Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography

Quantitative Evaluation of Type 1 and Type 2 Choroidal Neovascularization Components Under Treatment With Projection-Resolved OCT Angiography

Purpose

To evaluate the response of type 1 and type 2 macular neovascularization (MNV) components under anti-vascular endothelial growth factor (VEGF) treatment in age-related macular degeneration (AMD) using projection-resolved optical coherence tomography angiography (PR-OCTA).

Methods

This retrospective study included eyes with treatment-naïve exudative AMD treated with anti-VEGF injections under a pro re nata (PRN) protocol over 1 year. Two-dimensional MNV areas and three-dimensional MNV volumes were derived from macular PR-OCTA scans using an automated convolutional neural network. MNV was detected as flow signal within the outer retinal slab. Type 1 components and type 2 components were analyzed separately.

Results

Of 17 enrolled eyes, 12 eyes were pure type 1 MNV and five eyes were type 2 MNV. In eyes with pure type 1, the total (sum of type 1 and type 2 components) MNV area and volume did not change from baseline to 6 months or 12 months (P > 0.05). In eyes with type 2 MNV, the total MNV area significantly decreased from the baseline to 6 months (P = 0.0074) and 12 months (P = 0.014). The total type 2 MNV volume also decreased from baseline visit to visits at 6 months and at 12 months, nearing statistical signifiicance (P = 0.061 and P = 0.074). In eyes with type 2 MNV, the type 1 component increased from 0.093 mm2 to 0.30 mm2 (P = 0.058), and the type 2 component decreased from 0.37 mm2 at 6 months to 0 at 12 months (P = 0.0087).

Conclusions

Type 1 and type 2 MNV may have different response under PRN anti-VEGF treatment over 1 year.

Optical coherence tomography angiography in neovascular age-related macular degeneration: comprehensive review of advancements and future perspective

Optical coherence tomography angiography (OCTA) holds promise in enhancing the care of various retinal vascular diseases, including neovascular age-related macular degeneration (nAMD). Given nAMD's vascular nature and the distinct vasculature of macular neovascularization (MNV), detailed analysis is expected to gain significance. Research in artificial intelligence (AI) indicates that en-face OCTA views may offer superior predictive capabilities than spectral domain optical coherence tomography (SD-OCT) images, highlighting the necessity to identify key vascular parameters. Analyzing vasculature could facilitate distinguishing MNV subtypes and refining diagnosis. Future studies correlating OCTA parameters with clinical data might prompt a revised classification system. However, the combined utilization of qualitative and quantitative OCTA biomarkers to enhance the accuracy of diagnosing disease activity remains underdeveloped. Discrepancies persist regarding the optimal biomarker for indicating an active lesion, warranting comprehensive prospective studies for validation. AI holds potential in extracting valuable insights from the vast datasets within OCTA, enabling researchers and clinicians to fully exploit its OCTA imaging capabilities. Nevertheless, challenges pertaining to data quantity and quality pose significant obstacles to AI advancement in this field. As OCTA gains traction in clinical practice and data volume increases, AI-driven analysis is expected to further augment diagnostic capabilities.

Tail Artifact Removal via Transmittance Effect Subtraction in Optical Coherence Tail Artifact Images

Optical Coherence Tomography Angiography (OCTA) has revolutionized non-invasive, high-resolution imaging of blood vessels. However, the challenge of tail artifacts in OCTA images persists. In response, we present the Tail Artifact Removal via Transmittance Effect Subtraction (TAR-TES) algorithm that effectively mitigates these artifacts. Through a simple physics-based model, the TAR-TES accounts for variations in transmittance within the shallow layers with the vasculature, resulting in the removal of tail artifacts in deeper layers after the vessel. Comparative evaluations with alternative correction methods demonstrate that TAR-TES excels in eliminating these artifacts while preserving the essential integrity of vasculature images. Crucially, the success of the TAR-TES is closely linked to the precise adjustment of a weight constant, underlining the significance of individual dataset parameter optimization. In conclusion, TAR-TES emerges as a powerful tool for enhancing OCTA image quality and reliability in both clinical and research settings, promising to reshape the way we visualize and analyze intricate vascular networks within biological tissues. Further validation across diverse datasets is essential to unlock the full potential of this physics-based solution.

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed.

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.

CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA)

This paper aims to present an artificial intelligence-based algorithm for the automated segmentation of Choroidal Neovascularization (CNV) areas and to identify the presence or absence of CNV activity criteria (branching, peripheral arcade, dark halo, shape, loop and anastomoses) in OCTA images. Methods: This retrospective and cross-sectional study includes 130 OCTA images from 101 patients with treatment-naïve CNV. At baseline, OCTA volumes of 6 × 6 mm2 were obtained to develop an AI-based algorithm to evaluate the CNV activity based on five activity criteria, including tiny branching vessels, anastomoses and loops, peripheral arcades, and perilesional hypointense halos. The proposed algorithm comprises two steps. The first block includes the pre-processing and segmentation of CNVs in OCTA images using a modified U-Net network. The second block consists of five binary classification networks, each implemented with various models from scratch, and using transfer learning from pre-trained networks. Results: The proposed segmentation network yielded an averaged Dice coefficient of 0.86. The individual classifiers corresponding to the five activity criteria (branch, peripheral arcade, dark halo, shape, loop, and anastomoses) showed accuracies of 0.84, 0.81, 0.86, 0.85, and 0.82, respectively. The AI-based algorithm potentially allows the reliable detection and segmentation of CNV from OCTA alone, without the need for imaging with contrast agents. The evaluation of the activity criteria in CNV lesions obtains acceptable results, and this algorithm could enable the objective, repeatable assessment of CNV features.

