Automated motion correction using parallel-strip registration for wide-field en face OCT angiogram

Subpixel motion artifacts correction and motion estimation for 3D-OCT

A number of hardware-based and software-based strategies have been suggested to eliminate motion artifacts for improvement of 3D-optical coherence tomography (OCT) image quality. However, the hardware-based strategies have to employ additional hardware to record motion compensation information. Many software-based strategies have to need additional scanning for motion correction at the expense of longer acquisition time. To address this issue, we propose a motion artifacts correction and motion estimation method for OCT volumetric imaging of anterior segment, without requirements of additional hardware and redundant scanning. The motion correction effect with subpixel accuracy for in vivo 3D-OCT has been demonstrated in experiments. Moreover, the physiological information of imaging object, including respiratory curve and respiratory rate, has been experimentally extracted using the proposed method. The proposed method offers a powerful tool for scientific research and clinical diagnosis in ophthalmology and may be further extended for other biomedical volumetric imaging applications.

Variability in Capillary Perfusion Is Increased in Regions of Retinal Ischemia Due to Branch Retinal Vein Occlusion

Purpose

To investigate alterations in macular perfusion variability due to branch retinal vein occlusion (BRVO) using a novel approach based on optical coherence tomography angiography (OCTA) coefficient of variation (CoV) analysis.

Methods

Thirteen eyes of 13 patients with macular ischemia due to BRVO were studied. Multiple consecutive en face OCTA images were acquired. Bias field correction, spatial alignment, and normalization of intensities across the images were performed followed by pixelwise computation of standard deviation divided by the mean to generate a CoV map. Region of interest-based CoV values, derived from this map, for arterioles, venules, and the microvasculature were compared between regions with macular ischemia and control areas of the same eye. Control areas were regions of the same macula that were not affected by the BRVO and had normal retinal vascular structure as seen on multimodal imaging and normal retinal vascular density measurements as quantified using OCTA.

Results

CoV increased by a mean value of 17.6% within the microvasculature of ischemic regions compared to the control microvasculature (P < 0.0001). CoV measurements of microvasculature were consistently greater in the ischemic area of all 13 eyes compared to control. There were no differences in CoV measurements between ischemic and control areas for arterioles (P = 0.13) and venules (P = 1.0).

Conclusions

Greater variability in microvasculature perfusion occurs at sites of macular ischemia due to BRVO. We report a novel way for quantifying macular perfusion variability using OCTA. This technique may have applicability for studying the pathophysiology of other retinal vascular diseases.

Optical coherence tomography split-spectrum amplitude-decorrelation optoretinography

This pilot study reports the development of optical coherence tomography (OCT) split-spectrum amplitude-decorrelation optoretinography (SSADOR) that measures spatially resolved photoreceptor response to light stimuli. Using spectrally multiplexed narrowband OCT, SSADOR improves sensitivity to microscopic changes without the need for cellular resolution or optical phase detection. Therefore, a large field of view (up to 3 × 1 mm² demonstrated) using conventional OCT instrument design can be achieved, paving the way for clinical translation. SSADOR promises a fast, objective, and quantifiable functional biomarker for photoreceptor damage in the macula.

Anterior and posterior imaging with hyperparallel OCT

Hyperparallel OCT (HP-OCT) is a parallel spectral domain imaging technology particularly well-suited to the anterior segment. It uses a 2-dimensional grid of 1008 beams to simultaneously image across a wide area of the eye. In this paper we demonstrate that sparsely sampled volumes captured at 300 Hz can be registered without the need for active eye tracking to produce 3-dimensional (3D) volumes free from motion artefacts. The anterior volume provides complete 3D biometric information, including lens position, curvature, epithelial thickness, tilt, and axial length. We further demonstrate that, with the change of a detachable lens, we can capture high resolution anterior volumes and importantly, posterior volume images for preoperative assessment of the posterior segment. Advantageously, the retinal volumes have the same 11.2 mm Nyquist range as the anterior imaging mode.

