OCTA Derived Vessel Skeleton Density Versus Flux and Their Associations With Systemic Determinants of Health

Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk

Purpose

We aim to use fundus fluorescein angiography (FFA) to label the capillaries on color fundus (CF) photographs and train a deep learning model to quantify retinal capillaries noninvasively from CF and apply it to cardiovascular disease (CVD) risk assessment.

Design

Cross-sectional and longitudinal study.

Participants

A total of 90732 pairs of CF-FFA images from 3893 participants for segmentation model development, and 49229 participants in the UK Biobank for association analysis.

Methods

We matched the vessels extracted from FFA and CF, and used vessels from FFA as labels to train a deep learning model (RMHAS-FA) to segment retinal capillaries using CF. We tested the model's accuracy on a manually labeled internal test set (FundusCapi). For external validation, we tested the segmentation model on 7 vessel segmentation datasets, and investigated the clinical value of the segmented vessels in predicting CVD events in the UK Biobank.

Main Outcome Measures

Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity for segmentation. Hazard ratio (HR; 95% confidence interval [CI]) for Cox regression analysis.

Results

On the FundusCapi dataset, the segmentation performance was AUC = 0.95, accuracy = 0.94, sensitivity = 0.90, and specificity = 0.93. Smaller vessel skeleton density had a stronger correlation with CVD risk factors and incidence (P < 0.01). Reduced density of small vessel skeletons was strongly associated with an increased risk of CVD incidence and mortality for women (HR [95% CI] = 0.91 [0.84-0.98] and 0.68 [0.54-0.86], respectively).

Conclusions

Using paired CF-FFA images, we automated the laborious manual labeling process and enabled noninvasive capillary quantification from CF, supporting its potential as a sensitive screening method for identifying individuals at high risk of future CVD events.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dimension-based quantification of aging-associated cerebral microvasculature determined by optical coherence tomography and two-photon microscopy

Cerebral microvascular health is a key biomarker for the study of natural aging and associated neurological diseases. Our aim is to quantify aging-associated change of microvasculature at diverse dimensions in mice brain. We used optical coherence tomography (OCT) and two-photon microscopy (TPM) to obtain nonaged and aged C57BL/6J mice cerebral microvascular images in vivo. Our results indicated that artery & vein, arteriole & venule, and capillary from nonaged and aged mice showed significant differences in density, diameter, complexity, perimeter, and tortuosity. OCT angiography and TPM provided the comprehensive quantification for arteriole and venule via compensating the limitation of each modality alone. We further demonstrated that arteriole and venule at specific dimensions exhibited negative correlations in most quantification analyses between nonaged and aged mice, which indicated that TPM and OCT were able to offer complementary vascular information to study the change of cerebral blood vessels in aging.

Retinal perfusion is linked to cognition and brain MRI biomarkers in Black Americans

INTRODUCTION

We investigated whether retinal capillary perfusion is a biomarker of cerebral small vessel disease and impaired cognition among Black Americans, an understudied group at higher risk for dementia.

METHODS

We enrolled 96 Black Americans without known cognitive impairment. Four retinal perfusion measures were derived using optical coherence tomography angiography. Neurocognitive assessment and brain magnetic resonance imaging (MRI) were performed. Multiple linear regression analyses were performed.

RESULTS

Lower retinal capillary perfusion was correlated with worse Oral Symbol Digit Test (P < = 0.005) and Fluid Cognition Composite scores (P < = 0.02), but not with the Crystallized Cognition Composite score (P > = 0.41). Lower retinal perfusion was also correlated with higher free water and peak width of skeletonized mean diffusivity, and lower fractional anisotropy (all P < 0.05) on MRI (N = 35).

DISCUSSION

Lower retinal capillary perfusion is associated with worse information processing, fluid cognition, and MRI biomarkers of cerebral small vessel disease, but is not related to crystallized cognition.

Reduction in retinal microvascular perfusion during migraine attacks

OBJECTIVE

To determine if there are changes in structure and function of the retinal vasculature during and between migraine attacks using optical coherence tomography angiography (OCTA).