Longitudinal Comparison of Constant Artifacts in Optical Coherence Tomography Angiography in Patients with Posterior Uveitis Compared to Healthy Subjects

Background: Knowledge about artifacts in optical coherence tomography angiography (OCTA) is important to avoid misinterpretations. An overview of possible artifacts in posterior uveitis provides important information for interpretations. Methods: In this monocentric prospective study, OCTA images from a total of 102 eyes of 54 patients with posterior uveitis, and an age-matched control group including 34 healthy subjects (67 eyes), were evaluated (day 0, month 3, month 6). We assigned different artifacts to distinct layers. Various types of artifacts were examined in different retinal layers. The χ2 test for the comparison between the control and uveitis group and Cochran’s Q test for the longitudinal comparison within the uveitis group were used. Results: A total of 2238 images were evaluated; 1836 from uveitis patients and 402 from healthy subjects. A total of 2193 artifacts were revealed. Projection (812 [36.3%]), segmentation (579 [25.9%]), shadowing (404 [18.1%]), and blink artifacts (297 [13.3%]) were the most common artifact types. The uveitis group displayed significantly more segmentation artifacts and projection artifacts (p < 0.001). No segmentation artifacts were documented in healthy subjects. The consecutive examinations within the uveitis group revealed the same artifact types without significance (p > 0.1). Conclusions: The uveitis patients showed more segmentation and projection artifacts than the control group. Within the uveitis group, artifacts remained longitudinally constant in terms of artifact type and pattern. The artifacts therefore appear to be reproducible on an individual level.

LamNet: A Lesion Attention Maps-Guided Network for the Prediction of Choroidal Neovascularization Volume in SD-OCT Images

Choroidal neovascularization (CNV) volume prediction has an important clinical significance to predict the therapeutic effect and schedule the follow-up. In this paper, we propose a Lesion Attention Maps-Guided Network (LamNet) to automatically predict the CNV volume of next follow-up visit after therapy based on 3-dimentional spectral-domain optical coherence tomography (SD-OCT) images. In particular, the backbone of LamNet is a 3D convolutional neural network (3D-CNN). In order to guide the network to focus on the local CNV lesion regions, we use CNV attention maps generated by an attention map generator to produce the multi-scale local context features. Then, the multi-scale of both local and global feature maps are fused to achieve the high-precision CNV volume prediction. In addition, we also design a synergistic multi-task predictor, in which a trend-consistent loss ensures that the change trend of the predicted CNV volume is consistent with the real change trend of the CNV volume. The experiments include a total of 541 SD-OCT cubes from 68 patients with two types of CNV captured by two different SD-OCT devices. The results demonstrate that LamNet can provide the reliable and accurate CNV volume prediction, which would further assist the clinical diagnosis and design the treatment options.

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 .

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.

MF-Net: Multi-Scale Information Fusion Network for CNV Segmentation in Retinal OCT Images

Choroid neovascularization (CNV) is one of the blinding ophthalmologic diseases. It is mainly caused by new blood vessels growing in choroid and penetrating Bruch's membrane. Accurate segmentation of CNV is essential for ophthalmologists to analyze the condition of the patient and specify treatment plan. Although many deep learning-based methods have achieved promising results in many medical image segmentation tasks, CNV segmentation in retinal optical coherence tomography (OCT) images is still very challenging as the blur boundary of CNV, large morphological differences, speckle noise, and other similar diseases interference. In addition, the lack of pixel-level annotation data is also one of the factors that affect the further improvement of CNV segmentation accuracy. To improve the accuracy of CNV segmentation, a novel multi-scale information fusion network (MF-Net) based on U-Shape architecture is proposed for CNV segmentation in retinal OCT images. A novel multi-scale adaptive-aware deformation module (MAD) is designed and inserted into the top of the encoder path, aiming at guiding the model to focus on multi-scale deformation of the targets, and aggregates the contextual information. Meanwhile, to improve the ability of the network to learn to supplement low-level local high-resolution semantic information to high-level feature maps, a novel semantics-details aggregation module (SDA) between encoder and decoder is proposed. In addition, to leverage unlabeled data to further improve the CNV segmentation, a semi-supervised version of MF-Net is designed based on pseudo-label data augmentation strategy, which can leverage unlabeled data to further improve CNV segmentation accuracy. Finally, comprehensive experiments are conducted to validate the performance of the proposed MF-Net and SemiMF-Net. The experiment results show that both proposed MF-Net and SemiMF-Net outperforms other state-of-the-art algorithms.

Quantitative analysis of retinal intermediate and deep capillary plexus in patients with retinal deep vascular complex ischemia

AIM

To quantitatively analyze the retinal intermediate and deep capillary plexus (ICP and DCP) in patients with retinal deep vascular complex ischemia (RDVCI), using 3D projection artifacts removal (3D PAR) optical coherence tomography angiography (OCTA).

METHODS

RDVCI patients and gender- and age-matched healthy controls were assessed and underwent OCTA examinations. The parafoveal vessel density (PFVD) of retinal deep vascular complex (DVC), ICP, and DCP were analyzed, and the percentage of reduction (PR) of PFVD was calculated.

RESULTS

Twenty-four eyes in 22 RDVCI patients (20 in acute phase and 4 in chronic phase) and 24 eyes of 22 healthy subjects were enrolled as the control group. Significant reduction of PFVD in DVC, ICP, and DCP was observed in comparison with the controls (DVC: acute: 43.59%±6.58% vs 49.92%±5.49%, PR=12.69%; chronic: 43.50%±3.33% vs 51.20%±3.80%, PR=15.04%. ICP: acute: 40.28%±7.91% vs 46.97%±7.14%, PR=14.23%; chronic: 41.48%±2.87% vs 46.43%±3.29%, PR=10.66%. DCP: acute: 45.44%±8.27% vs 51.51%±9.97%, PR=11.79%; chronic: 37.78%±3.48% vs 51.73%±5.17%, PR=26.97%; all P<0.05). No significant PR difference was found among DVC, ICP, and DCP of RDVCI in acute phase (P=0.812), but significant difference in chronic phase (P=0.006, DVC vs DCP, ICP vs DCP). No significant difference in PR between acute and chronic phases in the DVC (P=0.735) or ICP (P=0.681) was found, but significant difference in the DCP (P=0.041).

CONCLUSION

The PFVD of DVC, ICP, and DCP in RDVCI is significantly decreased in both acute and chronic phases. ICP impairment is stabilized from acute to chronic phase in RDVCI, whereas subsequent DCP impairment is uncovered and can be explained by ischemia-reperfusion damage.

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.

Customized Slab-Segmentation Method for Projection-Artifact Elimination in Best Vitelliform Macular Dystrophy: A Swept-Source Optical Coherence Tomography Angiography Study

Purpose

To assess the efficacy of customized slab segmentation in eliminating projection artifacts in swept-source optical coherence tomography angiography (SS-OCTA) images of Best vitelliform macular dystrophy (BVMD).

