Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography

Optical coherence tomography angiography in diabetic retinopathy

There remain many unanswered questions on how to assess and treat the pathology and complications that arise from diabetic retinopathy (DR). Optical coherence tomography angiography (OCTA) is a novel and non-invasive three-dimensional imaging method that can visualize capillaries in all retinal layers. Numerous studies have confirmed that OCTA can identify early evidence of microvascular changes and provide quantitative assessment of the extent of diseases such as DR and its complications. A number of informative OCTA metrics could be used to assess DR in clinical trials, including measurements of the foveal avascular zone (FAZ; area, acircularity, 3D para-FAZ vessel density), vessel density, extrafoveal avascular zones, and neovascularization. Assessing patients with DR using a full-retinal slab OCTA image can limit segmentation errors and confounding factors such as those related to center-involved diabetic macular edema. Given emerging data suggesting the importance of the peripheral retinal vasculature in assessing and predicting DR progression, wide-field OCTA imaging should also be used. Finally, the use of automated methods and algorithms for OCTA image analysis, such as those that can distinguish between areas of true and false signals, reconstruct images, and produce quantitative metrics, such as FAZ area, will greatly improve the efficiency and standardization of results between studies. Most importantly, clinical trial protocols should account for the relatively high frequency of poor-quality data related to sub-optimal imaging conditions in DR and should incorporate time for assessing OCTA image quality and re-imaging patients where necessary.

Optical Coherence Tomography Angiography in Retinal Vascular Disorders

Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, which has some advantages over FA, such as its dye-free, non-invasive nature, and depth resolution. The depth resolution of OCTA allows for characterization of the retinal microvasculature in distinct anatomic layers, and commercial OCTA platforms also provide automated quantitative vascular and perfusion metrics. Quantitative and qualitative OCTA analysis in various retinal vascular disorders has facilitated the detection of pre-clinical vascular changes, greater understanding of known clinical signs, and the development of imaging biomarkers to prognosticate and guide treatment. With further technological improvements, such as a greater field of view and better image quality processing algorithms, it is likely that OCTA will play an integral role in the study and management of retinal vascular disorders. Artificial intelligence methods-in particular, deep learning-show promise in refining the insights to be gained from the use of OCTA in retinal vascular disorders. This review aims to summarize the current literature on this imaging modality in relation to common retinal vascular disorders.

Signal attenuation-compensated projection-resolved OCT angiography

Projection artifacts are a significant limitation of optical coherence tomographic angiography (OCTA). Existing techniques to suppress these artifacts are sensitive to image quality, becoming less reliable on low-quality images. In this study, we propose a novel signal attenuation-compensated projection-resolved OCTA (sacPR-OCTA) algorithm. In addition to removing projection artifacts, our method compensates for shadows beneath large vessels. The proposed sacPR-OCTA algorithm improves vascular continuity, reduces the similarity of vascular patterns in different plexuses, and removes more residual artifacts compared to existing methods. In addition, the sacPR-OCTA algorithm better preserves flow signal in choroidal neovascular lesions and shadow-affected areas. Because sacPR-OCTA processes the data along normalized A-lines, it provides a general solution for removing projection artifacts agnostic to the platform.

Integrated deep learning framework for accelerated optical coherence tomography angiography

Label-free optical coherence tomography angiography (OCTA) has become a premium imaging tool in clinics to obtain structural and functional information of microvasculatures. One primary technical drawback for OCTA, however, is its imaging speed. The current protocols require high sampling density and multiple acquisitions of cross-sectional B-scans to form one image frame, resulting in low acquisition speed. Recently, deep learning (DL)-based methods have gained attention in accelerating the OCTA acquisition process. They achieve faster acquisition using two independent reconstructing approaches: high-quality angiograms from a few repeated B-scans and high-resolution angiograms from undersampled data. While these approaches have shown promising results, they provide limited solutions that only partially account for the OCTA scanning mechanism. Herein, we propose an integrated DL method to simultaneously tackle both factors and further enhance the reconstruction performance in speed and quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully-sampled, high-quality (8 repeated B-scans) angiograms from their corresponding undersampled, low-quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. Using an in-vivo mouse brain vasculature dataset, we evaluate our proposed framework through quantitative and qualitative assessments and demonstrate that our method can achieve superior reconstruction performance compared to the conventional means. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256[Formula: see text] while preserving the image quality, thus enabling a convenient software-only solution to enhance preclinical and clinical studies.

