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

Rapid prototyping of a retinal multivascular network phantom for optical retinal vascular imaging equipment evaluation

Retinal vascular health holds paramount importance for healthy vision. Many technologies have been developed to examine retinal vasculature non-destructively, including fundus cameras, optical coherence tomography angiography (OCTA), fluorescein angiography (FA), and so on. However, there is a lack of a proper phantom simulating the critical features of the real human retina to calibrate and evaluate the performance of these technologies. In this work, we present a rapid, high-resolution, and economical technology based on 3D printed mold-based soft lithography and spin coating for the fabrication of a multivascular network and multilayer structural retinal phantom with the appropriate optical properties. The feasibility of the retinal phantom as a test device was demonstrated with an OCTA system and a confocal retinal ophthalmoscope. Experiment results prove that the retinal phantom could provide an objective evaluation of the OCTA and confocal retinal ophthalmoscope. Furthermore, the microfluidic phantoms enabled by this fabrication technology may support the development and evaluation of other techniques.

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network

Purpose

To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA).

Methods

This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset.

Results

The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%).

Conclusions

A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy.

Translational Relevance

Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.

Peripheral retinal lesions in diabetic retinopathy on ultra-widefield imaging

Peripheral retinal imaging plays a crucial role in the diagnosis, management, and prognosis of diabetic retinopathy (DR). Traditional fundus imaging techniques have limited coverage of the retina, resulting in missed peripheral lesions. The advent of ultra-widefield (UWF) imaging has revolutionized the assessment of the peripheral retina. UWF imaging modalities provide comprehensive visualization of the retina, enabling the detection of peripheral lesions without the need for mydriasis. Integration of UWF imaging with other modalities, including fluorescein angiography (FA), indocyanine green angiography, pseudocolor imaging, and fundus autofluorescence, further enhances our understanding of peripheral retinal lesions. UWF imaging has demonstrated improved detection of DR lesions and presumably more accurate management of DR compared to traditional fundus photography and dilated fundus examination. UWF-FA and UWF-optical coherence tomography angiography have emerged as valuable tools for assessing retinal and choroidal vascular abnormalities, nonperfusion areas, neovascularization, and microvascular abnormalities. The presence and increasing extent of predominantly peripheral lesions detected using UWF FA are associated with a higher risk of DR progression and proliferative DR. UWF imaging provides a comprehensive evaluation of DR severity, aiding in more accurate risk stratification and treatment decision-making. Overall, UWF imaging modalities have significantly advanced our understanding of peripheral retinal lesions in DR, facilitating early detection and targeted management for better visual outcomes.

Visualizing features with wide-field volumetric OCT angiography

Optical coherence tomography (OCT) and its extension OCT angiography (OCTA) have become essential clinical imaging modalities due to their ability to provide depth-resolved angiographic and tissue structural information non-invasively and at high resolution. Within a field of view, the anatomic detail available is sufficient to identify several structural and vascular pathologies that are clinically relevant for multiple prevalent blinding diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and vein occlusions. The main limitation in contemporary OCT devices is that this field of view is limited due to a fundamental trade-off between system resolution/sensitivity, sampling density, and imaging window dimensions. Here, we describe a swept-source OCT device that can capture up to a 12 × 23-mm field of view in a single shot and show that it can identify conventional pathologic features such as non-perfusion areas outside of conventional fields of view. We also show that our approach maintains sensitivity sufficient to visualize novel features, including choriocapillaris morphology beneath the macula and macrophage-like cells at the inner limiting membrane, both of which may have implications for disease.

Single-shot OCT and OCT angiography for slab-specific detection of diabetic retinopathy

In this study, we present an optical coherence tomographic angiography (OCTA) prototype using a 500 kHz high-speed swept-source laser. This system can generate a 75-degree field of view with a 10.4 µm lateral resolution with a single acquisition. With this prototype we acquired detailed, wide-field, and plexus-specific images throughout the retina and choroid in eyes with diabetic retinopathy, detecting early retinal neovascularization and locating pathology within specific retinal slabs. Our device could also visualize choroidal flow and identify signs of key biomarkers in diabetic retinopathy.

Utility of En Face OCT for the Detection of Clinically Unsuspected Retinal Neovascularization in Patients with Diabetic Retinopathy

PURPOSE

To assess the value of en face OCT for detecting clinically unsuspected retinal neovascularization (RNV) in patients with nonproliferative diabetic retinopathy (NPDR).

DESIGN

A retrospective, cross-sectional study.

