the retina arises from the need to calculate total flow from axial flow using a scale factor that is inversely related to the cosine of the angle between the flow vector and the imaging beam (i.e. Doppler angle α). With α near 90°, a small error in the measurement of α leads to large errors in the measured total flow 14. This has limited Doppler techniques to a small region near the optic nerve head where vessels are oriented to avoid α = 90°1 5. Across most of the retina, where vessels are oriented with α ~90°, only techniques such as multi-beam Doppler OCT have been successful in accurately measuring flow 16-19. However, the use of multiple, non-colinear beams in three-dimensional imaging significantly increases the hardware complexity. The required modifications to microscope and OCT hardware are barriers in this case to a broader adoption of the tech...