High speed, long range, deep penetration swept source OCT for structural and angiographic imaging of the anterior eye

This study reports the development of prototype swept-source optical coherence tomography (SS-OCT) technology for imaging the anterior eye. Advances in vertical-cavity surface-emitting laser (VCSEL) light sources, signal processing, optics and mechanical designs, enable a unique combination of high speed, long range, and deep penetration that addresses the challenges of anterior eye imaging. We demonstrate SS-OCT with a 325 kHz A-scan rate, 12.2 µm axial resolution (in air), and 15.5 mm depth range (in air) at 1310 nm wavelength. The ultrahigh 325 kHz A-scan rate not only facilitates biometry measurements by minimizing acquisition time and thus reducing motion, but also enables volumetric OCT for comprehensive structural analysis and OCT angiography (OCTA) for visualizing vasculature. The 15.5 mm (~ 11.6 mm in tissue) depth range spans all optical surfaces from the anterior cornea to the posterior lens capsule. The 1310 nm wavelength range enables structural OCT and OCTA deep in the sclera and through the iris. Achieving high speed and long range requires linearizing the VCSEL wavenumber sweep to efficiently utilize analog-to-digital conversion bandwidth. Dual channel recording of the OCT and calibration interferometer fringe signals, as well as sweep to sweep wavenumber compensation, is used to achieve invariant 12.2 µm (~ 9.1 µm in tissue) axial resolution and optimum point spread function throughout the depth range. Dynamic focusing using a tunable liquid lens extends the effective depth of field while preserving the lateral resolution. Improved optical and mechanical design, including parallax “split view” iris cameras and stable, ergonomic patient interface, facilitates accurate instrument positioning, reduces patient motion, and leads to improved imaging data yield and measurement accuracy. We present structural and angiographic OCT images of the anterior eye, demonstrating the unique imaging capabilities using representative scanning protocols which may be relevant to future research and clinical applications.

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.

Optical coherence tomography (OCT) angiolytics: a review of OCT angiography quantitative biomarkers

Optical coherence tomography angiography (OCTA) provides a non-invasive method to obtain angiography of the chorioretinal vasculature leading to its recent widespread adoption. With a growing number of studies exploring the use of OCTA, various biomarkers quantifying the vascular characteristics have come to light. In the current report, we summarize the biomarkers currently described for retinal and choroidal vasculature using OCTA systems and the methods used to obtain them. Further, we present a critical review of these methods and key findings in common retinal diseases and appraise future directions, including applications of artificial intelligence in OCTA .

An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses

Purpose

We propose a deep learning-based image reconstruction algorithm to produce high-resolution optical coherence tomographic angiograms (OCTA) of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP).

Methods

In this study, 6-mm × 6-mm macular scans with a 400 × 400 A-line sampling density and 3-mm × 3-mm scans with a 304 × 304 A-line sampling density were acquired on one or both eyes of 180 participants (including 230 eyes with diabetic retinopathy and 44 healthy controls) using a 70-kHz commercial OCT system (RTVue-XR; Optovue, Inc., Fremont, California, USA). Projection-resolved OCTA algorithm removed projection artifacts in voxel. ICP and DCP angiograms were generated by maximum projection of the OCTA signal within the respective plexus. We proposed a deep learning-based method, which receives inputs from registered 3-mm × 3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference to reconstruct 6-mm × 6-mm high-resolution ICP and DCP en face OCTA. We applied the same network on 3-mm × 3-mm angiograms to enhance these images further. We evaluated the reconstructed 3-mm × 3-mm and 6-mm × 6-mm angiograms based on vascular connectivity, Weber contrast, false flow signal (flow signal erroneously generated from background), and the noise intensity in the foveal avascular zone.