Methods

Prospective case series including different stages of BVMD. We analyzed SS-OCTA images for flow signals in the outer retina and coregistered B-scan images for distortion of the segmentation slabs defining the outer retina. We applied a customized method for slab realignment whenever BVMD lesions produced distortion of the slabs. Afterward, we checked the images to determine whether the previously noted flow signal had persisted or disappeared, described as “true flow” or “pseudoflow”, respectively. Categorical variables were analyzed with X² or Fisher’s exact tests, while quantitative variables were analyzed with independent t-test at p<0.05.

Results

The study included 39 eyes of 22 patients. We detected BVMD patterns I (dome-shaped hyperreflective lesion without neurosensory retinal detachment), II (knob-like hyperreflective lesion with localized neurosensory retinal detachment), and III (heterogeneous scattered hyperreflective material) in 49%, 23%, and 28% of eyes, respectively. Pseudoflow was evident mostly in eyes with pattern II lesions, presence of flow signal within BVMD lesions, and lesions whose height represented >80% of the retinal thickness (p<0.001).

Conclusion

Customized slab segmentation is effective in eliminating projection artifact in SS-OCTA images of BVMD.

Summary

Projection artifact is a significant confounding factor in emerging SS-OCTA technology through production of pseudoflow signals that can lead to misinterpretation of images of BVMD lesions. The present study proposes a customized method for correction of segmentation errors to eliminate projection artifacts in SS-OCTA images of BVMD patients.

Choroidal neovascularization fusion visualization for spectral-domain optical coherence tomography and optical coherence tomography angiography images

PURPOSE

The spectral-domain optical coherence tomography (SD-OCT) images and OCT angiography (OCTA) images can provide complementary information for choroidal neovascularization (CNV) visualization. We expected to simultaneously display multifaceted characteristics of CNV in a single projection image.

METHODS

We proposed a fusion method for CNV visualization by combining structural and angiographic images, which mainly involves four steps: (a) Generate SD-OCT and OCTA projection images from original volumetric data with retinal layer restriction. (b) For SD-OCT projection images, enhance retinal vessels and CNV. (c) For OCTA images, detect CNV region based on multimodal data and display the neovascularization in false color. (d) A maximum fusion strategy was adopted to generate the fused images.

RESULTS

Experimental results with 30 cases from 30 patients demonstrate that the fused images are more effective in displaying CNV than single-modality projection images. The average information entropy and the mean gradient in the CNV regions for SD-OCT projection images, OCTA images, and the fusion images are 4.66/0.21, 5.45/0.45, and 6.8/0.58, respectively.

CONCLUSIONS

The proposed method is more effective for CNV visualization than the conventional single-modality image-based method. The proposed method can combine complementary information from multimodal images and provide a satisfying visual effect.

Intra- and Intergrader Agreement for Detection of OCT Angiographic Characteristics Associated With Type 3 Neovascularization

Purpose

To examine the intra- and intergrader agreement on morphologic characteristics of type 3 neovascularization on optical coherence tomography angiography (OCT-A).

Methods

OCT-A images of 22 eyes from 21 patients with a new-onset, treatment-naive type 3 neovascularization were included in this cross-sectional retrospective agreement study. Each image was graded three times by two independent medical retina specialists to assess intra- and intergrader agreement. The graders scored the presence or absence of the following vascular and structural features: intraretinal neovascularization (IRN), subretinal neovascularization, sub-retinal pigment epithelium (RPE) neovascularization (SRPEN), retinal choroidal anastomosis (RCA), intraretinal cysts, subretinal fluid, and pigment epithelial detachment. Agreement was analyzed for each feature using Gwet's AC₁, к statistics, and percentage of agreement.

Results

The best agreement (AC₁) was found for intraretinal neovascularization (within_grader1: 0.94; within_grader2: 0.93 and between: 1.00) and intraretinal cysts (within_grader1, 1.00; within_grader2, 0.97 and between, 1.00). The poorest intragrader agreements were observed for SRPEN (within_grader1, 0.54 and within_grader2, 0.36) and RCA (within_grader1, 0.45 and within_grader2, 0.52), and the poorest intergrader agreement was found for SRPEN, RCA, and pigment epithelial detachment (0.18, 0.37, and 0.15, respectively).

Conclusions

Although the agreement values were high for intraretinal features, considerable grader variability was found for the vascular and structural features in the deeper retina or under the RPE. Clinicians should be careful to base therapeutic decisions on qualitative OCT-A assessment, because even well-trained specialists show a considerable grader variation in their subjective evaluation.

Translational Relevance

The clinical value of OCT-A imaging largely depends on the agreement of subjective evaluations by ophthalmologists.

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.

Optical Coherence Tomography Angiography in Intermediate and Late Age-Related Macular Degeneration: Review of Current Technical Aspects and Applications

Optical coherence tomography angiography (OCTA) is a non-invasive diagnostic instrument that has become indispensable for the management of age-related macular degeneration (AMD). OCTA allows quickly visualizing retinal and choroidal microvasculature, and in the last years, its use has increased in clinical practice as well as for research into the pathophysiology of AMD. This review provides a discussion of new technology and application of OCTA in intermediate and late AMD.

The Effect of Software Versions on the Measurement of Retinal Vascular Densities Using Optical Coherence Tomography Angiography

BACKGROUND

The goal of the study was to determine the effect of different software versions on the measurement of retinal vessel densities using optical coherence tomography angiography (OCTA) in normal subjects.

METHODS

Thirty-two eyes of eighteen healthy subjects were imaged using two OCTA devices: the Optovue RTVue and the Zeiss Cirrus. The macular 3 × 3 mm scan protocol was used. The images acquired using the Optovue OCTA device were exported using two different software versions in the system and compared to the images acquired through the Zeiss OCTA. In addition, the Optovue OCTA images were exported after manual adjustment of the segmentation boundaries according to the intraretinal layer definition. The densities of the superficial vascular plexus (SVP) and deep vascular plexus (DVP) were measured using fractal analysis by box-counting (Dbox).

RESULTS

Both the vessel densities of the SVP and DVP acquired using the Optovue OCTA device were significantly different when compared to those from the Zeiss OCTA device (all, P <.05). No significant difference was found between the vessel densities of the SVP exported using both the new and old versions of Optovue (P >.05). However, the DVP exported using the new Optovue software version was significantly different compared to those exported using the old version (P <.05). The vessel densities of the SVP and DVP were related among the Optovue OCTA software versions and manual adjustment method (r ranged from 0.55 to 0.77; all P <.05).

