Method for quantitative assessment of retinal vessel tortuosity in optical coherence tomography angiography applied to sickle cell retinopathy

Quantifying injury expansion in the cervical spinal cord with intravital ultrafast contrast-enhanced ultrasound imaging

Spinal cord injury is characterized by hemodynamic disruption at the injury epicenter and hypoperfusion in the penumbra, resulting in progressive ischemia and cell death. This degenerative secondary injury process has been well-described, though mostly using ex vivo or depth-limited optical imaging techniques. Intravital contrast-enhanced ultrasound enables longitudinal, quantitative evaluation of anatomical and hemodynamic changes in vivo through the entire spinal parenchyma. Here, we used ultrasound imaging to visualize and quantify subacute injury expansion (through 72 h post-injury) in a rodent cervical contusion model. Significant intraparenchymal hematoma expansion was observed through 72 h post-injury (1.86 ± 0.17-fold change from acute, p < 0.05), while the volume of the ischemic deficit largely increased within 24 h post-injury (2.24 ± 0.27-fold, p < 0.05). Histology corroborated these findings; increased apoptosis, tissue and vessel loss, and sustained tissue hypoxia were observed at 72 h post-injury. Vascular resistance was significantly elevated in the remaining perfused tissue, likely due in part to deformation of the central sulcal artery nearest to the lesion site. In conjunction, substantial hyperemia was observed in all perilesional areas examined except the ipsilesional gray matter. This study demonstrates the utility of longitudinal ultrasound imaging as a quantitative tool for tracking injury progression in vivo.

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.

An Open-source Image Analysis Toolbox for Quantitative Retinal Optical Coherence Tomography Angiography

Background

Qualitative and quantitative assessment of retinal perfusion using optical coherence tomography angiography (OCTA) has shown to be effective in the treatment and management of various retinal and optic nerve diseases. However, manual analyses of OCTA images to calculate metrics related to Foveal Avascular Zone (FAZ) morphology, and retinal vascular density and morphology are costly, time-consuming, subject to human error, and are exposed to both inter and intra operator variability.

Objective

This study aimed to develop an open-source software framework for quantitative OCTA (QOCTA). Particularly, for analyzing OCTA images and measuring several indices describing microvascular morphology, vessel morphology, and FAZ morphology.

Material and Methods

In this analytical study, we developed a toolbox or QOCTA using image processing algorithms provided in MATLAB. The software automatically determines FAZ and measures several parameters related to both size and shape of FAZ including area, perimeter, Feret's diameter circularity, axial ratio, roundness, and solidity. The microvascular structure is derived from the processed image to estimate the vessel density (VD). To assess the reliability of the software, three independent operators measured the mentioned parameters for the eyes of 21 subjects. The consistency of the values was assessed using the intraclass correlation coefficient (ICC) index.

Results

Excellent consistency was observed between the measurements completed for the superficial layer, ICC >0.9. For the deep layer, good reliability in the measurements was achieved, ICC >0.7.

Conclusion

The developed software is reliable; hence, it can facilitate quantitative OCTA, further statistical comparison in cohort OCTA studies, and can assist with obtaining deeper insights into retinal variations in various populations.

Assessment of Retinal Vessel Tortuosity Index in Patients with Fabry Disease Using Optical Coherence Tomography Angiography (OCTA)

Vessel tortuosity (VT) is a parameter used to assess retinal involvement in patients affected by systemic diseases such as Fabry disease (FD). In this study, we assessed a retinal VT index (VTI) using optical coherence tomography angiography (OCTA) in a group of patients with FD (FD cohort) compared to a healthy control group (HC cohort). This is a single-center, retrospective study analysis of all consecutive patients with genetically tested and confirmed FD who underwent regular ophthalmological visits from December 2017 to January 2020 at the Department of Ophthalmology at the University Hospital of Zurich, Switzerland. VTI was calculated for each OCTA image and the results were compared between FD and HC cohort. A total of 56 participants, 32 (male:female ratio 12:20) in the FD cohort and 24 (male:female ratio 13:11) in the HC cohort. Classic onset was determined in 18 patients. Overall, mean VTI (±SD) was 0.21 (±0.07). Male patients with classic-onset FD had a significantly higher mean VTI (0.33, SD ± 0.35) compared to all other subgroups (p-value < 0.05). Further investigations of retinal VTI in patients with FD could be helpful to use OCTA as a noninvasive screening and follow-up modality to assess disease progression in affected patients.