BACKGROUND

Migraine attacks commonly include visual symptoms, but the potential role of the retina in these symptoms is not well understood. OCTA is a rapid, non-invasive imaging technique that is used to visualize the retinal microvasculature with high spatial resolution in a clinical setting. In this study we used OCTA to quantify different features of the retinal vasculature in patients with migraine during and between attacks, as well as in healthy controls (HCs).

METHODS

We performed a prospective cohort study of 37 patients with migraine with aura (MA) (median [interquartile range, IQR] age of 37 [14] years, 86% female) and 30 with migraine without aura (MO) (median [IQR] age of 37 [17] years, 77% female) and 20 HCs (median [IQR] age of 35 [7] years, 50% female). Macular OCTA scans were obtained for all participants for the interictal analysis. In 12 MA and eight MO, scans were captured both during and outside of migraine attacks and five HCs had initial and repeat scans. In addition to analyzing the morphology of the foveal avascular zone, we calculated the vessel flux index (VFI), which is an indicator of retinal perfusion and conventional metrics (such as vessel area density) in the foveal and parafoveal regions.

RESULTS

There was a significant difference in the parafoveal VFI in the ictal state between the groups (p = 0.009). During migraine attacks there was a significant reduction in the parafoveal region VFI in MA (-7%, 95% confidence interval [CI] -10% to -4%; p = 0.006) and MO (-7%, 95% CI -10% to -3%; p = 0.016) from their interictal baseline as compared to the change between repeat scans in HCs (2%, 95% CI -3% to 7%). Interictally, there was a mean (standard deviation [SD]) 13% (10%) (p = 0.003) lower blood perfusion in the MA group as compared to the MO group in the foveal region (mean [SD] 0.093 [0.023] vs. 0.107 [0.021], p = 0.003). Interictal analysis also revealed higher circularity in the superficial foveal avascular zone in the MA group compared with the MO group (mean [SD] 0.686 [0.088] vs. 0.629 [0.120], p = 0.004). In addition, interictal analysis of the patients with MA or MO and unilateral headache showed increased retinal vascular parameters consistent with greater perfusion in the eye ipsilateral to the side of the pain as compared with the contralateral eye.

CONCLUSIONS

These results indicate that perfusion is reduced in MA and MO in the parafoveal retina during the ictal period. Interictally, the foveal retina in MA has reduced perfusion when compared to the foveal retina in MO. Patients with unilateral headache showed interictal asymmetry of retinal perfusion between eyes. These results indicate that changes in retinal perfusion could be a part of migraine pathophysiology, and that distinct retinal vascular signatures identified with OCTA could represent biomarkers for migraine.

OCT angiography and its retinal biomarkers [Invited]

Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.

Deep learning for artery-vein classification in optical coherence tomography angiography

Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retinal vascular details. Optical coherence tomography (OCT) can provide improved resolution for retinal imaging. However, it cannot discern capillary-level structures due to the limited image contrast. As a functional extension of OCT modality, optical coherence tomography angiography (OCTA) is a non-invasive, label-free method for enhanced contrast visualization of retinal vasculatures at the capillary level. Recently differential artery-vein (AV) analysis in OCTA has been demonstrated to improve the sensitivity for staging of retinopathies. Therefore, AV classification is an essential step for disease detection and diagnosis. However, current methods for AV classification in OCTA have employed multiple imagers, that is, fundus photography and OCT, and complex algorithms, thereby making it difficult for clinical deployment. On the contrary, deep learning (DL) algorithms may be able to reduce computational complexity and automate AV classification. In this article, we summarize traditional AV classification methods, recent DL methods for AV classification in OCTA, and discuss methods for interpretability in DL models.

An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography

BACKGROUND

Differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity.

METHOD

A deep learning network AVA-Net has been developed for automated AV area (AVA) segmentation in OCTA. Seven new OCTA features, including arterial area (AA), venous area (VA), AVA ratio (AVAR), total perfusion intensity density (T-PID), arterial PID (A-PID), venous PID (V-PID), and arterial-venous PID ratio (AV-PIDR), were extracted and tested for early detection of diabetic retinopathy (DR). Each of these seven features was evaluated for quantitative evaluation of OCTA images from healthy controls, diabetic patients without DR (NoDR), and mild DR.

