Minhaj Nur Alam

Quantitative characteristics of sickle cell retinopathy in optical coherence tomography angiography

Early detection is an essential step for effective intervention of sickle cell retinopathy (SCR). Emerging optical coherence tomography angiography (OCTA) provides excellent three-dimensional (3D) resolution to enable label-free, noninvasive visualization of retinal vascular structures, promising improved sensitivity in detecting SCR. However, quantitative analysis of SCR characteristics in OCTA images is yet to be established. In this study, we conducted comprehensive analysis of six OCTA parameters, including blood vessel tortuosity, vessel diameter, vessel perimeter index (VPI), area of foveal avascular zone (FAZ), contour irregularity of FAZ and parafoveal avascular density. Compared to traditional retinal thickness analysis, five of these six OCTA parameters show improved sensitivity for SCR detection than retinal thickness. It is observed that the most sensitive parameters were the contour irregularity of FAZ in the superficial layer and avascular density in temporal regions, while the area of FAZ, tortuosity and mean diameter of the vessel were moderately sensitive.

Computer-aided classification of sickle cell retinopathy using quantitative features in optical coherence tomography angiography

As a new optical coherence tomography (OCT) imaging modality, there is no standardized quantitative interpretation of OCT angiography (OCTA) characteristics of sickle cell retinopathy (SCR). This study is to demonstrate computer-aided SCR classification using quantitative OCTA features, i.e., blood vessel tortuosity (BVT), blood vessel diameter (BVD), vessel perimeter index (VPI), foveal avascular zone (FAZ) area, FAZ contour irregularity, parafoveal avascular density (PAD). It was observed that combined features show improved classification performance, compared to single feature. Three classifiers, including support vector machine (SVM), k-nearest neighbor (KNN) algorithm, and discriminant analysis, were evaluated. Sensitivity, specificity, and accuracy were quantified to assess the performance of each classifier. For SCR vs. control classification, all three classifiers performed well with an average accuracy of 95% using the six quantitative OCTA features. For mild vs. severe stage retinopathy classification, SVM shows better (97% accuracy) performance, compared to KNN algorithm (95% accuracy) and discriminant analysis (88% accuracy).

In vivo super-resolution retinal imaging through virtually structured detection

High resolution is important for sensitive detection of subtle distortions of retinal morphology at an early stage of eye diseases. We demonstrate virtually structured detection (VSD) as a feasible method to achieve in vivo super-resolution ophthalmoscopy. A line-scanning strategy was employed to achieve a super-resolution imaging speed up to 127 ?? frames / s with a frame size of 512 × 512 ?? pixels . The proof-of-concept experiment was performed on anesthetized frogs. VSD-based super-resolution images reveal individual photoreceptors and nerve fiber bundles unambiguously. Both image contrast and signal-to-noise ratio are significantly improved due to the VSD implementation.

Combining ODR and Blood Vessel Tracking for Artery–Vein Classification and Analysis in Color Fundus Images

Purpose This study aims to develop a fully automated algorithm for artery–vein (A-V) and arteriole-venule classification and to quantify the effect of hypertension on A-V caliber and tortuosity ratios of nonproliferative diabetic retinopathy (NPDR) patients. Methods We combine an optical density ratio (ODR) analysis and blood vessel tracking (BVT) algorithm to classify arteries and veins and arterioles and venules. An enhanced blood vessel map and ODR analysis are used to determine the blood vessel source nodes. The whole vessel map is then tracked beginning from the source nodes and classified as vein (venule) or artery (arteriole) using vessel curvature and angle information. Fifty color fundus images from NPDR patients are used to test the algorithm. Sensitivity, specificity, and accuracy metrics are measured to validate the classification method compared to ground truths. Results The combined ODR-BVT method demonstrates 97.06% accuracy in identifying blood vessels as vein or artery. Sensitivity and specificity of A-V identification are 97.58%, 97.81%, and 95.89%, 96.68%, respectively. Comparative analysis revealed that the average A-V caliber and tortuosity ratios of NPDR patients with hypertension have 48% and 15.5% decreases, respectively, compared to that of NPDR patients without hypertension. Conclusions Automated A-V classification has been achieved by combined ODR-BVT analysis. Quantitative analysis of color fundus images verified robust performance of the A-V classification. Comparative quantification of A-V caliber and tortuosity ratios provided objective biomarkers to differentiate NPDR groups with and without hypertension. Translational Relevance Automated A-V classification can facilitate quantitative analysis of retinal vascular distortions due to diabetic retinopathy and other eye conditions and provide increased sensitivity for early detection of eye diseases.

