OCT angiography and its retinal biomarkers [Invited]

Techniques for imaging the choroid and choroidal blood flow in vivo

The choroid, which is a highly vascularized layer between the retina and sclera, is essential for supplying oxygen and nutrients to the outer retina. Choroidal vascular dysfunction has been implicated in numerous ocular diseases, including age-related macular degeneration, central serous chorioretinopathy, polypoidal choroidal vasculopathy, and myopia. Traditionally, the in vivo assessment of choroidal blood flow relies on techniques such as laser Doppler flowmetry, laser speckle flowgraphy, pneumotonometry, laser interferometry, and ultrasonic color Doppler imaging. While the aforementioned methods have provided valuable insights into choroidal blood flow regulation, their clinical applications have been limited. Recent advancements in optical coherence tomography and optical coherence tomography angiography have expanded our understanding of the choroid, allowing detailed visualization of the larger choroidal vessels and choriocapillaris, respectively. This review provides an overview of the available techniques that can investigate the choroid and its blood flow in vivo. Future research should combine these techniques to comprehensively image the entire choroidal microcirculation and develop robust methods to quantify choroidal blood flow. The potential findings will provide a better picture of choroidal hemodynamics and its effect on ocular health and disease.

Adaptive contour-tracking to aid wide-field swept-source optical coherence tomography imaging of large objects with uneven surface topology

High-speed and wide-field optical coherence tomography (OCT) imaging is increasingly essential for clinical applications yet faces challenges due to its inherent sensitivity roll-off and limited depth of focus, particularly when imaging samples with significant variations in surface contour. Here, we propose one innovative solution of adaptive contour tracking and scanning methods to address these challenges. The strategy integrates an electrically tunable lens and adjustable optical delay line control with real-time surface contour information, enabling dynamic optimization of imaging protocols. It rapidly pre-scans the sample surface to acquire a comprehensive contour map. Using this map, it generates a tailored scanning protocol by partitioning the entire system ranging distance into depth-resolved segments determined by the optical Raleigh length of the objective lens, ensuring optimal imaging at each segment. Employing short-range imaging mode along the sample contour minimizes data storage and post-processing requirements, while adaptive adjustment of focal length and reference optical delay line maintains high imaging quality throughout. Experimental demonstrations show the effectiveness of the adaptive contour tracking OCT in maintaining high contrast and signal-to-noise ratio across the entire field of view, even in samples with significantly uneven surface curvatures. Notably, this approach achieves these results with reduced data volume compared to traditional OCT methods. This advancement holds promise for enhancing OCT imaging in clinical settings, particularly in applications requiring rapid, wide-field imaging of tissue structures and blood flow.

High-speed, long-range and wide-field OCT for in vivo 3D imaging of the oral cavity achieved by a 600 kHz swept source laser

We report a high-speed, long-range, and wide-field swept-source optical coherence tomography (SS-OCT) system aimed for imaging microstructures and microcirculations in the oral cavity. This system operates at a scan speed of 600 kHz, delivering a wide imaging field of view at 42 × 42 mm2 and a ranging distance of 36 mm. To simultaneously meet the requirements of high speed and long range, it is necessary for the k-clock trigger signal to be generated at its maximum speed, which may induce non-linear phase response in electronic devices due to the excessive k-clock frequency bandwidth, leading to phase errors. To address this challenge, we introduced a concept of electrical dispersion and a global k-clock compensation approach to improve overall performance of the imaging system. Additionally, image distortion in the wide-field imaging mode is also corrected using a method based on distortion vector maps. With this system, we demonstrate comprehensive structural and blood flow imaging of the anterior oral cavity in healthy individuals. The high-speed, long-range, and wide-field SS-OCT system opens new opportunities for comprehensive oral cavity examinations and holds promise as a reliable tool for assessing oral health conditions.

Towards standardising retinal OCT angiography image analysis with open-source toolbox OCTAVA

Quantitative assessment of retinal microvasculature in optical coherence tomography angiography (OCTA) images is important for studying, diagnosing, monitoring, and guiding the treatment of ocular and systemic diseases. However, the OCTA user community lacks universal and transparent image analysis tools that can be applied to images from a range of OCTA instruments and provide reliable and consistent microvascular metrics from diverse datasets. We present a retinal extension to the OCTA Vascular Analyser (OCTAVA) that addresses the challenges of providing robust, easy-to-use, and transparent analysis of retinal OCTA images. OCTAVA is a user-friendly, open-source toolbox that can analyse retinal OCTA images from various instruments. The toolbox delivers seven microvascular metrics for the whole image or subregions and six metrics characterising the foveal avascular zone. We validate OCTAVA using images collected by four commercial OCTA instruments demonstrating robust performance across datasets from different instruments acquired at different sites from different study cohorts. We show that OCTAVA delivers values for retinal microvascular metrics comparable to the literature and reduces their variation between studies compared to their commercial equivalents. By making OCTAVA publicly available, we aim to expand standardised research and thereby improve the reproducibility of quantitative analysis of retinal microvascular imaging. Such improvements will help to better identify more reliable and sensitive biomarkers of ocular and systemic diseases.

Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging

Effective biomarkers are required for assessing the progression of age-related macular degeneration (AMD), a prevalent and progressive eye disease. This paper presents a deep learning-based automated algorithm, applicable to both swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT) scans, for measuring outer retinal layer (ORL) thickness as a surrogate biomarker for outer retinal degeneration, e.g., photoreceptor disruption, to assess AMD progression. The algorithm was developed based on a modified TransUNet model with clinically annotated retinal features manifested in the progression of AMD. The algorithm demonstrates a high accuracy with an intersection of union (IoU) of 0.9698 in the testing dataset for segmenting ORL using both SS-OCT and SD-OCT datasets. The robustness and applicability of the algorithm are indicated by strong correlation (r = 0.9551, P < 0.0001 in the central-fovea 3 mm-circle, and r = 0.9442, P < 0.0001 in the 5 mm-circle) and agreement (the mean bias = 0.5440 um in the 3-mm circle, and 1.392 um in the 5-mm circle) of the ORL thickness measurements between SS-OCT and SD-OCT scans. Comparative analysis reveals significant differences (P < 0.0001) in ORL thickness among 80 normal eyes, 30 intermediate AMD eyes with reticular pseudodrusen, 49 intermediate AMD eyes with drusen, and 40 late AMD eyes with geographic atrophy, highlighting its potential as an independent biomarker for predicting AMD progression. The findings provide valuable insights into the ORL alterations associated with different stages of AMD and emphasize the potential of ORL thickness as a sensitive indicator of AMD severity and progression.

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed.

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

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