CONCLUSION

This is the first study to determine that different software versions with various intraretinal layer segmentation methods affect the vessel density measurements of the SVP and DVP.

Optical Coherence Tomography Angiography in Diabetes and Diabetic Retinopathy

Diabetic retinopathy (DR) is a common complication of diabetes mellitus that disrupts the retinal microvasculature and is a leading cause of vision loss globally. Recently, optical coherence tomography angiography (OCTA) has been developed to image the retinal microvasculature, by generating 3-dimensional images based on the motion contrast of circulating blood cells. OCTA offers numerous benefits over traditional fluorescein angiography in visualizing the retinal vasculature in that it is non-invasive and safer; while its depth-resolved ability makes it possible to visualize the finer capillaries of the retinal capillary plexuses and choriocapillaris. High-quality OCTA images have also enabled the visualization of features associated with DR, including microaneurysms and neovascularization and the quantification of alterations in retinal capillary and choriocapillaris, thereby suggesting a promising role for OCTA as an objective technology for accurate DR classification. Of interest is the potential of OCTA to examine the effect of DR on individual retinal layers, and to detect DR even before it is clinically detectable on fundus examination. We will focus the review on the clinical applicability of OCTA derived quantitative metrics that appear to be clinically relevant to the diagnosis, classification, and management of patients with diabetes or DR. Future studies with longitudinal design of multiethnic multicenter populations, as well as the inclusion of pertinent systemic information that may affect vascular changes, will improve our understanding on the benefit of OCTA biomarkers in the detection and progression of DR.

Mean-Subtraction Method for De-shadowing of Tail Artifacts in Cerebral OCTA Images: A Proof of Concept

When imaging brain vasculature with optical coherence tomography angiography (OCTA), volumetric analysis of cortical vascular networks in OCTA datasets is frequently challenging due to the presence of artifacts, which appear as multiple-scattering tails beneath superficial large vessels in OCTA images. These tails shadow underlying small vessels, making the assessment of vascular morphology in the deep cortex difficult. In this work, we introduce an image processing technique based on mean subtraction of the depth profile that can effectively reduce these tails to better reveal small hidden vessels compared to the current tail removal approach. With the improved vascular image quality, we demonstrate that this simple method can provide better visualization of three-dimensional vascular network topology for quantitative cerebrovascular studies.

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.

Effect of Choroidal Vessel Density on the Ellipsoid Zone and Visual Function in Retinitis Pigmentosa Using Optical Coherence Tomography Angiography

Purpose

We evaluate the effect of choroidal vessel density on the residual length of the ellipsoid zone (EZ) and visual function in patients with retinitis pigmentosa (RP) using optical coherence tomography angiography (OCTA).

Methods

Fifty-three patients with RP (n = 101 eyes) and 53 normal participants (n = 76 eyes) were enrolled in this study. Patients with RP were assigned to three groups according to their best-corrected visual acuity (BCVA). All patients underwent ophthalmologic examinations, including BCVA, fundus examination performed with a slit-lamp using an indirect 90 diopter (D) lens, OCTA, full-field electroretinogram (ERG), and visual field. The choroidal vessel density in the choriocapillaris-Sattler's layer (DC-S), Haller's layer (DH), horizontal length of the ellipsoid (HEL), and vertical length of the ellipsoid (VEL) were assessed using OCTA and Adobe Photoshop CS3 extended software.

Results

A significantly increasing impairment of choroidal vessel density (DC-S and DH) was characterized in the RP groups compared to those of the controls (P < 0.05 for all). The magnitude of the reduction in the DC-S and DH was much easier to identify for more severely impaired BCVA in the RP groups (P < 0.05 for all). The DC-S had the strongest correlation with the HEL, VEL, BCVA, visual field, and b-wave amplitude (r = 0.735, r = 0.753, r = -0.843, r = 0.579, and r = 0.671, respectively).

Conclusions

Using noninvasive OCTA, choroidal microcirculation, especially in the small/middle choroidal vessel layers, was a prominent factor affecting the EZ, visual acuity, visual field, and recordable ERG b-wave amplitude of patients with RP. This may provide new insights into the progress mechanism and treatment of RP.

Robust non-perfusion area detection in three retinal plexuses using convolutional neural network in OCT angiography

Non-perfusion area (NPA) is a quantitative biomarker useful for characterizing ischemia in diabetic retinopathy (DR). Projection-resolved optical coherence tomographic angiography (PR-OCTA) allows visualization of retinal capillaries and quantify NPA in individual plexuses. However, poor scan quality can make current NPA detection algorithms unreliable and inaccurate. In this work, we present a robust NPA detection algorithm using convolutional neural network (CNN). By merging information from OCT angiograms and OCT reflectance images, the CNN could exclude signal reduction and motion artifacts and detect the avascular features from local to global with the resolution preserved. Across a wide range of signal strength indices, and on both healthy and DR eyes, the algorithm achieved high accuracy and repeatability.

Optical coherence tomography angiography in uveitis

Before the introduction of optical coherence tomography angiography (OCTA) in the early 2000s, dye-based angiography was considered the “gold standard” for the diagnosis and monitoring of ocular inflammation. OCTA is a novel technique, which demonstrates capillary networks based on the amount of light returned from moving blood cells, providing further information on pathophysiological changes in uveitis.

The aim of this review is to describe the basic principles of OCTA and its application to ocular inflammatory disorders. It particularly emphasizes on its contribution not only in the diagnosis and management of the disease but also in the identification of possible complications, comparing it with fundus fluorescein angiography (FFA) and indocyanine green angiography (ICGA). Although the advent of OCTA has remarkably enhanced the assessment of uveitic entities, we highlight the need for further investigation in order to better understand its application to these conditions.

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments.

Differential phase standard-deviation-based optical coherence tomographic angiography for human retinal imaging in vivo

We present a differential phase standard-deviation (DPSD)-based optical coherence tomographic (OCT) angiography (OCTA) technique to calculate the angiography images of the human retina. The standard deviation was calculated along the depth direction on the differential phase image of two B-scans (from the same position, at different times) to contrast dynamic vascular signals. The performance of a DPSD was verified by both phantom and in vivo experiments. When compared to other OCTA algorithms such as phase variance OCT, speckle variance OCT, and optical microangiography, we showed that a DPSD achieved improved image contrast and higher sensitivity. Furthermore, we also found the improved signal-to-noise ratio and contrast-to-noise ratio of 1.6 dB and 0.5, respectively, in large scanning range images.

