Geographic Atrophy Progression Is Associated With Choriocapillaris Flow Deficits Measured With Optical Coherence Tomographic Angiography.

Purpose: The purpose of this study was to assess the associations between baseline choriocapillaris (CC) flow deficits and geographic atrophy (GA) progression. Methods: In this prospective cohort study, patients with GA underwent 3 × 3-mm macular spectral-domain optical coherence tomographic angiography (OCTA) at baseline and follow-up visits. Annual GA enlargement rate was defined as change of square root of GA area in 12 months. Shadow areas due to iris, media opacity, retinal vessels, and drusen were excluded. CC vessel density (CC-VD) in non-GA areas was measured using a validated machine-learning-based algorithm. Low perfusion area (LPA) was defined as capillary density below the 0.1 percentile threshold of the same location of 40 normal healthy control eye. Focal perfusion loss (FPL) was defined as percentage of CC loss within LPA compared with normal controls. Results: Ten patients with GA were enrolled and followed for 26 months on average. At baseline, the mean GA area was 0.84 ± 0.70 mm2. The mean CC-VD was 44.5 ± 15.2%, the mean LPA was 4.29 ± 2.6 mm2, and the mean FPL was 50.4 ± 28.2%. The annual GA enlargement rate was significantly associated with baseline CC-VD (r = -0.816, P = 0.004), LPA (r = 0.809, P = 0.005), and FPL (r = 0.800, P = 0.005), but not with age (r = 0.008, P = 0.98) and GA area (r = -0.362, P = 0.30). Conclusions: Baseline CC flow deficits were significantly associated with a faster GA enlargement over the course of 1 year, suggesting the choriocapillaris perfusion outside of a GA area may play a role in GA progression.

Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network.

Segmentation of retinal layers in optical coherence tomography (OCT) is an essential step in OCT image analysis for screening, diagnosis, and assessment of retinal disease progression. Real-time segmentation together with high-speed OCT volume acquisition allows rendering of en face OCT of arbitrary retinal layers, which can be used to increase the yield rate of high-quality scans, provide real-time feedback during image-guided surgeries, and compensate aberrations in adaptive optics (AO) OCT without using wavefront sensors. We demonstrate here unprecedented real-time OCT segmentation of eight retinal layer boundaries achieved by 3 levels of optimization: 1) a modified, low complexity, neural network structure, 2) an innovative scheme of neural network compression with TensorRT, and 3) specialized GPU hardware to accelerate computation. Inferencing with the compressed network U-NetRT took 3.5 ms, improving by 21 times the speed of conventional U-Net inference without reducing the accuracy. The latency of the entire pipeline from data acquisition to inferencing was only 41 ms, enabled by parallelized batch processing. The system and method allow real-time updating of en face OCT and OCTA visualizations of arbitrary retinal layers and plexuses in continuous mode scanning. To the best our knowledge, our work is the first demonstration of an ophthalmic imager with embedded artificial intelligence (AI) providing real-time feedback.

Sensorless adaptive-optics optical coherence tomographic angiography.

Optical coherence tomographic angiography (OCTA) can image the retinal blood flow but visualization of the capillary caliber is limited by the low lateral resolution. Adaptive optics (AO) can be used to compensate ocular aberrations when using high numerical aperture (NA), and thus improve image resolution. However, previously reported AO-OCTA instruments were large and complex, and have a small sub-millimeter field of view (FOV) that hinders the extraction of biomarkers with clinical relevance. In this manuscript, we developed a sensorless AO-OCTA prototype with an intermediate numerical aperture to produce depth-resolved angiograms with high resolution and signal-to-noise ratio over a 2 × 2 mm FOV, with a focal spot diameter of 6 µm, which is about 3 times finer than typical commercial OCT systems. We believe these parameters may represent a better tradeoff between resolution and FOV compared to large-NA AO systems, since the spot size matches better that of capillaries. The prototype corrects defocus, astigmatism, and coma using a figure of merit based on the mean reflectance projection of a slab defined with real-time segmentation of retinal layers. AO correction with the ability to optimize focusing in arbitrary retinal depths - particularly the plexuses in the inner retina - could be achieved in 1.35 seconds. The AO-OCTA images showed greater flow signal, signal-to-noise ratio, and finer capillary caliber compared to commercial OCTA. Projection artifacts were also reduced in the intermediate and deep capillary plexuses. The instrument reported here improves OCTA image quality without excessive sacrifice in FOV and device complexity, and thus may have potential for clinical translation.