Results

Compared to the originals, the Deep Capillary Angiogram Reconstruction Network (DCARnet)-enhanced 6-mm × 6-mm angiograms had significantly reduced noise intensity (ICP, 7.38 ± 25.22, P < 0.001; DCP, 11.20 ± 22.52, P < 0.001), improved vascular connectivity (ICP, 0.95 ± 0.01, P < 0.001; DCP, 0.96 ± 0.01, P < 0.001), and enhanced Weber contrast (ICP, 4.25 ± 0.10, P < 0.001; DCP, 3.84 ± 0.84, P < 0.001), without generating false flow signal when noise intensity lower than 650. The DCARnet-enhanced 3-mm × 3-mm angiograms also reduced noise, improved connectivity, and enhanced Weber contrast in 3-mm × 3-mm ICP and DCP angiograms from 101 eyes. In addition, DCARnet preserved the appearance of the dilated vessels in the reconstructed angiograms in diabetic eyes.

Conclusions

DCARnet can enhance 3-mm × 3-mm and 6-mm × 6-mm ICP and DCP angiogram image quality without introducing artifacts.

Translational Relevance

The enhanced 6-mm × 6-mm angiograms may be easier for clinicians to interpret qualitatively.

Temporal phase evolution OCT for measurement of tissue deformation in the human retina in-vivo

We demonstrate the use of temporal phase evolution (TPE-) OCT methods to evaluate retinal tissue deformation in-vivo over time periods of several seconds. A custom built spectral domain (SD)-OCT system with an integrated retinal tracker, ensuring stable imaging with sub-speckle precision, was used for imaging. TPE-OCT measures and images phase differences between an initial reference B-scan and each of the subsequent B-scans of the evaluated temporal sequence. In order to demonstrate the precision and repeatability of the measurements, retinal nerve fiber (RNF) tissue deformations induced by retinal vessels pulsating with the heartbeat were analyzed in several healthy subjects. We show TPE maps (M-scans of phase evolution as a function of position along B-scan trace vs. time) of wrapped phase data and corresponding deformation maps in selected regions of the RNF layer (RNFL) over the course of several cardiac cycles. A reproducible phase pattern is seen at each heartbeat cycle for all imaged volunteers. RNF tissue deformations near arteries and veins up to ∼ 1.6 µm were obtained with an average precision for a single pixel of about 30 nm. Differences of motion induced by arteries and veins are also investigated.

Artificial intelligence in OCT angiography

Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides three-dimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and monitoring, OCTA technology has seen rapid adoption in research and clinical settings. The value of OCTA imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features and pathology. Today, the most powerful image analysis methods are based on artificial intelligence (AI). While AI encompasses a large variety of techniques, machine-learning-based, and especially deep-learning-based, image analysis provides accurate measurements in a variety of contexts, including different diseases and regions of the eye. Here, we discuss the principles of both OCTA and AI that make their combination capable of answering new questions. We also review contemporary applications of AI in OCTA, which include accurate detection of pathologies such as choroidal neovascularization, precise quantification of retinal perfusion, and reliable disease diagnosis.

Signal properties of split-spectrum amplitude decorrelation angiography for quantitative optical coherence tomography-based velocimetry

Split-spectrum amplitude-decorrelation angiography (SSADA) is a noninvasive and three-dimensional angiographic technique with a microscale spatial resolution based on optical coherence tomography. The SSADA signal is known to be correlated with the blood flow velocity and the quantitative velocimetry with SSADA has been expected; however, the signal properties of SSADA are not completely understood due to lack of comprehensive investigations of parameters related to SSADA signals. In this study, phantom experiments were performed to comprehensively investigate the relation of SSADA signals with flow velocities, time separations, particle concentrations, signal-to-noise ratios, beam spot sizes, and viscosities, and revealed that SSADA signals reflect the spatial commonality within a coherence volume between adjacent A-scans.

