Glaucoma detection using non-perfused areas in OCTA.

Multiple ophthalmic diseases lead to decreased capillary perfusion that can be visualized using optical coherence tomography angiography images. To quantify the decrease in perfusion, past studies have often used the vessel density, which is the percentage of vessel pixels in the image. However, this method is often not sensitive enough to detect subtle changes in early pathology. More recent methods are based on quantifying non-perfused or intercapillary areas between the vessels. These methods rely upon the accuracy of vessel segmentation, which is a challenging task and therefore a limiting factor for reliability. Intercapillary areas computed from perfusion-distance measures are less sensitive to errors in the vessel segmentation since the distance to the next vessel is only slightly changing if gaps are present in the segmentation. We present a novel method for distinguishing between glaucoma patients and healthy controls based on features computed from the probability density function of these perfusion-distance areas. The proposed approach is evaluated on different capillary plexuses and outperforms previously proposed methods that use handcrafted features for classification. Moreover the results of the proposed method are in the same range as the ones of convolutional neural networks trained on the raw input images and is therefore a computationally efficient, simple to implement and explainable alternative to deep learning-based approaches.

SSN2V: unsupervised OCT denoising using speckle split.

Denoising in optical coherence tomography (OCT) is important to compensate the low signal-to-noise ratio originating from laser speckle. In recent years learning algorithms have been established as the most powerful denoising approach. Especially unsupervised denoising is an interesting topic since it is not possible to acquire noise free scans with OCT. However, speckle in in-vivo OCT images contains not only noise but also information about blood flow. Existing OCT denoising algorithms treat all speckle equally and do not distinguish between the noise component and the flow information component of speckle. Consequently they either tend to either remove all speckle or denoise insufficiently. Unsupervised denoising methods tend to remove all speckle but create results that have a blurry impression which is not desired in a clinical application. To this end we propose the concept, that an OCT denoising method should, besides reducing uninformative noise, additionally preserve the flow-related speckle information. In this work, we present a fully unsupervised algorithm for single-frame OCT denoising (SSN2V) that fulfills these goals by incorporating known operators into our network. This additional constraint greatly improves the denoising capability compared to a network without. Quantitative and qualitative results show that the proposed method can effectively reduce the speckle noise in OCT B-scans of the human retina while maintaining a sharp impression outperforming the compared methods.

Retinal blood flow speed quantification at the capillary level using temporal autocorrelation fitting OCTA [Invited].

Optical coherence tomography angiography (OCTA) can visualize vasculature structures, but provides limited information about blood flow speed. Here, we present a second generation variable interscan time analysis (VISTA) OCTA, which evaluates a quantitative surrogate marker for blood flow speed in vasculature. At the capillary level, spatially compiled OCTA and a simple temporal autocorrelation model, ρ(τ) = exp(-ατ), were used to evaluate a temporal autocorrelation decay constant, α, as the blood flow speed marker. A 600 kHz A-scan rate swept-source OCT prototype instrument provides short interscan time OCTA and fine A-scan spacing acquisition, while maintaining multi mm2 field of views for human retinal imaging. We demonstrate the cardiac pulsatility and assess repeatability of α measured with VISTA. We show different α for different retinal capillary plexuses in healthy eyes and present representative VISTA OCTA in eyes with diabetic retinopathy.

Trainable joint bilateral filters for enhanced prediction stability in low-dose CT.

Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning (DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline's ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding RED-CNN/QAE, two well-established DL-based denoisers in our pipeline, the denoising performance is improved by 10%/82% (RMSE) and 3%/81% (PSNR) in regions containing metal and by 6%/78% (RMSE) and 2%/4% (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines.

Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography.

BACKGROUND: Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. PURPOSE: Most data-driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. METHODS: This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. RESULTS: Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal-to-noise ratio values of 33.17 and 43.07 on the respective data sets. CONCLUSIONS: Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.

Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising.

Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic OCTA signal models and maximum likelihood estimates. Reconstruction results using total variation minimization and wavelet shrinkage for regularization were compared against an OCTA ground truth volume, merged from six co-registered single OCTA volumes. The results show a significant improvement in peak signal-to-noise ratio and structural similarity. The presented algorithm brings together OCTA image generation and Bayesian statistics and can be developed into new OCTA image generation and denoising algorithms.