Depth-resolved optimization of a real-time sensorless adaptive optics optical coherence tomography.

Sensorless adaptive optics optical coherence tomography (AO-OCT) is a technology to image retinal tissue with high resolution by compensating ocular aberrations without wavefront sensors. In this Letter, a fast and robust hill-climbing algorithm is developed to optimize five Zernike modes in AO-OCT with a numerical aperture between that of conventional AO and commercial OCT systems. The merit function is generated in real time using graphics processing unit while axially tracking the retinal layer of interest. A new method is proposed to estimate the largest achievable field of view for which aberrations are corrected uniformly in sensorless AO-OCT.

DETECTION OF CLINICALLY UNSUSPECTED RETINAL NEOVASCULARIZATION WITH WIDE-FIELD OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PURPOSE: To evaluate wide-field optical coherence tomography angiography (OCTA) for detection of clinically unsuspected neovascularization (NV) in diabetic retinopathy (DR). METHODS: This prospective observational single-center study included adult patients with a clinical diagnosis of nonproliferative DR. Participants underwent a clinical examination, standard 7-field color photography, and OCTA with commercial and prototype swept-source devices. The wide-field OCTA was achieved by montaging five 6 × 10-mm scans from a prototype device into a 25 × 10-mm image and three 6 × 6-mm scans from a commercial device into a 15 × 6-mm image. A masked grader determined the retinopathy severity from color photographs. Two trained readers examined conventional and wide-field OCTA images for the presence of NV. RESULTS: Of 27 participants, photographic grading found 13 mild, 7 moderate, and 7 severe nonproliferative DR. Conventional 6 × 6-mm OCTA detected NV in 2 eyes (7%) and none with 3 × 3-mm scans. Both prototype and commercial wide-field OCTA detected NV in two additional eyes. The mean area of NV was 0.38 mm (range 0.17-0.54 mm). All eyes with OCTA-detected NV were photographically graded as severe nonproliferative DR. CONCLUSION: Wide-field OCTA can detect small NV not seen on clinical examination or color photographs and may improve the clinical evaluation of DR.

Detecting and measuring areas of choriocapillaris low perfusion in intermediate, non-neovascular age-related macular degeneration.

Age-related macular degeneration (AMD) is a vision-threatening disease that affects the outer retina and choroid of elderly adults. Because photoreceptors are found in the outer retina and rely primarily on the trophic support of the underlying choriocapillaris, imaging of flow or lack thereof in choriocapillaris by optical coherence tomography angiography (OCTA) has great clinical potential in AMD assessment. We introduce a metric using OCTA, named "focal perfusion loss" (FPL) to describe the effects of age and non-neovascular AMD on choriocapillaris flow. Because OCTA imaging of choriocapillaris is vulnerable to artifacts-namely motion, projections, segmentation errors, and shadows-they are removed by postprocessing software. The shadow detection software is a machine learning algorithm recently developed for the evaluation of the retinal circulation and here adapted for choriocapillaris analysis. It aims to exclude areas with unreliable flow signal due to blocking of the OCT beam by objects anterior to the choriocapillaris (e.g., drusen, retinal vessels, vitreous floaters, and iris). We found that both the FPL and the capillary density were able to detect changes in the choriocapillaris of AMD and healthy age-matched subjects with respect to young controls. The dominant cause of shadowing in AMD is drusen, and the shadow exclusion algorithm helps determine which areas under drusen retain sufficient signal for perfusion evaluation and which areas must be excluded. Such analysis allowed us to determine unambiguously that choriocapillaris density under drusen is indeed reduced.

Monitoring retinal responses to acute intraocular pressure elevation in rats with visible light optical coherence tomography.