Deformable registration of multimodal retinal images using a weakly supervised deep learning approach

There are different retinal vascular imaging modalities widely used in clinical practice to diagnose different retinal pathologies. The joint analysis of these multimodal images is of increasing interest since each of them provides common and complementary visual information. However, if we want to facilitate the comparison of two images, obtained with different techniques and containing the same retinal region of interest, it will be necessary to make a previous registration of both images. Here, we present a weakly supervised deep learning methodology for robust deformable registration of multimodal retinal images, which is applied to implement a method for the registration of fluorescein angiography (FA) and optical coherence tomography angiography (OCTA) images. This methodology is strongly inspired by VoxelMorph, a general unsupervised deep learning framework of the state of the art for deformable registration of unimodal medical images. The method was evaluated in a public dataset with 172 pairs of FA and superficial plexus OCTA images. The degree of alignment of the common information (blood vessels) and preservation of the non-common information (image background) in the transformed image were measured using the Dice coefficient (DC) and zero-normalized cross-correlation (ZNCC), respectively. The average values of the mentioned metrics, including the standard deviations, were DC = 0.72  ±  0.10 and ZNCC = 0.82  ±  0.04. The time required to obtain each pair of registered images was 0.12 s. These results outperform rigid and deformable registration methods with which our method was compared.

A two-stage framework for optical coherence tomography angiography image quality improvement

Introduction

Optical Coherence Tomography Angiography (OCTA) is a new non-invasive imaging modality that gains increasing popularity for the observation of the microvasculatures in the retina and the conjunctiva, assisting clinical diagnosis and treatment planning. However, poor imaging quality, such as stripe artifacts and low contrast, is common in the acquired OCTA and in particular Anterior Segment OCTA (AS-OCTA) due to eye microtremor and poor illumination conditions. These issues lead to incomplete vasculature maps that in turn makes it hard to make accurate interpretation and subsequent diagnosis.

Methods

In this work, we propose a two-stage framework that comprises a de-striping stage and a re-enhancing stage, with aims to remove stripe noise and to enhance blood vessel structure from the background. We introduce a new de-striping objective function in a Stripe Removal Net (SR-Net) to suppress the stripe noise in the original image. The vasculatures in acquired AS-OCTA images usually exhibit poor contrast, so we use a Perceptual Structure Generative Adversarial Network (PS-GAN) to enhance the de-striped AS-OCTA image in the re-enhancing stage, which combined cyclic perceptual loss with structure loss to achieve further image quality improvement.

Results and discussion

To evaluate the effectiveness of the proposed method, we apply the proposed framework to two synthetic OCTA datasets and a real AS-OCTA dataset. Our results show that the proposed framework yields a promising enhancement performance, which enables both conventional and deep learning-based vessel segmentation methods to produce improved results after enhancement of both retina and AS-OCTA modalities.

Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sampling frequency, which is not conducive to clinicians for analyzing larger vascular areas. In this paper, we propose a novel Sparse-based domain Adaptation Super-Resolution network (SASR) for the reconstruction of realistic [Formula: see text]/low-resolution (LR) OCTA images to high-resolution (HR) representations. To be more specific, we first perform a simple degradation of the [Formula: see text]/high-resolution (HR) image to obtain the synthetic LR image. An efficient registration method is then employed to register the synthetic LR with its corresponding [Formula: see text] image region within the [Formula: see text] image to obtain the cropped realistic LR image. We then propose a multi-level super-resolution model for the fully-supervised reconstruction of the synthetic data, guiding the reconstruction of the realistic LR images through a generative-adversarial strategy that allows the synthetic and realistic LR images to be unified in the feature domain. Finally, a novel sparse edge-aware loss is designed to dynamically optimize the vessel edge structure. Extensive experiments on two OCTA sets have shown that our method performs better than state-of-the-art super-resolution reconstruction methods. In addition, we have investigated the performance of the reconstruction results on retina structure segmentations, which further validate the effectiveness of our approach.