Efficient and high accuracy 3-D OCT angiography motion correction in pathology.

We describe a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes. This is the first approach that aligns predominantly axial structural features such as retinal layers as well as transverse angiographic vascular features in a joint optimization. Combined with orthogonal scanning and favorization of kinematically more plausible displacements, subpixel alignment and micrometer-scale distortion correction is achieved in all 3 dimensions. As no specific structures are segmented, the method is by design robust to pathologic changes. Furthermore, the method is designed for highly parallel implementation and short runtime, allowing its integration into clinical workflow even for high density or wide-field scans. We evaluated the algorithm with metrics related to clinically relevant features in an extensive quantitative evaluation based on 204 volumetric scans of 17 subjects, including patients with diverse pathologies and healthy controls. Using this method, we achieve state-of-the-art axial motion correction and show significant advances in both transverse co-alignment and distortion correction, especially in the subgroup with pathology.

OCT-OCTA segmentation: combining structural and blood flow information to segment Bruch's membrane.

In this paper we present a fully automated graph-based segmentation algorithm that jointly uses optical coherence tomography (OCT) and OCT angiography (OCTA) data to segment Bruch's membrane (BM). This is especially valuable in cases where the spatial correlation between BM, which is usually not visible on OCT scans, and the retinal pigment epithelium (RPE), which is often used as a surrogate for segmenting BM, is distorted by pathology. We validated the performance of our proposed algorithm against manual segmentation in a total of 18 eyes from healthy controls and patients with diabetic retinopathy (DR), non-exudative age-related macular degeneration (AMD) (early/intermediate AMD, nascent geographic atrophy (nGA) and drusen-associated geographic atrophy (DAGA) and geographic atrophy (GA)), and choroidal neovascularization (CNV) with a mean absolute error of ∼0.91 pixel (∼4.1 µm). This paper suggests that OCT-OCTA segmentation may be a useful framework to complement the growing usage of OCTA in ophthalmic research and clinical communities.

Glaucoma classification in 3 x 3 mm en face macular scans using deep learning in a different plexus.

Glaucoma is among the leading causes of irreversible blindness worldwide. If diagnosed and treated early enough, the disease progression can be stopped or slowed down. Therefore, it would be very valuable to detect early stages of glaucoma, which are mostly asymptomatic, by broad screening. This study examines different computational features that can be automatically deduced from images and their performance on the classification task of differentiating glaucoma patients and healthy controls. Data used for this study are 3 x 3 mm en face optical coherence tomography angiography (OCTA) images of different retinal projections (of the whole retina, the superficial vascular plexus (SVP), the intermediate capillary plexus (ICP) and the deep capillary plexus (DCP)) centered around the fovea. Our results show quantitatively that the automatically extracted features from convolutional neural networks (CNNs) perform similarly well or better than handcrafted ones when used to distinguish glaucoma patients from healthy controls. On the whole retina projection and the SVP projection, CNNs outperform the handcrafted features presented in the literature. Area under receiver operating characteristics (AUROC) on the SVP projection is 0.967, which is comparable to the best reported values in the literature. This is achieved despite using the small 3 × 3 mm field of view, which has been reported as disadvantageous for handcrafted vessel density features in previous works. A detailed analysis of our CNN method, using attention maps, suggests that this performance increase can be partially explained by the CNN automatically relying more on areas of higher relevance for feature extraction.