Classification of Choroidal Neovascularization Using Projection-Resolved Optical Coherence Tomographic Angiography

Purpose

To evaluate if projection-resolved optical coherence tomographic angiography (PR-OCTA) reduces projection artifact with less attenuation of choroidal neovascularization (CNV) flow signal compared to conventional OCTA with slab subtraction.

Methods

In this retrospective cross-sectional study, participants with subfoveal treatment-naïve CNV secondary to age-related macular degeneration underwent OCTA. Scans were exported for custom processing including manual segmentation as necessary, application of slab subtraction and PR-OCTA algorithm, and calculation of CNV vascular area and connectivity. CNV was classified as type 1, minimally type 2, or predominantly type 2 based on fluorescein angiography (FA) and OCT. Two masked retina specialists independently classified CNV using cross-sectional conventional OCTA and PR-OCTA.

Results

A total of 17 eyes were enrolled in this study. Mean CNV vessel area (mm2) was 0.67 ± 0.51 for PR-OCTA and 0.53 ± 0.41 for slab subtraction (P = 0.018). Mean vascular connectivity was 96.80 ± 1.28 for PR-OCTA and 90.90 ± 4.42 (P = 0.018) for slab subtraction. Within-visit repeatability (coefficient of variation) of PR-OCTA was 0.044 for CNV vessel area and 0.012 for vascular connectivity, compared to 0.093 and 0.028 by slab subtraction. PR-OCTA classification agreement with FA/OCT was 88.2% and 76.5% for the two graders, while conventional OCTA agreement was 58.8% and 70.6% (grader 1, P = 0.025; grader 2, P = 0.56). Moreover, PR-OCTA enabled the individual quantification of type 1 and type 2 components of a CNV.

Conclusions

PR-OCTA had greater CNV vessel area and vascular connectivity, as well as better repeatability, compared to slab subtraction, suggesting PR-OCTA is a superior technique for imaging CNV. Furthermore, PR-OCTA removes projection artifact on cross-sectional OCTA, improving the ability to classify and quantify CNV components.

An overview of optical coherence tomography angiography and the posterior pole

Optical coherence tomography angiography is a relatively new, noninvasive technology that has revolutionized imaging of the retinal and choroidal microvasculature. This technology is based on the detection of movement or changes that represent moving red cells in sequential optical coherence tomography scans. As with other established imaging technologies, it has unique benefits as well as certain disadvantages, which include a limited field of view and vulnerability to imaging artifacts. However, software and hardware improvements are continually evolving to mitigate these limitations. Optical coherence tomography angiography has been used to gain a better understanding of microvascular changes across a spectrum of ocular diseases including diabetic retinopathy, age-related macular degeneration, glaucoma, and retinal vein occlusions. In this article, we review algorithms and techniques commonly utilized in optical coherence tomography angiography systems and compare optical coherence tomography angiography to fluorescein angiography, the current gold standard for imaging the retinal vasculature. In addition, we provide an overview of important optical coherence tomography angiography findings in a variety of ocular diseases. Although the clinical role of this technology is still poorly defined, optical coherence tomography angiography has the potential to become an invaluable tool in the diagnosis and monitoring of vascular pathologies.

Optimization-based vessel segmentation pipeline for robust quantification of capillary networks in skin with optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) provides in-vivo images of microvascular perfusion in high resolution. For its application to basic and clinical research, an automatic and robust quantification of the capillary architecture is mandatory. Only this makes it possible to reliably analyze large amounts of image data, to establish biomarkers, and to monitor disease developments. However, due to its optical properties, OCTA images of skin often suffer from a poor signal-to-noise ratio and contain imaging artifacts. Previous work on automatic vessel segmentation in OCTA mostly focuses on retinal and cerebral vasculature. Its applicability to skin and, furthermore, its robustness against imaging artifacts had not been systematically evaluated. We propose a segmentation method that improves the quality of vascular quantification in OCTA images even if corrupted by imaging artifacts. Both the combination of image processing methods and the choice of their parameters are systematically optimized to match the manual labeling of an expert for OCTA images of skin. The efficacy of this optimization-based vessel segmentation is further demonstrated on sample images as well as by a reduced error of derived quantitative vascular network characteristics.

Detection of Nonexudative Choroidal Neovascularization and Progression to Exudative Choroidal Neovascularization Using OCT Angiography

PURPOSE

To detect nonexudative choroidal neovascularization (CNV) in age-related macular degeneration (AMD) with OCT angiography (OCTA) and determine the risk of exudative CNV developing compared with eyes without nonexudative CNV.

DESIGN

Prospective, longitudinal, observational study.

PARTICIPANTS

Consecutive patients with drusen and pigmentary changes in the study eye and exudative neovascular AMD in the fellow eye.

METHODS

In this prospective observational study, participants underwent spectral-domain OCTA (AngioVue; Optovue, Inc, Fremont, CA), clinical examination, and structural OCT at baseline and 6-month intervals for 2 years. OCT angiography images were exported for custom processing to remove projection artifact and calculate CNV vessel area.

MAIN OUTCOME MEASURES

Rate of developing exudation in eyes with and without nonexudative CNV as detected by OCTA on regular follow-up.

RESULTS

Sixty-three study participants were followed up every 6 months and 48 completed the 2-year study. Mean age was 78 years and 60.3% were female. On the baseline visit, 5 eyes (7.9%) were found to have nonexudative CNV by OCTA, and 3 of them demonstrated exudation. Of 58 eyes with a normal OCTA on baseline visit, 5 eyes developed nonexudative CNV during a follow-up visit. All 5 of these nonexudative CNV went on to develop exudation in subsequent visits. Overall, 8 of the 10 eyes with nonexudative CNV developed exudation with a mean time of 8 months and mean CNV area growth rate of 20% per month (P = 0.014, exponential model). Initiation of antiangiogenic treatment halted their growth. In comparison, exudation occurred in only 6 of the 53 eyes (11%) that lacked a precursor nonexudative CNV. Cox proportional hazard analysis showed that having nonexudative CNV detected was associated with an 18.1-fold increase in the rate of exudation subsequently developing (P < 0.0001).