PARTICIPANTS

Treatment-naïve patients clinically graded as NPDR in an ongoing prospective observational OCT angiography (OCTA) study at a tertiary care center.

METHODS

Each patient underwent imaging of 1 eye with a spectral-domain OCTA, generating a 17 × 17-mm widefield image by montaging four 9 × 9-mm scans. Two independent graders examined a combination of en face OCT, en face OCTA with a custom vitreoretinal interface slab, and cross-sectional OCTA to determine the presence of RNV. We measured the area of RNV flow within RNV lesions on en face OCTA.

MAIN OUTCOME MEASURES

Detection rate of clinically occult RNV with OCT and OCTA.

RESULTS

Of 63 enrolled eyes, 27 (43%) were clinically graded as severe NPDR, 16 (25%) as moderate NPDR, and 20 (32%) as mild NPDR. Using the combination of en face OCT, en face OCTA, and cross-sectional OCTA, the graders detected 42 RNV lesions in 12 (19%) eyes, of which 8 (67%) were graded as severe NPDR, 2 (17%) as moderate NPDR, and 2 (17%) as mild NPDR. The sensitivity of en face OCT alone for detecting eyes with RNV was similar to that of en face OCTA alone (100% vs. 92%; P = 0.32), whereas the specificity of en face OCT alone was significantly lower than that of en face OCTA alone (32% vs. 73%; P < 0.001). For detecting individual RNV lesions, the en face OCT was 100% sensitive, compared with 67% sensitivity for the en face OCTA (P < 0.001). The area of RNV lesions that manual grading with en face OCTA alone missed was significantly smaller than that of manually detectable RNV (Mean [standard deviation] RNV flow area, 0.015 [0.020] mm2 vs. 0.16 [0.36] mm2; P < 0.001).

CONCLUSION

The combination of en face OCT and OCTA can detect clinically occult RNV with high sensitivity. For screening these small lesions, en face OCT may be a useful imaging modality.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Functional OCT angiography reveals early retinal neurovascular dysfunction in diabetes with capillary resolution

Altered retinal neurovascular coupling may contribute to the development and progression of diabetic retinopathy (DR) but remains highly challenging to measure due to limited resolution and field of view of the existing functional hyperemia imaging. Here, we present a novel modality of functional OCT angiography (fOCTA) that allows a 3D imaging of retinal functional hyperemia across the entire vascular tree with single-capillary resolution. In fOCTA, functional hyperemia was evoked by a flicker light stimulation, recorded by a synchronized time-lapse OCTA (i.e., 4D), and extracted precisely from each capillary segment (space) and stimulation period (time) in the OCTA time series. The high-resolution fOCTA revealed that the retinal capillaries, particularly the intermediate capillary plexus, exhibited apparent hyperemic response in normal mice, and significant functional hyperemia loss (P < 0.001) at an early stage of DR with few overt signs of retinopathy and visible restoration after aminoguanidine treatment (P < 0.05). Retinal capillary functional hyperemia has strong potential to provide sensitive biomarkers of early DR, and retinal fOCTA would provide new insights into the pathophysiology, screening and treatment of early DR.

Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification

Optical coherence tomography (OCT) and OCT angiography (OCT-A) may benefit the screening of diabetic retinopathy (DR). This study investigated the effect of laterally subsampling OCT/OCT-A en face scans by up to a factor of 8 when using deep neural networks for automated referable DR classification. There was no significant difference in the classification performance across all evaluation metrics when subsampling up to a factor of 3, and only minimal differences up to a factor of 8. Our findings suggest that OCT/OCT-A can reduce the number of samples (and hence the acquisition time) for a volume for a given field of view on the retina that is acquired for rDR classification.

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.

Quantification of Nonperfusion Area in Montaged Widefield OCT Angiography Using Deep Learning in Diabetic Retinopathy

Purpose

To examine the efficacy of a deep learning-based algorithm to quantify the nonperfusion area (NPA) on montaged widefield OCT angiography (OCTA) for assessment of diabetic retinopathy (DR) severity.

Design

Cross-sectional study.

Participants

One hundred thirty-seven participants with a full range of DR severity and 26 healthy participants.