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

PURPOSE: To develop an optical coherence tomography angiography (OCTA)-based framework for quantitatively analyzing the spatial distribution of choriocapillaris (CC) impairment around choroidal neovascularization (CNV) secondary to age-related macular degeneration. METHODS: In a retrospective, cross-sectional study, 400-kHz swept-source OCTA images from 7 eyes of 6 patients with CNV secondary to age-related macular degeneration were quantitatively analyzed using custom software. A lesion-centered zonal OCTA analysis technique-which portioned the field-of-view into zones relative to CNV boundaries-was developed to quantify the spatial dependence of CC flow deficits. RESULTS: Quantitative, lesion-centered zonal analysis of CC OCTA images revealed highest flow-deficit percentages near CNV boundaries, decreasing in zones farther from the boundaries. Optical coherence tomography angiography using shorter (1.5 ms) interscan times revealed more severe flow deficits than OCTA using longer (3.0 ms) interscan times; however, spatial trends were similar for both interscan times. A detailed description of the OCTA processing steps and parameters was provided so as to elucidate their influence on quantitative measurements. CONCLUSION: Impairment of the CC, assessed by flow-deficit percentages, was most prominent closest to CNV boundaries. The lesion-centered zonal analysis technique enabled quantitative CC measurements relative to focal lesions. Understanding how processing steps, imaging/processing parameters, and artifacts can affect quantitative CC measurements is important for longitudinal, OCTA-based studies of disease progression, and treatment response.

A Framework for Multiscale Quantitation of Relationships Between Choriocapillaris Flow Impairment and Geographic Atrophy Growth.

PURPOSE: To develop a multiscale analysis framework for investigating the relationships between geographic atrophy (GA) growth rate and choriocapillaris (CC) blood flow impairment using optical coherence tomography (OCT) and OCT angiography (OCTA). DESIGN: Retrospective case series. METHODS: We developed an OCT/OCTA analysis framework that quantitatively measures GA growth rates at global and local scales and CC impairment at global, zonal, and local scales. A geometric GA growth model was used to measure local GA growth rates. The utility of the framework was demonstrated on 7 eyes with GA imaged at 2 time points using a prototype 400-kHz, 1050-nm swept-source OCTA system. RESULTS: Qualitatively, there was a trend of increasing GA growth rate with increasing CC impairment. The local analysis model enabled growth rates to be estimated at each point on the GA boundary. However, there was no generally observed trend between local GA growth rates and local CC impairment. CONCLUSIONS: The global, zonal, and local analysis framework may be useful for investigating relationships between GA growth and CC impairment at different spatial scales. The geometric GA growth model enables spatially resolved growth measurements that capture the anisotropy of GA growth and may improve the characterization of GA progression.

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

PURPOSE: To map and measure the depths of corneal neovascularization (NV) using 3-dimensional optical coherence tomography angiography (OCTA) at 2 different wavelengths. METHODS: Corneal NV of varying severity, distribution, and underlying etiology was examined. Average NV depth and vessel density were measured using 840-nm spectral-domain OCTA and 1050-nm swept-source OCTA. The OCTA results were compared with clinical slit-lamp estimation of NV depth. RESULTS: Twelve eyes with corneal NV from 12 patients were imaged with OCTA. Clinically "superficial," "midstromal," and "deep" cases had an average vessel depth of 23%, 39%, and 66% on 1050-nm OCTA, respectively. Average vessel depth on OCTA followed a statistically significant ordinal trend according to the clinical classification of vessel depth (Jonckheere-Terpstra test, P < 0.001). In 8 cases where both 840-nm OCTA and 1050-nm OCTA were acquired, there was excellent agreement in the mean vessel depth between the 2 systems (concordance correlation coefficient = 0.94, P < 0.001). The average vessel density measured by 840-nm OCTA was higher (average 1.6-fold) than that measured by 1050-nm OCTA. CONCLUSIONS: Corneal OCTA was able to map corneal NV in 3 dimensions and measure vessel depth and density. The depth of corneal NV varied between different pathologies in a manner consistent with previous pathologic studies. The measured vessel density appeared to be affected by the interscan time, which affects blood flow velocity sensitivity, and the wavelength, which affects the ability to penetrate through opacity. These findings suggest possible clinical applications of OCTA for the diagnosis of corneal pathology and quantitative monitoring of therapeutic response in patients with corneal NV.

Technical Note: PYRO-NN: Python reconstruction operators in neural networks.