Elevated intraocular pressure (IOP) is an important risk factor for glaucoma. However, the role of IOP in glaucoma progression, as well as retinal physiology in general, remains incompletely understood. We demonstrate the use of visible light optical coherence tomography to measure retinal responses to acute IOP elevation in Brown Norway rats. We monitored retinal responses in reflectivity, angiography, blood flow, oxygen saturation ( sO 2 ), and oxygen metabolism over a range of IOP from 10 to 100 mmHg. As IOP was elevated, nerve fiber layer reflectivity was found to decrease. Vascular perfusion in the three retinal capillary plexuses remained steady until IOP exceeded 70 mmHg and arterial flow was noted to reverse periodically at high IOPs. However, a significant drop in total retinal blood flow was observed first at 40 mmHg. As IOP increased, the venous sO 2 demonstrated a gradual decrease despite steady arterial sO 2 , which is consistent with increased arterial-venous oxygen extraction across the retinal capillary beds. Calculated total retinal oxygen metabolism was steady, reflecting balanced responses of blood flow and oxygen extraction, until IOP exceeded 40 mmHg, and fell to 0 at 70 and 80 mmHg. Above this, measurements were unattainable. All measurements reverted to baseline when the IOP was returned to 10 mmHg, indicating good recovery following acute pressure challenge. These results demonstrate the ability of this system to monitor retinal oxygen metabolism noninvasively and how it can help us understand retinal responses to elevated IOP.

Development and validation of a deep learning algorithm for distinguishing the nonperfusion area from signal reduction artifacts on OCT angiography.

The capillary nonperfusion area (NPA) is a key quantifiable biomarker in the evaluation of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). However, signal reduction artifacts caused by vitreous floaters, pupil vignetting, or defocus present significant obstacles to accurate quantification. We have developed a convolutional neural network, MEDnet-V2, to distinguish NPA from signal reduction artifacts in 6×6 mm2 OCTA. The network achieves strong specificity and sensitivity for NPA detection across a wide range of DR severity and scan quality.

Three-dimensional structural and angiographic evaluation of foveal ischemia in diabetic retinopathy: method and validation.

Optical coherence tomography angiography (OCTA) allows us to noninvasively investigate foveal ischemia, a key feature of diabetic retinopathy (DR). However, the sizes of the foveal avascular zone (FAZ) have a significant variation in normal population, preventing the objective assessment of pathological enlargement of FAZ due to capillary dropout. Based on the relationship between FAZ and ganglion cell complex (GCC) thickness in normal eyes, we defined a theoretical baseline FAZ (tbFAZ) on structural OCT and measured 2D and 3D vessel density in its vicinity on the simultaneously acquired OCTA in normal and diabetic eyes. We found that the structure-based tbFAZ was a reliable reference to identify foveal ischemia and that the 3D vessel density demonstrated ischemia more effectively than the 2D method. The proposed 3D para-FAZ vessel density correlates well with DR severity and potentially is a useful diagnostic biomarker, especially in the early stages of DR.

Signal Strength Reduction Effects in OCT Angiography.

PURPOSE: To elucidate the relationship between vessel density (VD) measurements and signal strength in OCT angiography (OCTA). DESIGN: Cross-sectional study. PARTICIPANTS: Healthy volunteers. METHODS: OCT angiography images obtained from healthy volunteers were analyzed to demonstrate the relationship between signal strength index (SSI) and VD. Experiments were performed to determine the effects of signal strength reduction on VD measurements on the Optovue/AngioVue (Optovue, Inc, Fremont, CA) and Cirrus/AngioPlex OCTA (Carl Zeiss Meditec, Inc, Dublin, CA) systems. Signal strength reduction was generated by either neutral density filters (NDFs) or defocus. MAIN OUTCOME MEASURES: Regression analysis of signal strength effects on VD. RESULTS: Vessel density decreased linearly with signal strength with high statistical significance on both OCTA systems tested and for all analyzed sources of variation in signal strength. The slope of VD versus SSI was greatest when signal strength was adjusted by NDFs, followed by defocus, interscan difference, interindividual variation, and left-right eye difference. Multivariate analysis revealed that both SSI and age had a significant effect on the interindividual variation in VD. CONCLUSIONS: Vessel density measurements using OCTA were affected significantly by OCT signal strengths on 2 OCTA platforms. Investigators should exercise caution when interpreting VD data from OCTA scans. Quantification algorithms for OCTA should ideally remove the signal strength bias.

Automated detection of shadow artifacts in optical coherence tomography angiography.