Integrated deep learning framework for accelerated optical coherence tomography angiography

Label-free optical coherence tomography angiography (OCTA) has become a premium imaging tool in clinics to obtain structural and functional information of microvasculatures. One primary technical drawback for OCTA, however, is its imaging speed. The current protocols require high sampling density and multiple acquisitions of cross-sectional B-scans to form one image frame, resulting in low acquisition speed. Recently, deep learning (DL)-based methods have gained attention in accelerating the OCTA acquisition process. They achieve faster acquisition using two independent reconstructing approaches: high-quality angiograms from a few repeated B-scans and high-resolution angiograms from undersampled data. While these approaches have shown promising results, they provide limited solutions that only partially account for the OCTA scanning mechanism. Herein, we propose an integrated DL method to simultaneously tackle both factors and further enhance the reconstruction performance in speed and quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully-sampled, high-quality (8 repeated B-scans) angiograms from their corresponding undersampled, low-quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. Using an in-vivo mouse brain vasculature dataset, we evaluate our proposed framework through quantitative and qualitative assessments and demonstrate that our method can achieve superior reconstruction performance compared to the conventional means. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256[Formula: see text] while preserving the image quality, thus enabling a convenient software-only solution to enhance preclinical and clinical studies.

High speed, long range, deep penetration swept source OCT for structural and angiographic imaging of the anterior eye

This study reports the development of prototype swept-source optical coherence tomography (SS-OCT) technology for imaging the anterior eye. Advances in vertical-cavity surface-emitting laser (VCSEL) light sources, signal processing, optics and mechanical designs, enable a unique combination of high speed, long range, and deep penetration that addresses the challenges of anterior eye imaging. We demonstrate SS-OCT with a 325 kHz A-scan rate, 12.2 µm axial resolution (in air), and 15.5 mm depth range (in air) at 1310 nm wavelength. The ultrahigh 325 kHz A-scan rate not only facilitates biometry measurements by minimizing acquisition time and thus reducing motion, but also enables volumetric OCT for comprehensive structural analysis and OCT angiography (OCTA) for visualizing vasculature. The 15.5 mm (~ 11.6 mm in tissue) depth range spans all optical surfaces from the anterior cornea to the posterior lens capsule. The 1310 nm wavelength range enables structural OCT and OCTA deep in the sclera and through the iris. Achieving high speed and long range requires linearizing the VCSEL wavenumber sweep to efficiently utilize analog-to-digital conversion bandwidth. Dual channel recording of the OCT and calibration interferometer fringe signals, as well as sweep to sweep wavenumber compensation, is used to achieve invariant 12.2 µm (~ 9.1 µm in tissue) axial resolution and optimum point spread function throughout the depth range. Dynamic focusing using a tunable liquid lens extends the effective depth of field while preserving the lateral resolution. Improved optical and mechanical design, including parallax “split view” iris cameras and stable, ergonomic patient interface, facilitates accurate instrument positioning, reduces patient motion, and leads to improved imaging data yield and measurement accuracy. We present structural and angiographic OCT images of the anterior eye, demonstrating the unique imaging capabilities using representative scanning protocols which may be relevant to future research and clinical applications.

Artificial intelligence in OCT angiography

Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides three-dimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and monitoring, OCTA technology has seen rapid adoption in research and clinical settings. The value of OCTA imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features and pathology. Today, the most powerful image analysis methods are based on artificial intelligence (AI). While AI encompasses a large variety of techniques, machine-learning-based, and especially deep-learning-based, image analysis provides accurate measurements in a variety of contexts, including different diseases and regions of the eye. Here, we discuss the principles of both OCTA and AI that make their combination capable of answering new questions. We also review contemporary applications of AI in OCTA, which include accurate detection of pathologies such as choroidal neovascularization, precise quantification of retinal perfusion, and reliable disease diagnosis.

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.

Robust multimodal registration of fluorescein angiography and optical coherence tomography angiography images using evolutionary algorithms

Optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) are two different vascular imaging modalities widely used in clinical practice to diagnose and grade different relevant retinal pathologies. Although each of them has its advantages and disadvantages, the joint analysis of the images produced by both techniques to analyze a specific area of the retina is of increasing interest, given that they provide common and complementary visual information. However, in order to facilitate this analysis task, a previous registration of the pair of FA and OCTA images is desirable in order to superimpose their common areas and focus the gaze on the regions of interest. Normally, this task is manually carried out by the expert clinician, but it turns out to be tedious and time-consuming. Here, we present a three-stage methodology for robust multimodal registration of FA and superficial plexus OCTA images. The first one is a preprocessing stage devoted to reducing the noise and segmenting the main vessels in both types of images. The second stage uses the vessel information to do an approximate registration based on template matching. Lastly, the third stage uses an evolutionary algorithm based on differential evolution to refine the previous registration and obtain the optimal registration. The method was evaluated in a dataset with 172 pairs of FA and OCTA images, obtaining a success rate of 98.8%. The best mean execution time of the method was less than 5 s per image.