CONCLUSIONS

Nonexudative CNV frequently is detected by OCTA in the fellow eyes of those with exudative CNV. These lesions carry a high risk of exudation developing within the first year after detection and could benefit from close monitoring. The high risk of progression may justify prophylactic treatment; further studies are needed.

Analysis of Foveal Microvascular Abnormalities in Diabetic Retinopathy Using Optical Coherence Tomography Angiography with Projection Artifact Removal

PURPOSE

To analyze foveal microvascular abnormalities in different stages of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) with projection artifact removal (PAR).

METHODS

We analyzed 93 eyes of 59 patients with diabetes-31 with no DR (no DR), 34 with mild to moderate nonproliferative DR (mild DR), and 28 with severe nonproliferative DR to proliferative DR (severe DR)-and 31 age-matched healthy controls. Sections measuring 3 × 3 mm2 centered on the fovea were obtained using OCTA. The area, perimeter, and acircularity index (AI) of the foveal avascular zone (FAZ), vessel density within a 300 μm wide region of the FAZ (FD-300), and parafoveal vessel density in the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were calculated using novel built-in software with PAR.

RESULTS

There was no statistically significant difference in the FAZ area (p=0.162). There was a statistically significant difference in the FAZ perimeter (p=0.010) and the AI (p < 0.001) between the four groups. There was a correlation between the AI and the increasing severity of DR (p=0.010). Statistically significant decreases of vessel density in the FD-300, SCP, and DCP were observed (all p < 0.001). There was a difference in parafoveal vessel density in the DCP between the healthy control eyes and the eyes with diabetes without DR (p=0.027). There was a significant correlation between vessel density and increasing severity of DR (p < 0.001).

CONCLUSION

Compared with the FAZ area, AI allows a more helpful quantitative assessment of the changes in the FAZ. Vessel density determined using OCTA with PAR might be a useful parameter indicating the progression of DR. Parafoveal vessel density in the DCP after PAR might be a potential early biomarker of DR before appearance of clinically evident retinopathy and needs further investigation.

Automated segmentation of retinal layer boundaries and capillary plexuses in wide-field optical coherence tomographic angiography

Advances in the retinal layer segmentation of structural optical coherence tomography (OCT) images have allowed the separation of capillary plexuses in OCT angiography (OCTA). With the increased scanning speeds of OCT devices and wider field images (≥10 mm on fast-axis), greater retinal curvature and anatomic variations have introduced new challenges. In this study, we developed a novel automated method to segment seven retinal layer boundaries and two retinal plexuses in wide-field OCTA images. The algorithm was initialized by a series of points forming a guidance point array that estimates the location of retinal layer boundaries. A guided bidirectional graph search method consisting of an improvement of our previous segmentation algorithm was used to search for the precise boundaries. We validated the method on normal and diseased eyes, demonstrating subpixel accuracy for all groups. By allowing independent visualization of the superficial and deep plexuses, this method shows potential for the detection of plexus-specific peripheral vascular abnormalities.

Automatic segmentation of abnormal capillary nonperfusion regions in optical coherence tomography angiography images using marker-controlled watershed algorithm

Diabetic retinopathy (DR) is one of the most complications of diabetes. It is a progressive disease leading to significant vision loss in the patients. Abnormal capillary nonperfusion (CNP) regions are one of the important characteristics of DR increasing with its progression. Therefore, automatic segmentation and quantification of abnormal CNP regions can be helpful to monitor the patient's treatment process. We propose an automatic method for segmentation of abnormal CNP regions on the superficial and deep capillary plexuses of optical coherence tomography angiography (OCTA) images using the marker-controlled watershed algorithm. The proposed method has three main steps. In the first step, original images are enhanced using the vesselness filter and then foreground and background marker images are computed. In the second step, abnormal CNP region candidates are segmented using the marker-controlled watershed algorithm, and in the third step, the candidates are modeled using an undirected weighted graph and finally, by applying merging and removing procedures correct abnormal CNP regions are identified. The proposed method was evaluated on a dataset with 36 normal and diabetic subjects using the ground truth obtained by two observers. The results show the proposed method outperformed some of the state-of-the-art methods on the superficial and deep capillary plexuses according to the most important metrics.

Quantitative Evaluation of Choroidal Neovascularization under Pro Re Nata Anti-Vascular Endothelial Growth Factor Therapy with OCT Angiography

PURPOSE

To use optical coherence tomography angiography (OCTA) derived quantitative metrics to assess the response of choroidal neovascularization to pro-re-nata (PRN) anti-endothelial growth factor (anti-VEGF) treatment in neovascular age-related macular degeneration (AMD).

DESIGN

Prospective longitudinal cohort study.

PARTICIPANTS

Fourteen eyes from 14 study participants with treatment-naïve neovascular AMD were enrolled.

METHODS

Subjects were evaluated monthly and treated with intravitreal anti-VEGF agents under a PRN protocol for one year. At each visit, two 3×3 mm2 OCTA scans were obtained. Custom image processing was applied to segment the outer retinal slab, suppress projection artifact, and automatically detect CNV. CNV membrane area (mm2) and CNV vessel area (mm2) was calculated.

MAIN OUTCOMES

Individual and mean CNV membrane area and CNV vessel area at each visit; within-visit repeatability determined by coefficient of variation.

RESULTS

Eight eyes had entire CNV within 3×3 mm2 scanning area and had adequate image quality for CNV quantification. One case (case #2) was excluded from analysis due to the presence of a large subretinal hemorrhage overlying the CNV membrane. In the remaining cases, CNV vessel area was reduced by 39%, 50%, 43%, and 41% at months 1, 3, 6, and 12 respectively. CNV membrane area was reduced by 39%, 51%, 54%, and 45% at months 1, 3, 6, and 12. At month 6, mean change from baseline was not statistically significant for CNV vessel area, while it was statistically significant for CNV membrane area. Neither metric was significantly different compared to baseline at month 12. Individual analyses revealed each CNV had a unique response under PRN treatment. Within-visit repeatability was was 7.96% (coefficient of variation) for CNV vessel area and 7.37% for CNV membrane area.

CONCLUSIONS

In this small exploratory study of CNV response to PRN anti-VEGF treatment, both CNV vessel area and membrane area were reduced compared to baseline after three months. After one year of follow-up, these reductions were no longer statistically significant. When anti-VEGF treatment was held, increasing CNV vessel area over time often resulted in exudation, but it was not possible to exactly when exudation occurs.

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.