Frequently, when imaging retinal vasculature with optical coherence tomography angiography (OCTA) in diseased eyes, there are unavoidable obstacles to the propagation of light such as vitreous floaters or the pupil boundary. These obstacles can block the optical coherence tomography (OCT) beam and impede the visualization of the underlying retinal microcirculation. Detecting these shadow artifacts is especially important in the quantification of metrics that assess retinal disease progression because they might masquerade as regional perfusion loss. In this work, we present an algorithm to identify shadowed areas in OCTA of healthy subjects as well as patients with diabetic retinopathy, uveitis and age-related macular degeneration. The aim is to exclude these areas from analysis so that the overall OCTA parameters are minimally affected by shadow artifacts.

Real-time cross-sectional and en face OCT angiography guiding high-quality scan acquisition.

Defocusing, vignetting, and bulk motion degrade the image quality of optical coherence tomography angiography (OCTA) more significantly than structural OCT. The assessment of focus, alignment conditions, and stability of imaging subjects in commercially available OCTA systems are currently based on OCT signal quality alone, without knowledge of OCTA signal quality. This results in low yield rates for further quantification. In this Letter, we developed a novel OCTA platform based on a graphics processing unit (GPU) for a real-time, high refresh rate, B-san-by-B-scan split-spectrum amplitude-decorrelation angiography. The GPU provides a real-time display of both cross-sectional and en face images to assist operators during scan acquisition and ensure OCTA scan quality.

Invariant features-based automated registration and montage for wide-field OCT angiography.

The field of view of optical coherence tomography angiography (OCTA) images of the retina can be increased by montaging consecutive scans acquired at different retinal regions. Given the dramatic variation in aberrations throughout the entire posterior pole region, it is challenging to achieve seamless merging with apparent capillary continuity across the boundaries between adjacent angiograms. For this purpose, we propose herein a method that performs automated registration of contiguous OCTA images based on invariant features and uses a novel montage algorithm. The invariant features were used to register the overlapping areas between adjacently located scans by estimating the affine transformation matrix needed to accurately stitch them. Then, the flow signal was compensated to homogenize the angiograms with different brightness and the joints were blended to generate a seamless, montaged wide-field angiogram. We evaluated the algorithm on normal and diabetic retinopathy eyes. The proposed method could montage the angiograms seamlessly and provided a wide-field of view of retinal vasculature.

Automated phase unwrapping in Doppler optical coherence tomography.

Phase wrapping is a crucial issue in Doppler optical coherence tomography (OCT) and restricts its automatic implementation for clinical applications that quantify total retinal blood flow. We propose an automated phase-unwrapping technique that takes advantage of the parabolic profile of blood flow velocity in vessels. Instead of inspecting the phase shift manually, the algorithm calculates the gradient magnitude of the phase shift on the cross-sectional image and automatically detects the presence of phase wrapping. The voxels affected by phase wrapping are corrected according to the determined flow direction adjacent to the vessel walls. We validated this technique in the rodent retina using a prototype visible-light OCT and in the human retina with a commercial infrared OCT system. We believe this signal processing method may well accelerate clinical applications of Doppler OCT in ophthalmology.

Enhanced Quantification of Retinal Perfusion by Improved Discrimination of Blood Flow From Bulk Motion Signal in OCTA.

PURPOSE: Quantification of optical coherence tomography angiography (OCTA) is confounded by the prevalence of bulk motion. We have previously developed a regression-based bulk motion subtraction (rb-BMS) algorithm that estimates bulk motion velocity and corrects for its effect on flow signal. Here, we aim to investigate its ability to improve the reliability of capillary density (CD) quantification. METHODS: Two spectral-domain systems (70-kHz Avanti/AngioVue and 68-kHz Cirrus/AngioPlex) acquired 6 × 6-mm OCTA scans. The rb-BMS algorithm was applied on each OCTA volume. Regression analysis of angiographic versus reflectance signal of avascular A-lines in B-frames was used to set an optimized reflectance-adjusted threshold for discriminating vascular versus nonvascular voxels. The CD was calculated from en face maximum projections of the superficial vascular complex in macular scans and the nerve fiber layer plexus in disc scans, excluding large vessels. The retinal signal strength (RSS) was calculated by averaging the logarithmic-scale OCT reflectance signal, and its correlation with CD was investigated. RESULTS: Eight healthy eyes were scanned with each instrument on 2 separate days. The rb-BMS algorithm improved within-visit repeatability and between-visit reproducibility of CD compared with a global-threshold measurement algorithm. Using the rb-BMS algorithm, the CD results were less affected by RSS and the population variation was reduced. Motion-induced line artifacts were also reduced. CONCLUSIONS: The rb-BMS algorithm improved the reliability of perfusion quantification in OCTA on both Food and Drug Administration-cleared spectral-domain OCTA systems. TRANSLATIONAL RELEVANCE: The rb-BMS method helped reduce the inter-scan variability by generating accurate vessel maps, improving the reliability of retinal perfusion quantification.