Homogeneous-resolution photoacoustic microscopy for ultrawide field-of-view neurovascular imaging in Alzheimer's disease

Neurovascular imaging is essential for investigating neurodegenerative diseases. However, the existing neurovascular imaging technology suffers from a trade-off between a field of view (FOV) and resolution in the whole brain, resulting in an inhomogeneous resolution and lack of information. Here, homogeneous-resolution arched-scanning photoacoustic microscopy (AS-PAM), which has an ultrawide FOV to cover the entire mouse cerebral cortex, was developed. Imaging of the neurovasculature was performed with a homogenous resolution of 6.9 µm from the superior sagittal sinus to the middle cerebral artery and caudal rhinal vein in an FOV of 12 × 12 mm². Moreover, using AS-PAM, vascular features of the meninges and cortex were quantified in early Alzheimer's disease (AD) and wild-type (WT) mice. The results demonstrated high sensitivity to the pathological progression of AD on tortuosity and branch index. The high-fidelity imaging capability in large FOV enables AS-PAM to be a promising tool for precise brain neurovascular visualization and quantification.

An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization

Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.

The RETA Benchmark for Retinal Vascular Tree Analysis

Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.

Variability of Retinal Vessel Tortuosity Measurements Using a Semiautomated Method Applied to Fundus Images in Subjects With Papilledema

Purpose

To develop methods to quantitatively measure retinal vessel tortuosity from fundus images acquired in subjects with papilledema and assess sources of variability in these measurements.

Methods

Digital fundus images from 30 eyes of subjects with untreated idiopathic intracranial hypertension and papilledema were analyzed. Retinal vein and artery tortuosities for three to four vessels of each type were measured in a region of interest 1.8 to 2.7 mm from the center of the optic nerve head. Measurements were averaged to generate a venous tortuosity index (VTI) and arterial tortuosity index (ATI) for each eye. One image of each eye was analyzed two times by the same rater. Two images of each eye, differing by focal depth, were analyzed by the same rater. Correlations between VTI and ATI for the same image and different images were calculated.

Results

Intrarater Pearson correlations (r) were 0.8 (95% confidence interval [CI], 0.59–0.9) and 0.90 (95% CI, 0.73–0.96) for VTI and ATI, respectively, with one outlier removed. Interimage r values were 0.72 (95% CI, 0.48–0.87) and 0.96 (95% CI, 0.89–0.99) for VTI and ATI, respectively, with one outlier removed. The intraclass correlation coefficients for agreement and consistency were similar, suggesting that the discrepancy between measurements was due to residual random error.

Conclusions

The finding of similar intrarater and interimage variability suggests that intrarater variability may be a more dominant source than physiology and image acquisition.

Translational Relevance

Standardizing rater procedures and averaging multiple measuring sessions are strategies to reduce variability and improve reliability of detecting retinal vessel tortuosity changes in images of eyes with papilledema.

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 .

Retinal Venular Tortuosity Jointly with Retinal Amyloid Burden Correlates with Verbal Memory Loss: A Pilot Study

Introduction: Retinal imaging is a non-invasive tool to study both retinal vasculature and neurodegeneration. In this exploratory retinal curcumin-fluorescence imaging (RFI) study, we sought to determine whether retinal vascular features combined with retinal amyloid burden correlate with the neurocognitive status. Methods: We used quantitative RFI in a cohort of patients with cognitive impairment to automatically compute retinal amyloid burden. Retinal blood vessels were segmented, and the vessel tortuosity index (VTI), inflection index, and branching angle were quantified. We assessed the correlations between retinal vascular and amyloid parameters, and cognitive domain Z-scores using linear regression models. Results: Thirty-four subjects were enrolled and twenty-nine (55% female, mean age 64 ± 6 years) were included in the combined retinal amyloid and vascular analysis. Eleven subjects had normal cognition and 18 had impaired cognition. Retinal VTI was discriminated among cognitive scores. The combined proximal mid-periphery amyloid count and venous VTI index exhibited significant differences between cognitively impaired and cognitively normal subjects (0.49 ± 1.1 vs. 0.91 ± 1.4, p = 0.006), and correlated with both the Wechsler Memory Scale-IV and SF-36 mental component score Z-scores (p < 0.05). Conclusion: This pilot study showed that retinal venular VTI combined with the proximal mid-periphery amyloid count could predict verbal memory loss. Future research is needed to finesse the clinical application of this retinal imaging-based technology.