DETECTION OF CLINICALLY UNSUSPECTED RETINAL NEOVASCULARIZATION WITH WIDE-FIELD OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY

PURPOSE

To evaluate wide-field optical coherence tomography angiography (OCTA) for detection of clinically unsuspected neovascularization (NV) in diabetic retinopathy (DR).

METHODS

This prospective observational single-center study included adult patients with a clinical diagnosis of nonproliferative DR. Participants underwent a clinical examination, standard 7-field color photography, and OCTA with commercial and prototype swept-source devices. The wide-field OCTA was achieved by montaging five 6 × 10-mm scans from a prototype device into a 25 × 10-mm image and three 6 × 6-mm scans from a commercial device into a 15 × 6-mm image. A masked grader determined the retinopathy severity from color photographs. Two trained readers examined conventional and wide-field OCTA images for the presence of NV.

RESULTS

Of 27 participants, photographic grading found 13 mild, 7 moderate, and 7 severe nonproliferative DR. Conventional 6 × 6-mm OCTA detected NV in 2 eyes (7%) and none with 3 × 3-mm scans. Both prototype and commercial wide-field OCTA detected NV in two additional eyes. The mean area of NV was 0.38 mm (range 0.17-0.54 mm). All eyes with OCTA-detected NV were photographically graded as severe nonproliferative DR.

CONCLUSION

Wide-field OCTA can detect small NV not seen on clinical examination or color photographs and may improve the clinical evaluation of DR.

Real-time measurement of repetitive micro bulk motion vector and motion noise removal in optical coherence tomography angiography

In this work, we developed a motion estimation and correction method which real-time obtained the direction and displacement of repetitive micro bulk motion (such as cardiac and respiratory motion) on an SS-OCT system without additional tracking hardware, and reduced the motion noise in optical coherence tomography angiography (OCTA). In the approach, the direction of repetitive micro bulk motion was considered fixed, and proportional relationships between the motion components in three directions were determined; Then we performed one-dimension cross-correlation to obtain depth displacement which was further used to obtain other two motion components, and greatly reduced the computation; The processing speed on a graphic processing unit was 478 pairs of B-Scans per second, and the measurement range was larger than the range of the angiogram-based methods. Lastly, corrupt angiograms were recovered by adaptive scan protocol, and reduced acquisition time in comparison with the previous work.

Artifacts and artifact removal in optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) enables rapid imaging of retinal vasculature in three dimensions. While the technique has provided quantification of healthy vessels as well as pathology in several diseases, it is not unusual for OCTA data to contain artifacts that may influence measurement outcomes or defy image interpretation. In this review, we discuss the sources of several OCTA artifacts-including projection, motion, and signal reduction-as well as strategies for their removal. Artifact compensation can improve the accuracy of OCTA measurements, and the most effective use of the technology will incorporate hardware and software that can perform such correction.

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.

Optimizing 3D retinal vasculature imaging in diabetic retinopathy using registration and averaging of OCT-A

High resolution visualization of optical coherence tomography (OCT) and OCT angiography (OCT-A) data is required to fully take advantage of the imaging modality's three-dimensional nature. However, artifacts induced by patient motion often degrade OCT-A data quality. This is especially true for patients with deteriorated focal vision, such as those with diabetic retinopathy (DR). We propose a novel methodology for software-based OCT-A motion correction achieved through serial acquisition, volumetric registration, and averaging. Motion artifacts are removed via a multi-step 3D registration process, and visibility is significantly enhanced through volumetric averaging. We demonstrate that this method permits clear 3D visualization of retinal pathologies and their surrounding features, 3D visualization of inner retinal capillary connections, as well as reliable visualization of the choriocapillaris layer.

Accurately motion-corrected Lissajous OCT with multi-type image registration

Passive motion correction methods for optical coherence tomography (OCT) use image registration to estimate eye movements. To improve motion correction, a multi-image cross-correlation that employs spatial features in different image types is introduced. Lateral motion correction using en face OCT and OCT-A projections on Lissajous-scanned OCT data is applied. Motion correction using OCT-A projection of whole depth and OCT amplitude, OCT logarithmic intensity, and OCT maximum intensity projections were evaluated in retinal imaging with 76 patients. The proposed method was compared with motion correction using OCT-A projection of whole depth. The comparison shows improvements in the image quality of motion-corrected superficial OCT-A images and image registration.