Aflibercept Treatment Leads to Vascular Abnormalization of the Choroidal Neovascularization

Recent studies do not support the hypothesis of vascular normalization in the eyes receiving various types of intravitreous antivascular endothelial growth factor (VEGF). This retrospective study considered 57 eyes of 32 patients with vascular age-related macular degeneration (AMD) undergoing aflibercept treatment. In this study, we measured the vessel density, Horton-Strahler (HS) ramification ratio (complexity), and the length ratio in 14 eyes with choroidal neovascularization treated with 3–5 Eylea injections, 17 eyes receiving 1-2 injections, and 14 treatment-naïve eyes to the use of swept source optical coherence tomography angiography (OCTA). Macular 6 × 6 mm scans were acquired using the DRI OCT Triton by a single trained technician. OCTA images were standardized, binarized, and skeletonized using ImageJ. Then, the HS analysis of the CNV was performed. Our data suggest that the vascular density significantly decreases after an anti-VEGF injection 36 and 93 versus 41 and 87 in treatment-naïve patients. Moreover, CNV before the treatment and in a group with 3–5 injections was more complex than after receiving 1-2 injections. The branch length was not changed. Repeated anti-VEGF can lead to vascular abnormalization and further research is needed to confirm the results of this study.

Plexus-Specific Detection of Retinal Vascular Pathologic Conditions with Projection-Resolved OCT Angiography

Objective

To evaluate the projection-resolved (PR) optical coherence tomography angiography (OCTA) algorithm in detecting plexus-specific vascular abnormalities in retinal pathologies.

Design

Cross-sectional observational clinical study.

Participants

Patients diagnosed with retinal vascular diseases and healthy volunteers.

Methods

Eyes were imaged using an OCT system operating at 840 nm and employing the split-spectrum amplitude decorrelation algorithm. A novel algorithm suppressed projection artifacts inherent to OCTA. The volumetric scans were segmented and visualized on different plexuses.

Main Outcome Measures

Qualitative observation of vascular abnormalities on both cross-sectional and en face PR-OCTA images.

Results

Eight illustrative cases are reported. In cases of diabetic retinopathy, retinal vessel occlusion, and retinitis pigmentosa, PR-OCTA detected retinal nonperfusion regions within deeper retinal plexuses not visualized by conventional OCTA. In age-related macular degeneration, cross-sectional PR-OCTA permitted the classification of choroidal neovascularization, and, in a case of retinal angiomatous proliferation, identified a vertical vessel contiguous with the deep capillary plexus. In macular telangiectasia, PR-OCTA detected a diving perifoveal vein and delineated subretinal neovascularization.

Conclusions

Application of PR-OCTA promises to improve sensitive, accurate evaluation of individual vascular plexuses in multiple retinal diseases.

Tail artifact removal in OCT angiography images of rodent cortex

Optical coherence tomography angiography (OCTA) is a surging non-invasive, label-free, in vivo volumetric imaging method, currently being translated to clinical ophthalmology and becoming popular in neuroscience. Despite its attractiveness, there is an inherent issue of using OCT angiograms for quantitative cerebrovascular studies: The dynamic scattering of moving erythrocytes within pial vasculature creates tail-like artifacts that shadow the capillary vessels in the deeper layers of cortex. This false flow effect is relatively benign for qualitative visualization purposes, but it might have a significant impact on quantitative interpretation of angiographic results. In this work, we propose a simple image processing method to remove these tail artifacts in depth-resolved OCTA images using an adaptive enface mask generated with OCT structural images. We demonstrate the effectiveness of our method by comparing vessel densities and vessel similarities of depth-resolved OCT angiograms in a stroke study in a rodent model, in vivo. Thanks to the ability of seeing through the tails of pial vessels, capillary vessels beneath these vessels could be recovered to some extend in the deeper layers of mouse cerebral cortex, leading to a more accurate quantification. Tail artifact removed enface OCT angiogram of deeper layer in vivo mouse cortex.

Analyzing Relative Blood Flow Speeds in Choroidal Neovascularization Using Variable Interscan Time Analysis OCT Angiography

PURPOSE

Longitudinally visualizing relative blood flow speeds within choroidal neovascularization (CNV) may provide valuable information regarding the evolution of CNV and the response to vascular endothelial growth factor (VEGF) inhibitors.

DESIGN

Retrospective, longitudinal case series conducted at the New England Eye Center.

PARTICIPANTS

Patients with either treatment-naïve or previously treated CNV secondary to neovascular age-related macular degeneration.

METHODS

Optical coherence tomography angiography (OCTA) was performed using a 400-kHz, 1050-nm swept-source OCT system with a 5-repeat B-scan protocol. Variable interscan time analysis (VISTA) was used to compute relative flow speeds from pairs of B-scans having 1.5- and 3.0-ms separations; VISTA signals then were mapped to a color space for display.

MAIN OUTCOME MEASURES

Quantitative outcomes included OCTA-based area and volume measurements of CNV at initial and follow-up visits. Qualitative outcomes included VISTA OCTA analysis of relative blood flow speeds, along with analysis of contraction, expansion, densification, and rarefication of CNV.

RESULTS

Seven eyes of 6 patients (4 women and 2 men) with neovascular age-related macular degeneration were evaluated. Two eyes were treatment naïve at the initial visit. Choroidal neovascularization in all eyes at each visit showed relatively higher flow speeds in the trunk, central, and larger vessels and lower flow speed in the small vessels, which generally were located at the periphery of the CNV complex. Overall, the CNV appeared to expand over time despite retention of good visual acuity in all patients. In the treatment-naïve patients, slower-flow-speed vessels contracted with treatment, whereas the larger vessels with higher flow speed remained constant.

CONCLUSIONS

Variable interscan time analysis OCTA allows for longitudinal observations of relative blood flow speeds in CNV treated with anti-VEGF intravitreal injections. A common finding in this study is that the main trunk and larger vessels seem to have relatively faster blood flow speeds compared with the lesions' peripheral vasculature. Moreover, an overall growth of chronically treated CNV was seen despite retention of good visual acuity. The VISTA framework may prove useful for developing clinical end points, as well as for studying hemodynamics, disease pathogenesis, and treatment response.