A novel multiple subdivision-based algorithm for quantitative assessment of retinal vascular tortuosity

Vascular tortuosity as an indicator of retinal vascular morphological changes can be quantitatively analyzed and used as a biomarker for the early diagnosis of relevant disease such as diabetes. While various methods have been proposed to evaluate retinal vascular tortuosity, the main obstacle limiting their clinical application is the poor consistency compared with the experts' evaluation. In this research, we proposed to apply a multiple subdivision-based algorithm for the vessel segment vascular tortuosity analysis combining with a learning curve function of vessel curvature inflection point number, emphasizing the human assessment nature focusing not only global but also on local vascular features. Our algorithm achieved high correlation coefficients of 0.931 for arteries and 0.925 for veins compared with clinical grading of extracted retinal vessels. For the prognostic performance against experts' prediction in retinal fundus images from diabetic patients, the area under the receiver operating characteristic curve reached 0.968, indicating a good consistency with experts' predication in full retinal vascular network evaluation.

Relating Retinal Vascular Oxygen Saturation and Microvasculature Morphology at Progressive Stages of Diabetic Retinopathy

Purpose

Diabetic retinopathy (DR) is a common cause of vision loss in working age adults and presents changes in retinal vessel oxygenation and morphology. The purpose of this study was to test the hypothesis that there is an association of retinal vessel oxygen saturation with vessel density (VD) and tortuosity in DR.

Methods

Ninety-five subjects were classified in the following groups: nondiabetic control (N = 25), no DR (N = 28), mild nonproliferative DR (NPDR; N = 21), moderate to severe NPDR (N = 14), or treated proliferative DR (PDR; N = 7). Retinal oximetry was performed to measure arterial and venous oxygen saturation (SO_2A and SO_2V) and calculate oxygen extraction fraction (OEF). Optical coherence tomography angiography (OCTA) was performed for measurements of VD and vessel tortuosity index (VTI).

Results

There were statistically significant differences in SO_2A and SO_2V among groups (P< 0.004). SO_2A and SO_2V were higher in the PDR group compared to the control group and SO_2V was also higher in the moderate to severe NPDR group. VD differed significantly among groups (P = 0.003), whereas VTI was not significantly different (P = 0.22). Compared to the control group, VD was lower in moderate to severe NPDR and PDR groups. VD was also lower in the PDR group than that in the no DR group (P = 0.03). There was a significant correlation of VTI with SO_2V (r = 0.32, P = 0.002) and OEF (r = −0.35, P = 0.001).

Conclusions

Retinal vessel morphology, oxygenation, and tissue oxygen extraction were associated with each other in a cohort of subjects with and without DR.

Translational Relevance

The findings of this study have the potential to improve clinical management of DR by providing better understanding of human disease pathophysiology and propelling future studies to identify multiple image-based biomarkers for improved disease diagnosis and monitoring.

Characterization of Lenticulostriate Arteries and Its Associations With Vascular Risk Factors in Community-Dwelling Elderly