PURPOSE: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches use workarounds for mathematically unambiguously solvable problems. METHODS: PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan-, and cone-beam projectors, and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high-level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. RESULTS: The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high-level Python API allows a simple use of the layers as known from Tensorflow. All algorithms and tools are referenced to a scientific publication and are compared to existing non-deep learning reconstruction frameworks. To demonstrate the capabilities of the layers, the framework comes with baseline experiments, which are described in the supplementary material. The framework is available as open-source software under the Apache 2.0 licence at https://github.com/csyben/PYRO-NN. CONCLUSIONS: PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step toward reproducible research and give the medical physics community a toolkit to elevate medical image reconstruction with new deep learning techniques.

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.

Polypoidal Choroidal Vasculopathy on Swept-Source Optical Coherence Tomography Angiography with Variable Interscan Time Analysis.

PURPOSE: To use a novel optical coherence tomography angiography (OCTA) algorithm termed variable interscan time analysis (VISTA) to evaluate relative blood flow speeds in polypoidal choroidal vasculopathy (PCV). METHODS: Prospective cross-sectional study enrolling patients with confirmed diagnosis of PCV. OCTA of the retina and choroid was obtained with a prototype swept-source OCT system. The acquired OCT volumes were centered on the branching vascular network (BVN) and polyps as determined by indocyanine-green angiography (ICGA). The relative blood flow speeds were characterized on VISTA-OCTA. RESULTS: Seven eyes from seven patients were evaluated. Swept-source OCTA enabled detailed enface visualization of the BVN and polyps in six eyes. VISTA-OCTA revealed variable blood flow speeds in different PCV lesion components of the same eye, with faster flow in the periphery of polyps and slower flow in the center of each polyp, as well as relatively slow flow in BVN when compared with retinal vessels. BVNs demonstrated relatively faster blood flow speeds in the larger trunk vessels and relatively slower speeds in the smaller vessels. CONCLUSIONS: Swept-source OCTA identifies polyps in most, but not all, PCV lesions. This limitation that may be related to relatively slow blood flow within the polyp, which may be below the OCTA's sensitivity. VISTA-OCTA showed heterogeneous blood flow speeds within the polyps, which may indicate turbulent flow in the polyps. TRANSLATIONAL RELEVANCE: These results bring relevant insights into disease mechanisms that can account for the variable course of PCV, and can be relevant for diagnosis and management of patients with PCV.

The Definition, Rationale, and Effects of Thresholding in OCT Angiography.

PURPOSE: To examine the definition, rationale, and effects of thresholding in OCT angiography (OCTA). DESIGN: A theoretical description of OCTA thresholding in combination with qualitative and quantitative analysis of the effects of OCTA thresholding in eyes from a retrospective case series. PARTICIPANTS: Four eyes were qualitatively examined: 1 from a 27-year-old control, 1 from a 78-year-old exudative age-related macular degeneration (AMD) patient, 1 from a 58-year-old myopic patient, and 1 from a 77-year-old nonexudative AMD patient with geographic atrophy (GA). One eye from a 75-year-old nonexudative AMD patient with GA was quantitatively analyzed. MAIN OUTCOME MEASURES: A theoretical thresholding model and a qualitative and quantitative description of the dependency of OCTA on thresholding level. RESULTS: Due to the presence of system noise, OCTA thresholding is a necessary step in forming OCTA images; however, thresholding can complicate the relationship between blood flow and OCTA signal. CONCLUSIONS: Thresholding in OCTA can cause significant artifacts, which should be considered when interpreting and quantifying OCTA images.

SWEPT-SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY REVEALS CHORIOCAPILLARIS ALTERATIONS IN EYES WITH NASCENT GEOGRAPHIC ATROPHY AND DRUSEN-ASSOCIATED GEOGRAPHIC ATROPHY.