Angiographic and structural imaging using high axial resolution fiber-based visible-light OCT

Optical coherence tomography using visible-light sources can increase the axial resolution without the need for broader spectral bandwidth. Here, a high-resolution, fiber-based, visible-light optical coherence tomography system is built and used to image normal retina in rats and blood vessels in chicken embryo. In the rat retina, accurate segmentation of retinal layer boundaries and quantification of layer thicknesses are accomplished. Furthermore, three distinct capillary plexuses in the retina and the choriocapillaris are identified and the characteristic pattern of the nerve fiber layer thickness in rats is revealed. In the chicken embryo model, the microvascular network and a venous bifurcation are examined and the ability to identify and segment large vessel walls is demonstrated.

Optical coherence tomography angiography: Technical principles and clinical applications in ophthalmology

Optical coherence tomography angiography (OCTA) is a functional extension of OCT that provides information on retinal and choroidal circulations without the need for dye injections. With the recent development of high-speed OCT systems and efficient algorithms, OCTA has become clinically feasible. In this review article, we discuss the technical principles of OCTA, including image processing and artifacts, and its clinical applications in ophthalmology. We summarize recent studies which qualitatively or quantitatively assess disease presentation, progression, and/or response to treatment.

Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications

OCT has revolutionized the practice of ophthalmology over the past 10-20 years. Advances in OCT technology have allowed for the creation of novel OCT-based methods. OCT-Angiography (OCTA) is one such method that has rapidly gained clinical acceptance since it was approved by the FDA in late 2016. OCTA images are based on the variable backscattering of light from the vascular and neurosensory tissue in the retina. Since the intensity and phase of backscattered light from retinal tissue varies based on the intrinsic movement of the tissue (e.g. red blood cells are moving, but neurosensory tissue is static), OCTA images are essentially motion-contrast images. This motion-contrast imaging provides reliable, high resolution, and non-invasive images of the retinal vasculature in an efficient manner. In many cases, these images are approaching histology level resolution. This unprecedented resolution coupled with the simple, fast and non-invasive imaging platform have allowed a host of basic and clinical research applications. OCTA demonstrates many important clinical findings including areas of macular telangiectasia, impaired perfusion, microaneurysms, capillary remodeling, some types of intraretinal fluid, and neovascularization among many others. More importantly, OCTA provides depth-resolved information that has never before been available. Correspondingly, OCTA has been used to evaluate a spectrum of retinal vascular diseases including diabetic retinopathy (DR), retinal venous occlusion (RVO), uveitis, retinal arterial occlusion, and age-related macular degeneration among others. In this review, we will discuss the methods used to create OCTA images, the practical applications of OCTA in light of invasive dye-imaging studies (e.g. fluorescein angiography) and review clinical studies demonstrating the utility of OCTA for research and clinical practice.

Sensitivity and Specificity of OCT Angiography to Detect Choroidal Neovascularization

PURPOSE

To determine the sensitivity and specificity of optical coherence tomography angiography (OCTA) in the detection of choroidal neovascularization (CNV) in age-related macular degeneration (AMD).

DESIGN

Prospective case series.

SUBJECTS

Prospective series of seventy-two eyes were studied, which included eyes with treatment-naive CNV due to AMD, non-neovascular AMD, and normal controls.

METHODS

All eyes underwent OCTA with a spectral domain (SD) OCT (Optovue, Inc.). The 3D angiogram was segmented into separate en face views including the inner retinal angiogram, outer retinal angiogram, and choriocapillaris angiogram. Detection of abnormal flow in the outer retina served as candidate CNV with OCTA. Masked graders reviewed structural OCT alone, en face OCTA alone, and en face OCTA combined with cross-sectional OCTA for the presence of CNV.

MAIN OUTCOME MEASURE

The sensitivity and specificity of CNV detection compared to the gold standard of fluorescein angiography (FA) and OCT was determined for structural SD-OCT alone, en face OCTA alone, and with en face OCTA combined with cross-sectional OCTA.

RESULTS

Of 32 eyes with CNV, both graders identified 26 true positives with en face OCTA alone, resulting in a sensitivity of 81.3%. Four of the 6 false negatives had large subretinal hemorrhage (SRH) and sensitivity improved to 94% for both graders if eyes with SRH were excluded. The addition of cross-sectional OCTA along with en face OCTA improved the sensitivity to 100% for both graders. Structural OCT alone also had a sensitivity of 100%. The specificity of en face OCTA alone was 92.5% for grader A and 97.5% for grader B. The specificity of structural OCT alone was 97.5% for grader A and 85% for grader B. Cross-sectional OCTA combined with en face OCTA had a specificity of 97.5% for grader A and 100% for grader B.

CONCLUSIONS

Sensitivity and specificity for CNV detection with en face OCTA combined with cross-sectional OCTA approaches that of the gold standard of FA with OCT, and it is better than en face OCTA alone. Structural OCT alone has excellent sensitivity for CNV detection. False positives from structural OCT can be mitigated with the addition of flow information with OCTA.

OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF CHOROIDAL NEOVASCULARIZATION IN FOUR INHERITED RETINAL DYSTROPHIES

PURPOSE

To demonstrate the clinical utility of optical coherence tomography (OCT) angiography (OCT-A) in inherited retinal dystrophies complicated by choroidal neovascularization (CNV).

METHODS

Optical coherence tomography angiography and structural OCT were performed using a 70-kHz spectral domain OCT system using the split-spectrum amplitude-decorrelation angiography algorithm. Semiautomated image processing software was used to segment and measure the CNV.

RESULTS

Four participants were enrolled to study the following inherited retinal dystrophies complicated by CNV: choroideremia, EFEMP1-related retinopathy, Best vitelliform dystrophy, and adult-onset vitelliform dystrophy. Interpretation of fluorescein angiography was difficult because of abnormal retinal architecture but suggested the presence of CNV. Structural OCT revealed subretinal or subretinal pigment epithelium fibrovascular tissue, within which flow signal was observed on OCT-A. The CNV morphology varied from dense capillary networks in active lesions to asymptomatic large caliber loops. Baseline CNV vessel areas ranged from 0.07 mm to 0.98 mm. After treatment with intravitreal bevacizumab, the CNV in choroideremia decreased in the vessel area then rebounded, whereas the one in EFEMP1-related retinopathy remained largely unchanged.

CONCLUSION

Optical coherence tomography angiography enables morphologic characterization and quantification of CNV in patients with retinal dystrophies despite distorted retinal architecture, can assess response to treatment, and may facilitate differentiation between active and regressed lesions.