Lenticulostriate arteries (LSAs) supply blood to important subcortical areas and are, therefore, essential for maintaining the optimal functioning of the brain’s most metabolically active nuclei. Past studies have demonstrated the potential for quantifying the morphology of LSAs as biomarkers of vascular fragility or underlying arteriopathies. Thus, the current study aims to evaluate the morphological features of LSAs, their potential value in cerebrovascular risk stratification, and their concordance with other vascular risk factors in community-dwelling elderly people. A total of 125 community-dwelling elderly subjects who underwent a brain MRI scan were selected from our prospectively collected imaging database. The morphological measures of LSAs were calculated on the vascular skeletons obtained by manual tracing, and the number of LSAs was counted. Additionally, imaging biomarkers of small vessel disease were evaluated, and the diameters of major cerebral arteries were measured. The effects of vascular risk factors on LSA morphometry, as well as the relationship between LSA measures and other imaging biomarkers, were investigated. We found that smokers had shorter (p = 0.04) and straighter LSAs (p < 0.01) compared to nonsmokers, and the presence of hypertension is associated with less tortuous LSAs (p = 0.03) in community-dwelling elderly. Moreover, the middle cerebral artery diameter was positively correlated with LSA count (r = 0.278, p = 0.025) and vessel tortuosity (r = 0.257, p = 0.04). The posterior cerebral artery diameter was positively correlated with vessel tortuosity and vessel length. Considering the scarcity of noninvasive methods for measuring small artery abnormalities in the brain, the LSA morphological measures may provide valuable information to better understand cerebral small vessel degeneration during aging.

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.

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.

Retinal vascular tortuosity: Mechanisms and measurements

Retinal vessel tortuosity has been used in the diagnosis and management of different clinical situations. Notwithstanding, basic concepts, standards and tools of measurement, reliable normative data and clinical applications have many gaps or points of divergence. In this review we discuss triggering causes of retinal vessel tortuosity and resources used to assess and quantify it, as well as current limitations.

Classification of advanced and early stages of diabetic retinopathy from non-diabetic subjects by an ordinary least squares modeling method applied to OCTA images

As the prevalence of diabetic retinopathy (DR) continues to rise, there is a need to develop computer-aided screening methods. The current study reports and validates an ordinary least squares (OLS) method to model optical coherence tomography angiography (OCTA) images and derive OLS parameters for classifying proliferative DR (PDR) and no/mild non-proliferative DR (NPDR) from non-diabetic subjects. OLS parameters were correlated with vessel metrics quantified from OCTA images and were used to determine predicted probabilities of PDR, no/mild NPDR, and non-diabetics. The classification rates of PDR and no/mild NPDR from non-diabetic subjects were 94% and 91%, respectively. The method had excellent predictive ability and was validated. With further development, the method may have potential clinical utility and contribute to image-based computer-aided screening and classification of stages of DR and other ocular and systemic diseases.

Assessment of retinal vascular oxygenation and morphology at stages of diabetic retinopathy in African Americans

BACKGROUND

Diabetic retinopathy (DR) is a microvascular complication of diabetes and a leading cause of blindness in working-age adults. The likelihood of visual impairment associated with DR is two-fold higher in the African-American (AA) compared to non-Hispanic white. Although alterations in retinal vessel oxygenation and morphology have been reported in DR, there is limited knowledge about these vascular changes in AA subjects. The purpose of the current study was to investigate alterations in retinal vascular oxygen saturation (SO2), vessel diameter (D) and tortuosity at severity stages of DR in AA subjects.

METHODS

A nested case-control study of 56 AA subjects was conducted. Right eyes were grouped as non-diabetic (ND) (N = 26), no clinical DR (NDR) (N = 19), or moderate/severe non-proliferative DR (NPDR) (N = 11). Imaging was performed using a commercially available scanning laser ophthalmoscope. Images were analyzed to determine retinal arterial and venous SO2 (SO2A and SO2V), diameter (DA and DV), and vessel tortuosity index (VTI) (VTIA and VTIV).

RESULTS

SO2V and DV were higher in NPDR compared to ND and NDR groups (P < 0.05). There were no significant differences in SO2A and DA among ND, NDR, and NPDR groups (P > 0.8). Maximum VTIA was higher in diabetics (NDR and NPDR) compared to non-diabetics (P < 0.03). There was no significant difference in maximum VTIV among the 3 groups (P = 0.5).

CONCLUSIONS

The findings advance our understanding of DR pathophysiology in the AA population and may propel identification of race-specific retinal vascular biomarkers for improved diagnosis and monitoring of DR.

Quantitative optical coherence tomography angiography: A review

As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal vascular analysis have been established for objective OCTA assessment. Moreover, differential artery–vein analysis has been recently demonstrated to improve OCTA performance for objective detection and classification of eye diseases. In this review, technical rationales and clinical applications of these quantitative OCTA features are summarized, and future prospects for using these quantitative OCTA features for artificial intelligence classification of eye conditions are discussed.