Contact-free trans-pars-planar illumination enables snapshot fundus camera for nonmydriatic wide field photography

In conventional fundus photography, trans-pupillary illumination delivers illuminating light to the interior of the eye through the peripheral area of the pupil, and only the central part of the pupil can be used for collecting imaging light. Therefore, the field of view of conventional fundus cameras is limited, and pupil dilation is required for evaluating the retinal periphery which is frequently affected by diabetic retinopathy (DR), retinopathy of prematurity (ROP), and other chorioretinal conditions. We report here a nonmydriatic wide field fundus camera employing trans-pars-planar illumination which delivers illuminating light through the pars plana, an area outside of the pupil. Trans-pars-planar illumination frees the entire pupil for imaging purpose only, and thus wide field fundus photography can be readily achieved with less pupil dilation. For proof-of-concept testing, using all off-the-shelf components a prototype instrument that can achieve 90° fundus view coverage in single-shot fundus images, without the need of pharmacologic pupil dilation was demonstrated.

Automated classification and quantitative analysis of arterial and venous vessels in fundus images

It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).

Functional optical coherence tomography of neurovascular coupling interactions in the retina

Quantitative evaluation of retinal neurovascular coupling is essential for a better understanding of visual function and early detection of eye diseases. However, there is no established method to monitor coherent interactions between stimulus-evoked neural activity and hemodynamic responses at high resolution. Here, we report a multimodal functional optical coherence tomography (OCT) imaging methodology to enable concurrent intrinsic optical signal (IOS) imaging of stimulus-evoked neural activity and hemodynamic responses at capillary resolution. OCT angiography guided IOS analysis was used to separate neural-IOS and hemodynamic-IOS changes in the same retinal image sequence. Frequency flicker stimuli evoked neural-IOS changes in the outer retina; that is, photoreceptor layer, first and then in the inner retina, including outer plexus layer (OPL), inner plexiform layer (IPL), and ganglion cell layer (GCL), which were followed by hemodynamic-IOS changes primarily in the inner retina; that is, OPL, IPL, and GCL. Different time courses and signal magnitudes of hemodynamic-IOS responses were observed in blood vessels with various diameters.

Color Fundus Image Guided Artery-Vein Differentiation in Optical Coherence Tomography Angiography

Purpose This study aimed to develop a method for automated artery-vein classification in optical coherence tomography angiography (OCTA), and to verify that differential artery-vein analysis can improve the sensitivity of OCTA detection and staging of diabetic retinopathy (DR). Methods For each patient, the color fundus image was used to guide the artery-vein differentiation in the OCTA image. Traditional mean blood vessel caliber (m-BVC) and mean blood vessel tortuosity (m-BVT) in OCTA images were quantified for control and DR groups. Artery BVC (a-BVC), vein BVC (v-BVC), artery BVT (a-BVT), and vein BVT (a-BVT) were calculated, and then the artery-vein ratio (AVR) of BVC (AVR-BVC) and AVR of BVT (AVR-BVT) were quantified for comparative analysis. Sensitivity, specificity, and accuracy were used as performance metrics of artery-vein classification. One-way, multilabel ANOVA with Bonferronis test and Students t-test were employed for statistical analysis. Results Forty eyes of 20 control subjects and 80 eyes of 48 NPDR patients (18 mild, 16 moderate, and 14 severe NPDR) were evaluated in this study. The color fundus image–guided artery-vein differentiation reliably identified individual arteries and veins in OCTA. AVR-BVC and AVR-BVT provided significant (P < 0.001) and moderate (P < 0.05) improvements, respectively, in detecting and classifying NPDR stages, compared with traditional m-BVC analysis. Conclusions Color fundus image–guided artery-vein classification provides a feasible method to differentiate arteries and veins in OCTA. Differential artery-vein analysis can improve the sensitivity of OCTA detection and classification of DR. AVR-BVC is the most-sensitive feature, which can classify control and mild NPDR, providing a quantitative biomarker for objective detection of early DR.