AI-based monitoring of retinal fluid in disease activity and under therapy

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Reproducible measurement of retinal fluid relies on precise AI-based segmentation methods that assign a label to each OCT voxel denoting its fluid type such as intraretinal fluid (IRF) and subretinal fluid (SRF) or pigment epithelial detachment (PED) and its location within the central 1-, 3- and 6-mm macular area. Such reliable analysis is most relevant to reflect differences in pathophysiological mechanisms and impacts on retinal function, and the dynamics of fluid resolution during therapy with different regimens and substances. Yet, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed. Greater insight regarding adequate methods for performance, validation assessment, and device- and scanning-pattern-dependent variations is necessary to empower ophthalmologists to become qualified AI users. Fluid/function correlation can lead to a better definition of valid fluid variables relevant for optimal outcomes on an individual and a population level. AI-based fluid analysis opens the way for precision medicine in real-world practice of the leading retinal diseases of modern times.

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.

Real-time measurement of repetitive micro bulk motion vector and motion noise removal in optical coherence tomography angiography

In this work, we developed a motion estimation and correction method which real-time obtained the direction and displacement of repetitive micro bulk motion (such as cardiac and respiratory motion) on an SS-OCT system without additional tracking hardware, and reduced the motion noise in optical coherence tomography angiography (OCTA). In the approach, the direction of repetitive micro bulk motion was considered fixed, and proportional relationships between the motion components in three directions were determined; Then we performed one-dimension cross-correlation to obtain depth displacement which was further used to obtain other two motion components, and greatly reduced the computation; The processing speed on a graphic processing unit was 478 pairs of B-Scans per second, and the measurement range was larger than the range of the angiogram-based methods. Lastly, corrupt angiograms were recovered by adaptive scan protocol, and reduced acquisition time in comparison with the previous work.

SPATIAL DISTRIBUTION OF CHORIOCAPILLARIS IMPAIRMENT IN EYES WITH CHOROIDAL NEOVASCULARIZATION SECONDARY TO AGE-RELATED MACULAR DEGENERATION: A Quantitative OCT Angiography Study

PURPOSE

To develop an optical coherence tomography angiography (OCTA)-based framework for quantitatively analyzing the spatial distribution of choriocapillaris (CC) impairment around choroidal neovascularization (CNV) secondary to age-related macular degeneration.

METHODS

In a retrospective, cross-sectional study, 400-kHz swept-source OCTA images from 7 eyes of 6 patients with CNV secondary to age-related macular degeneration were quantitatively analyzed using custom software. A lesion-centered zonal OCTA analysis technique-which portioned the field-of-view into zones relative to CNV boundaries-was developed to quantify the spatial dependence of CC flow deficits.

RESULTS

Quantitative, lesion-centered zonal analysis of CC OCTA images revealed highest flow-deficit percentages near CNV boundaries, decreasing in zones farther from the boundaries. Optical coherence tomography angiography using shorter (1.5 ms) interscan times revealed more severe flow deficits than OCTA using longer (3.0 ms) interscan times; however, spatial trends were similar for both interscan times. A detailed description of the OCTA processing steps and parameters was provided so as to elucidate their influence on quantitative measurements.

CONCLUSION

Impairment of the CC, assessed by flow-deficit percentages, was most prominent closest to CNV boundaries. The lesion-centered zonal analysis technique enabled quantitative CC measurements relative to focal lesions. Understanding how processing steps, imaging/processing parameters, and artifacts can affect quantitative CC measurements is important for longitudinal, OCTA-based studies of disease progression, and treatment response.