Relationship between retinal vessel tortuosity and oxygenation in sickle cell retinopathy

Background

Reduced retinal vascular oxygen (O₂) content causes tissue hypoxia and may lead to development of vision-threatening pathologies. Since increased vessel tortuosity is an early sign for some hypoxia-implicated retinopathies, we investigated a relationship between retinal vascular O₂ content and vessel tortuosity indices.

Methods

Dual wavelength retinal oximetry using a commercially available scanning laser ophthalmoscope was performed in both eyes of 12 healthy (NC) and 12 sickle cell retinopathy (SCR) subjects. Images were analyzed to quantify retinal arterial and venous O₂ content and determine vessel tortuosity index (VTI) and vessel inflection index (VII) in circumpapillary regions. Linear mixed model analysis was used to determine the effect of disease on vascular O₂ content, VTI and VII, and relate vascular O₂ content with VTI and VII. Models accounted for vessel type, fellow eyes, age and mean arterial pressure.

Results

Retinal arterial and venous O₂ content were lower in SCR (O_2A = 11 ± 4 mLO₂/dL, O_2V = 7 ± 2 mLO₂/dL) compared to NC (O_2A = 18 ± 3 mLO₂/dL, O_2V = 13 ± 3 mLO₂/dL) subjects (p < 0.001). As expected, O₂ content was higher in arteries (15 ± 5 mLO₂/dL) than veins (10 ± 4 mLO₂/dL) (p < 0.001), but not different between eyes (OD: 12 ± 5 mLO₂/dL; OS:13 ± 5 mLO₂/dL) (p = 0.3). VTI was not significantly different between SCR (0.18 ± 0.07) and NC (0.15 ± 0.04) subjects, or between arteries (0.18 ± 0.07) and veins (0.16 ± 0.04), or between eyes (OD: 0.18 ± 0.07, OS:0.17 ± 0.05) (p ≥ 0.06). VII was significantly higher in SCR (10 ± 2) compared to NC subjects (8 ± 1) (p = 0.003). VII was also higher in veins (9 ± 2) compared to arteries (8 ± 5) (p = 0.04), but not different between eyes (OD: 9 ± 2; OS: 9 ± 2) (p = 0.2). There was an inverse linear relationship between vascular O₂ (13 ± 5 mLO₂/dL) content and VII (9 ± 2) (β = -0.5; p = 0.02).

Conclusions

The findings augment knowledge of relationship between retinal vascular oxygenation and morphological changes and potentially contribute to identifying biomarkers for assessment of retinal hypoxia due to SCR and other retinopathies.

Relating retinal blood flow and vessel morphology in sickle cell retinopathy

Purpose

The purpose of the current study was to determine associations between retinal blood flow and vessel morphology metrics in sickle cell retinopathy (SCR) and healthy normal control (NC) subjects.

Methods

Optical coherence tomography angiography (OCTA) and Doppler OCT imaging were performed in 12 SCR (15 eyes) and 19 NC (26 eyes) subjects. Vessel tortuosity was measured using a dedicated image analysis algorithm applied to OCTA images. Vessel density and spacing between vessels were determined from OCTA images by a fractal dimension analysis method. Retinal blood flow was quantified using a phase-resolved technique applied to en face Doppler OCT images.

Results

There was a significant association between increased retinal blood flow and increased vessel tortuosity (P = 0.03). Furthermore, increased retinal blood flow was associated with increased vessel density (P = 0.03) and decreased spacing between small vessels (P = 0.01). There was no significant association between retinal blood flow and spacing between large vessels (P = 0.11). Vessel tortuosity and blood flow were increased, whereas spacing between small vessels was decreased in SCR compared to NC group (P ≤ 0.03). There were no significant differences in vessel density or spacing between large vessels between the SCR and NC groups (P ≥ 0.31).

Conclusions

Associations between retinal hemodynamics and vessel morphology were reported, providing better understanding of retinal pathophysiology and insight into potential quantitative biomarkers to evaluate SCR.

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.