Abstract:We demonstrate the utility of a novel scanning method for optical coherence tomography angiography (OCTA). Although raster scanning is commonly used for OCTA imaging, a bidirectional approach would lessen the distortion caused by galvanometer-based scanners as sources continue to increase sweep rates. As shown, a unidirectional raster scan approach has a lower effective scanning time than bidirectional approaches; however, a strictly bidirectional approach causes contrast variation along the B-scan direction due to the non-uniform time interval between B-scans. Therefore, a stepped bidirectional approach is introduced and successfully applied to retinal imaging in normal controls and in a pathological subject with diabetic retinopathy. Young, M. V. Sarunic, and M. F. Beg, "Comparative analysis of repeatability of manual and automated choroidal thickness measurements in nonneovascular age-related macular degeneration," Invest. Ophthalmol.

High resolution visualization of optical coherence tomography (OCT) and OCT angiography (OCT-A) data is required to fully take advantage of the imaging modality’s three-dimensional nature. However, artifacts induced by patient motion often degrade OCT-A data quality. This is especially true for patients with deteriorated focal vision, such as those with diabetic retinopathy (DR). We propose a novel methodology for software-based OCT-A motion correction achieved through serial acquisition, volumetric registration, and averaging. Motion artifacts are removed via a multi-step 3D registration process, and visibility is significantly enhanced through volumetric averaging. We demonstrate that this method permits clear 3D visualization of retinal pathologies and their surrounding features, 3D visualization of inner retinal capillary connections, as well as reliable visualization of the choriocapillaris layer.

To determine the fidelity of optical coherence tomography angiography (OCTA) techniques by direct comparison of the retinal capillary network images obtained from the same region as imaged by OCTA and high-resolution confocal microscope.Method: Ten porcine eyes were perfused with red blood cells for OCTA image acquisition from the area centralis and then perfusion-fixed, and the vessels were labeled for confocal imaging. Two approaches involving post-processing of two-dimensional projection images and vessel tracking on three dimensional image stacks were used to obtain quantitative measurements. Data collected include vessel density, length of visible vessel track, count of visible branch points, vessel track depth, vessel diameter, angle of vessel descent, and angle of dive for comparison and analysis.Results: Comparing vascular images acquired from OCTA and confocal microscopy, we found (1) a good representation of the larger caliber retinal vessels, (2) an underrepresentation of retinal microvessels smaller than 10 μm and branch points in all four retinal vascular plexuses, particularly the intermediate capillary plexus, (3) reduced visibility associated with an increase in the angle of descent, (4) a tendency to loss visibility of vessel track at a branch point or during a sharp dive, and (5) a reduction in visibility with increase in retinal depth on OCTA images.Conclusions: Current OCTA techniques can visualize the retinal capillary network, but some types of capillaries cannot be detected by OCTA, particularly in the middle to deeper layers.Translational Relevance: The information indicates the limitation in clinical use and scopes for improvement in the current OCTA technologies.

Automated measurements of the human cone mosaic requires the identification of individual cone photoreceptors. The current gold standard, manual labeling, is a tedious process and can not be done in a clinically useful timeframe. As such, we present an automated algorithm for identifying cone photoreceptors in adaptive optics optical coherence tomography (AO-OCT) images. Our approach fine-tunes a pre-trained convolutional neural network originally trained on AO scanning laser ophthalmoscope (AO-SLO) images, to work on previously unseen data from a different imaging modality. On average, the automated method correctly identified 94% of manually labeled cones when compared to manual raters, from twenty different AO-OCT images acquired from five normal subjects. Voronoi analysis confirmed the general hexagonal-packing structure of the cone mosaic as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the high reliability and practical utility of having an automated solution to this problem.