Artifacts and artifact removal in optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) enables rapid imaging of retinal vasculature in three dimensions. While the technique has provided quantification of healthy vessels as well as pathology in several diseases, it is not unusual for OCTA data to contain artifacts that may influence measurement outcomes or defy image interpretation. In this review, we discuss the sources of several OCTA artifacts-including projection, motion, and signal reduction-as well as strategies for their removal. Artifact compensation can improve the accuracy of OCTA measurements, and the most effective use of the technology will incorporate hardware and software that can perform such correction.

Application of Corneal Optical Coherence Tomography Angiography for Assessment of Vessel Depth in Corneal Neovascularization

PURPOSE

To map and measure the depths of corneal neovascularization (NV) using 3-dimensional optical coherence tomography angiography (OCTA) at 2 different wavelengths.

METHODS

Corneal NV of varying severity, distribution, and underlying etiology was examined. Average NV depth and vessel density were measured using 840-nm spectral-domain OCTA and 1050-nm swept-source OCTA. The OCTA results were compared with clinical slit-lamp estimation of NV depth.

RESULTS

Twelve eyes with corneal NV from 12 patients were imaged with OCTA. Clinically "superficial," "midstromal," and "deep" cases had an average vessel depth of 23%, 39%, and 66% on 1050-nm OCTA, respectively. Average vessel depth on OCTA followed a statistically significant ordinal trend according to the clinical classification of vessel depth (Jonckheere-Terpstra test, P < 0.001). In 8 cases where both 840-nm OCTA and 1050-nm OCTA were acquired, there was excellent agreement in the mean vessel depth between the 2 systems (concordance correlation coefficient = 0.94, P < 0.001). The average vessel density measured by 840-nm OCTA was higher (average 1.6-fold) than that measured by 1050-nm OCTA.

CONCLUSIONS

Corneal OCTA was able to map corneal NV in 3 dimensions and measure vessel depth and density. The depth of corneal NV varied between different pathologies in a manner consistent with previous pathologic studies. The measured vessel density appeared to be affected by the interscan time, which affects blood flow velocity sensitivity, and the wavelength, which affects the ability to penetrate through opacity. These findings suggest possible clinical applications of OCTA for the diagnosis of corneal pathology and quantitative monitoring of therapeutic response in patients with corneal NV.

Plexus-specific retinal vascular anatomy and pathologies as seen by projection-resolved optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) is a novel technology capable of imaging retinal vasculature three-dimensionally at capillary scale without the need to inject any extrinsic dye contrast. However, projection artifacts cause superficial retinal vascular patterns to be duplicated in deeper layers, thus interfering with the clean visualization of some retinal plexuses and vascular pathologies. Projection-resolved OCTA (PR-OCTA) uses post-processing algorithms to reduce projection artifacts. With PR-OCTA, it is now possible to resolve up to 4 distinct retinal vascular plexuses in the living human eye. The technology also allows us to detect and distinguish between various retinal and optic nerve diseases. For example, optic nerve diseases such as glaucoma primarily reduces the capillary density in the superficial vascular complex, which comprises the nerve fiber layer plexus and the ganglion cell layer plexus. Outer retinal diseases such as retinitis pigmentosa primarily reduce the capillary density in the deep vascular complex, which comprises the intermediate capillary plexus and the deep capillary plexus. Retinal vascular diseases such as diabetic retinopathy and vein occlusion affect all plexuses, but with different patterns of capillary loss and vascular malformations. PR-OCTA is also useful in distinguishing various types of choroidal neovascularization and monitoring their response to anti-angiogenic medications. In retinal angiomatous proliferation and macular telangiectasia type 2, PR-OCTA can trace the pathologic vascular extension into deeper layers as the disease progress through stages. Plexus-specific visualization and measurement of retinal vascular changes are improving our ability to diagnose, stage, monitor, and assess treatment response in a wide variety of optic nerve and retinal diseases. These applications will be further enhanced with the continuing improvement of the speed and resolution of the OCT platforms, as well as the development of software algorithms to reduce artifacts, improve image quality, and make quantitative measurements.