Methods

A deep learning-based algorithm was developed for detecting and quantifying NPA in the superficial vascular complex on widefield OCTA comprising 3 horizontally montaged 6 × 6-mm OCTA scans from the nasal, macular, and temporal regions. We trained the algorithm on 978 volumetric OCTA scans from all participants using 5-fold cross-validation. The algorithm can distinguish NPA from shadow artifacts. The F1 score evaluated segmentation accuracy. The area under the receiver operating characteristic curve and sensitivity with specificity fixed at 95% quantified network performance to distinguish patients with diabetes from healthy control participants, referable DR from nonreferable DR (nonproliferative DR [NPDR] less than moderate severity), and severe DR (severe NPDR, proliferative DR, or DR with edema) from nonsevere DR (mild to moderate NPDR).

Main Outcome Measures

Widefield OCTA NPA, visual acuity (VA), and DR severities.

Results

Automatically segmented NPA showed high agreement with the manually delineated ground truth, with a mean ± standard deviation F1 score of 0.78 ± 0.05 in nasal, 0.82 ± 0.07 in macular, and 0.78 ± 0.05 in temporal scans. The extrafoveal avascular area (EAA) in the macular scan showed the best sensitivity at 54% for differentiating those with diabetes from healthy control participants, whereas montaged widefield OCTA scan showed significantly higher sensitivity than macular scans (P < 0.0001, McNemar’s test) for detecting eyes with DR at 66%, referable DR at 63%, and severe DR at 62%. Montaged widefield OCTA showed the highest correlation (Spearman ρ = 0.74; P < 0.0001) between EAA and DR severity. The macular scan showed the strongest negative correlation (Pearson ρ = –0.42; P < 0.0001) between EAA and best-corrected VA.

Conclusions

A deep learning-based algorithm for montaged widefield OCTA can detect NPA accurately and can improve the detection of clinically important DR.

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.

Optical Coherence Tomography Angiography Avascular Area Association With 1-Year Treatment Requirement and Disease Progression in Diabetic Retinopathy

PURPOSE

To assess the association between optical coherence tomography angiography (OCTA)-quantified avascular areas (AAs) and diabetic retinopathy (DR) severity, progression, and treatment requirement in the following year.

DESIGN

Prospective cohort study.

METHODS

We recruited patients with diabetes from a tertiary academic retina practice and obtained 3-mm × 3-mm macular OCTA scans with the AngioVue system and standard 7-field color photographs at baseline and at a 1-year follow-up visit. A masked grader determined the severity of DR from the color photographs using the Early Treatment of Diabetic Retinopathy scale. A custom algorithm detected extrafoveal AA (EAA) excluding the central 1-mm circle in projection-resolved superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP).

RESULTS

Of 138 patients, 92 (41 men, ranging in age from 26-84 years [mean 59.4 years]) completed 1 year of follow-up. At baseline, EAAs for SVC, ICP, and DCP were all significantly correlated with retinopathy severity (P < .0001). DCP EAA was significantly associated with worse visual acuity (r = -0.24, P = .02), but SVC and ICP EAA were not. At 1 year, 11 eyes progressed in severity by at least 1 step. Multivariate logistic regression analysis demonstrated the progression was significantly associated with baseline SVC EAA (odds ratio = 8.73, P = .04). During the follow-up period, 33 eyes underwent treatment. Multivariate analysis showed that treatment requirement was significantly associated with baseline DCP EAA (odds ratio = 3.39, P = .002). No baseline metric was associated with vision loss at 1 year.

CONCLUSIONS

EAAs detected by OCTA in diabetic eyes are significantly associated with baseline DR severity, disease progression, and treatment requirement over 1 year.

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.

Automatic Visual Acuity Estimation by Means of Computational Vascularity Biomarkers Using Oct Angiographies

Optical Coherence Tomography Angiography (OCTA) constitutes a new non-invasive ophthalmic image modality that allows the precise visualization of the micro-retinal vascularity that is commonly used to analyze the foveal region. Given that there are many systemic and eye diseases that affect the eye fundus and its vascularity, the analysis of that region is crucial to diagnose and estimate the vision loss. The Visual Acuity (VA) is typically measured manually, implying an exhaustive and time-consuming procedure. In this work, we propose a method that exploits the information of the OCTA images to automatically estimate the VA with an accurate error of 0.1713.

Progress in Multimodal En Face Imaging: feature introduction

This feature issue contains papers that report on the most recent advances in the field of en face optical coherence tomography (OCT) and of combinations of modalities facilitated by the en face view. Hardware configurations for delivery of en face OCT images are described as well as specific signal and image processing techniques tailored to deliver relevant clinical diagnoses. The value of the en face perspective for enabling multimodality is illustrated by several combination modalities.