MEDnet, a neural network for automated detection of avascular area in OCT angiography.

Screening and assessing diabetic retinopathy (DR) are essential for reducing morbidity associated with diabetes. Macular ischemia is known to correlate with the severity of retinopathy. Recent studies have shown that optical coherence tomography angiography (OCTA), with intrinsic contrast from blood flow motion, is well suited for quantified analysis of the avascular area, which is potentially a useful biomarker in DR. In this study, we propose the first deep learning solution to segment the avascular area in OCTA of DR. The network design consists of a multi-scaled encoder-decoder neural network (MEDnet) to detect the non-perfusion area in 6 × 6 mm2 and in ultra-wide field retinal angiograms. Avascular areas were effectively detected in DR subjects of various disease stages as well as in the foveal avascular zone of healthy subjects.

Rodent retinal circulation organization and oxygen metabolism revealed by visible-light optical coherence tomography.

Visible light optical coherence tomography (vis-OCT) is an emerging label-free and high-resolution 3-dimensional imaging technique that can provide retinal oximetry, angiography, and flowmetry in one modality. In this paper, we studied the organization of the arterial and venous retinal circulation in rats using vis-OCT. Arterioles were found predominantly in the superficial vascular plexus whereas veins tended to drain capillaries from the deep capillary plexus. After that, we determined the oxygen metabolic rate supported by retinal microcirculation by combining retinal vessel oxygen saturation and blood flow measurements. The ability to visualize and monitor retinal circulation organization and oxygen metabolism by vis-OCT may provide new opportunities for understanding the pathology of ocular diseases.

Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases.

The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.

Automatic quantification of choroidal neovascularization lesion area on OCT angiography based on density cell-like P systems with active membranes.

Detecting and quantifying the size of choroidal neovascularization (CNV) is important for the diagnosis and assessment of neovascular age-related macular degeneration. Depth-resolved imaging of the retinal and choroidal vasculature by optical coherence tomography angiography (OCTA) has enabled the visualization of CNV. However, due to the prevalence of artifacts, it is difficult to segment and quantify the CNV lesion area automatically. We have previously described a saliency algorithm for CNV detection that could identify a CNV lesion area with 83% accuracy. However, this method works under the assumption that the CNV region is the most salient area for visual attention in the whole image and consequently, errors occur when this requirement is not met (e.g. when the lesion occupies a large portion of the image). Moreover, saliency image processing methods cannot extract the edges of the salient object very accurately. In this paper, we propose a novel and automatic CNV segmentation method based on an unsupervised and parallel machine learning technique named density cell-like P systems (DEC P systems). DEC P systems integrate the idea of a modified clustering algorithm into cell-like P systems. This method improved the accuracy of detection to 87.2% on 22 subjects and obtained clear boundaries of the CNV lesions.

Automated spectroscopic retinal oximetry with visible-light optical coherence tomography.

Accurate, quantitative assessment of retinal blood oxygen saturation (sO2 ) may provide a useful early indicator of pathophysiology in several ocular diseases. Here, with visible-light optical coherence tomography (OCT), we demonstrate an automated spectroscopic retinal oximetry algorithm to measure the sO2 within the retinal arteries (A-sO2 ) and veins (V-sO2 ) in rats by automatically detecting the vascular posterior boundary on cross-sectional structural OCT. The algorithm was validated in vitro with flow phantoms and in vivo in rats by comparing the sO2 results, respectively, to those obtained using a blood gas analyzer and pulse oximetry. We also investigated the response of oxygen extraction (A-V sO2 ), including inter-session reproducibility, at different inhaled oxygen concentrations.