Interplane bulk motion analysis and removal based on normalized cross-correlation in optical coherence tomography angiography

Bulk motion seriously degrades the image quality of optical coherence tomography angiography (OCTA). Conventional correction methods focus on in-plane displacement, while the bulk motion component perpendicular to B-scans also introduces noise. This work first presents an evaluation of this component using a specific scan protocol and an approximate expression derived from peak-normalized cross-correlation values, and then quantitatively assesses how interplane bulk motion noise reduce the sensitivity of cross-sectional angiograms. Finally, we developed a repetitive bulk motion correction method based on the estimated displacements and redundant volume scans. The correction does not require registration and angiogram reconstruction of low flow sensitivity frames, and the results of in vivo mice skin OCTA imaging experiments show that the proposed method can effectively reduce bulk motion noise caused by cardiac and respiratory motion and occasional shaking, and improve OCTA image quality, which has practical significance for clinical OCTA diagnosis and analysis.

Reconstruction of high-resolution 6×6-mm OCT angiograms using deep learning

Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3×3- or 6×6-mm. Compared to 3×3-mm angiograms with proper sampling density, 6×6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6×6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3×3-mm and 6×6-mm angiograms from the same eyes. The reconstructed 6×6-mm angiograms have significantly lower noise intensity, stronger contrast and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6×6-mm OCTA.

High-resolution wide-field OCT angiography with a self-navigation method to correct microsaccades and blinks

In this study, we demonstrate a novel self-navigated motion correction method that suppresses eye motion and blinking artifacts on wide-field optical coherence tomographic angiography (OCTA) without requiring any hardware modification. Highly efficient GPU-based, real-time OCTA image acquisition and processing software was developed to detect eye motion artifacts. The algorithm includes an instantaneous motion index that evaluates the strength of motion artifact on en face OCTA images. Areas with suprathreshold motion and eye blinking artifacts are automatically rescanned in real-time. Both healthy eyes and eyes with diabetic retinopathy were imaged, and the self-navigated motion correction performance was demonstrated.

Universal digital filtering for denoising volumetric retinal OCT and OCT angiography in 3D shearlet domain

Retinal optical coherence tomography (OCT) and OCT angiography (OCTA) suffer from the degeneration of image quality due to speckle noise and bulk-motion noise, respectively. Because the cross-sectional retina has distinct features in OCT and OCTA B-scans, existing digital filters that can denoise OCT efficiently are unable to handle the bulk-motion noise in OCTA. In this Letter, we propose a universal digital filtering approach that is capable of minimizing both types of noise. Considering that the retinal capillaries in OCTA are hard to differentiate in B-scans while having distinct curvilinear structures in 3D volumes, we decompose the volumetric OCT and OCTA data with 3D shearlets, thus efficiently separating the retinal tissue and vessels from the noise in this transform domain. Compared with wavelets and curvelets, the shearlets provide better representation of the layer edges in OCT and the vasculature in OCTA. Qualitative and quantitative results show the proposed method outperforms the state-of-the-art OCT and OCTA denoising methods. Also, the superiority of 3D denoising is demonstrated by comparing the 3D shearlet filtering with its 2D counterpart.

Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning

Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and en face visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring.

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.

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.

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.

Maximum value projection produces better en face OCT angiograms than mean value projection

Optical coherence tomography angiography (OCTA) images rely on en face data projections for both qualitative and quantitative interpretation. Both maximum value and mean value projections are commonly used, and many researchers consider them essentially interchangeable approaches. On the contrary, we find that maximum value projection achieves a consistently higher signal-to-noise ratio and higher image contrast across multiple vascular layers, in both healthy eyes and for each disease examined.

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.

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.

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.