RESULTS

It was observed that the area features, i.e., AA, VA and AVAR, can reveal significant differences between the control and mild DR. Vascular perfusion parameters, including T-PID and A-PID, can differentiate mild DR from control group. AV-PIDR can disclose significant differences among all three groups, i.e., control, NoDR, and mild DR. According to Bonferroni correction, the combination of A-PID and AV-PIDR can reveal significant differences in all three groups.

CONCLUSIONS

AVA-Net, which is available on GitHub for open access, enables quantitative AV analysis of AV area and vascular perfusion intensity. Comparative analysis revealed AV-PIDR as the most sensitive feature for OCTA detection of early DR. Ensemble AV feature analysis, e.g., the combination of A-PID and AV-PIDR, can further improve the performance for early DR assessment.

Normalized Blood Flow Index in Optical Coherence Tomography Angiography Provides a Sensitive Biomarker of Early Diabetic Retinopathy

Purpose

To evaluate the sensitivity of normalized blood flow index (NBFI) for detecting early diabetic retinopathy (DR).

Methods

Optical coherence tomography angiography (OCTA) images of healthy controls, diabetic patients without DR (NoDR), and patients with mild nonproliferative DR (NPDR) were analyzed in this study. The OCTA images were centered on the fovea and covered a 6 mm × 6 mm area. Enface projections of the superficial vascular plexus (SVP) and the deep capillary plexus (DCP) were obtained for the quantitative OCTA feature analysis. Three quantitative OCTA features were examined: blood vessel density (BVD), blood flow flux (BFF), and NBFI. Each feature was calculated from both the SVP and DCP and their sensitivities to distinguish the three cohorts of the study were evaluated.

Results

The only quantitative feature capable of distinguishing all three cohorts was NBFI in the DCP image. Comparative study revealed that both BVD and BFF were able to distinguish the controls and NoDR from mild NPDR. However, neither BVD nor BFF was sensitive enough to separate NoDR from the healthy controls.

Conclusions

The NBFI has been demonstrated as a sensitive biomarker of early DR, revealing retinal blood flow abnormality better than traditional BVD and BFF. The NBFI in the DCP was verified as the most sensitive biomarker, supporting that diabetes affects the DCP earlier than SVP in DR.

Translational Relevance

NBFI provides a robust biomarker for quantitative analysis of DR-caused blood flow abnormalities, promising early detection and objective classification of DR.

Retinal Microvascular Changes in Familial Hypercholesterolemia: Analysis with Swept-Source Optical Coherence Tomography Angiography

Familial hypercholesterolemia (FH) is a common but underdiagnosed genetic disorder affecting cholesterol metabolism, leading to atherosclerotic disease. The relationship between retinal microvascular changes and the presence of atheroma in patients with FH (FH group), and in comparison to volunteers without FH (CT group), needs further investigation. This cross-sectional study was conducted in a university hospital between October 1, 2020 and May 31, 2021. Cardiovascular data, including the Coronary Artery Calcium (CAC) score, were recorded for FH patients. Macula angiograms were acquired using swept-source optical coherence tomography angiography (SS OCT-A) to analyze both the superficial capillary plexus (SCP) and deep capillary plexus (DCP). A total of 162 eyes of 83 patients were enrolled in the FH group and 121 eyes of 78 volunteers in the CT group. A statistically significant association was found between the CAC score and both vessel density (β = −0.002 [95% CI, −0.004; −0.0005], p = 0.010) and vessel length (β = −0.00005 [95% CI, −0.00008; −0.00001], p = 0.010) in the DCP. The FH group had a significantly lower foveal avascular zone circularity index than the CT group in multivariate analysis (0.67 ± 0.16 in the FH group vs. 0.72 ± 0.10 in the CT group, β = 0.04 [95% CI, 0.002; 0.07], p = 0.037). Retinal microvascularization is altered in FH and retinal vascular densities are modified according to the CAC score.