PURPOSE: To investigate choriocapillaris (CC) alteration in patients with nascent geographic atrophy (nGA) and/or drusen-associated geographic atrophy (DAGA) using swept-source optical coherence tomography angiography (OCTA). METHODS: A 1,050-nm wavelength, 400 kHz A-scan rate swept-source optical coherence tomography prototype was used to perform volumetric swept-source optical coherence tomography angiography over 6 mm × 6 mm fields of view in patients with nGA and/or DAGA. The resulting optical coherence tomography (OCT) and OCTA data were analyzed using a combination of en face and cross-sectional techniques. Variable interscan time analysis (VISTA) was used to differentiate CC flow impairment from complete CC atrophy. RESULTS: A total of 7 eyes from 6 patients (mean age: 73.8 ± 5.7 years) were scanned. Seven areas of nGA and three areas of DAGA were identified. Analysis of cross-sectional OCT and OCTA images identified focal alterations of the CC underlying all seven areas of nGA and all three areas of DAGA. En face OCTA analysis of the CC revealed diffuse CC alterations in all eyes. Variable interscan time analysis processing suggested that the observed CC flow alterations predominantly corresponded to flow impairment rather than complete CC atrophy. CONCLUSION: The OCTA imaging of the CC revealed focal CC flow impairment associated with areas of nGA and DAGA, as well as diffuse CC flow impairment throughout the imaged field. En face OCT analysis should prove useful for understanding the pathogenesis of nGA and DAGA and for identifying the formation of nGA and DAGA as endpoints in therapeutic trials.

AN AUTOMATIC, INTERCAPILLARY AREA-BASED ALGORITHM FOR QUANTIFYING DIABETES-RELATED CAPILLARY DROPOUT USING OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PURPOSE: To develop a robust, sensitive, and fully automatic algorithm to quantify diabetes-related capillary dropout using optical coherence tomography (OCT) angiography (OCTA). METHODS: A 1,050-nm wavelength, 400 kHz A-scan rate swept-source optical coherence tomography prototype was used to perform volumetric optical coherence tomography angiography imaging over 3 mm × 3 mm fields in normal controls (n = 5), patients with diabetes without diabetic retinopathy (DR) (n = 7), patients with nonproliferative diabetic retinopathy (NPDR) (n = 9), and patients with proliferative diabetic retinopathy (PDR) (n = 5); for each patient, one eye was imaged. A fully automatic algorithm to quantify intercapillary areas was developed. RESULTS: Of the 26 evaluated eyes, the segmentation was successful in 22 eyes (85%). The mean values of the 10 and 20 largest intercapillary areas, either including or excluding the foveal avascular zone, showed a consistent trend of increasing size from normal control eyes, to eyes with diabetic retinopathy but without diabetic retinopathy, to nonproliferative diabetic retinopathy eyes, and finally to PDR eyes. CONCLUSION: Optical coherence tomography angiography-based screening and monitoring of patients with diabetic retinopathy is critically dependent on automated vessel analysis. The algorithm presented was able to automatically extract an intercapillary area-based metric in patients having various stages of diabetic retinopathy. Intercapillary area-based approaches are likely more sensitive to early stage capillary dropout than vascular density-based methods.

TOWARD QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY: Visualizing Blood Flow Speeds in Ocular Pathology Using Variable Interscan Time Analysis.

PURPOSE: Currently available optical coherence tomography angiography systems provide information about blood flux but only limited information about blood flow speed. The authors develop a method for mapping the previously proposed variable interscan time analysis (VISTA) algorithm into a color display that encodes relative blood flow speed. METHODS: Optical coherence tomography angiography was performed with a 1,050 nm, 400 kHz A-scan rate, swept source optical coherence tomography system using a 5 repeated B-scan protocol. Variable interscan time analysis was used to compute the optical coherence tomography angiography signal from B-scan pairs having 1.5 millisecond and 3.0 milliseconds interscan times. The resulting VISTA data were then mapped to a color space for display. RESULTS: The authors evaluated the VISTA visualization algorithm in normal eyes (n = 2), nonproliferative diabetic retinopathy eyes (n = 6), proliferative diabetic retinopathy eyes (n = 3), geographic atrophy eyes (n = 4), and exudative age-related macular degeneration eyes (n = 2). All eyes showed blood flow speed variations, and all eyes with pathology showed abnormal blood flow speeds compared with controls. CONCLUSION: The authors developed a novel method for mapping VISTA into a color display, allowing visualization of relative blood flow speeds. The method was found useful, in a small case series, for visualizing blood flow speeds in a variety of ocular diseases and serves as a step toward quantitative optical coherence tomography angiography.