Correcting intra-volume distortion for AO-OCT using 3D correlation based registration

Adaptive optics (AO) based ophthalmic imagers, such as scanning laser ophthalmoscopes (SLO) and optical coherence tomography (OCT), are used to evaluate the structure and function of the retina with high contrast and resolution. Fixational eye movements during a raster-scanned image acquisition lead to intra-frame and intra-volume distortion, resulting in an inaccurate reproduction of the underlying retinal structure. For three-dimensional (3D) AO-OCT, segmentation-based and 3D correlation based registration methods have been applied to correct eye motion and achieve a high signal-to-noise ratio registered volume. This involves first selecting a reference volume, either manually or automatically, and registering the image/volume stream against the reference using correlation methods. However, even within the chosen reference volume, involuntary eye motion persists and affects the accuracy with which the 3D retinal structure is finally rendered. In this article, we introduced reference volume distortion correction for AO-OCT using 3D correlation based registration and demonstrate a significant improvement in registration performance via a few metrics. Conceptually, the general paradigm follows that developed previously for intra-frame distortion correction for 2D raster-scanned images, as in an AOSLO, but extended here across all three spatial dimensions via 3D correlation analyses. We performed a frequency analysis of eye motion traces before and after intra-volume correction and revealed how periodic artifacts in eye motion estimates are effectively reduced upon correction. Further, we quantified how the intra-volume distortions and periodic artifacts in the eye motion traces, in general, decrease with increasing AO-OCT acquisition speed. Overall, 3D correlation based registration with intra-volume correction significantly improved the visualization of retinal structure and estimation of fixational eye movements.

Automated Nuclear Lamina Network Recognition and Quantitative Analysis in Structured Illumination Super-Resolution Microscope Images Using a Gaussian Mixture Model and Morphological Processing

Studying the architecture of nuclear lamina networks is significantly important in biomedicine owing not only to their influence on the genome, but also because they are associated with several diseases. To save labor and time, an automated method for nuclear lamina network recognition and quantitative analysis is proposed for use with lattice structured illumination super-resolution microscope images in this study. This method is based on a Gaussian mixture model and morphological processing. It includes steps for target region generation, bias field correction, image segmentation, network connection, meshwork generation, and meshwork analysis. The effectiveness of the proposed method was confirmed by recognizing and quantitatively analyzing nuclear lamina networks in five images that are presented to show the method’s performance. The experimental results show that our algorithm achieved high accuracy in nuclear lamina network recognition and quantitative analysis, and the median face areas size of lamina networks from U2OS osteosarcoma cells are 0.3184 μm2.

Cross-scanning optical coherence tomography angiography for eye motion correction

We propose a cross-scanning optical coherence tomography (CS-OCT) system to correct eye motion artifacts in OCT angiography images. This system employs a dual-illumination configuration with two orthogonally polarized beams, each of which simultaneously perform raster scanning in perpendicular direction with each other over the same area. In the reference arm, a polarization delay unit is used to acquire the two orthogonally polarized interferograms with a single photo detector by introducing different optical delay lines. The two cross-scanned volume data are affected by the same eye motion but in two orthogonal directions. We developed a motion correction algorithm, which removes artifacts in the slow axis of each angiogram using the other and merges them through a nonrigid registration algorithm. In this manner, we obtained a motion-corrected angiogram within a single volume scanning time without additional eye-tracking devices.

Cooperative Low-Rank Models for Removing Stripe Noise From OCTA Images

Optical coherence tomography angiography (OCTA) is an emerging non-invasive imaging technique for imaging the microvasculature of the eye based on phase variance or amplitude decorrelation derived from repeated OCT images of the same tissue area. Stripe noise occurs during the OCTA acquisition process due to the involuntary movement of the eye. To remove the stripe noise (or 'destriping') effectively, we propose two novel image decomposition models to simultaneously destripe all the OCTA images of the same eye cooperatively: cooperative uniformity destriping (CUD) model and cooperative similarity destriping (CSD) model. Both the models consider stripe noise by low-rank constraint but in different ways: the CUD model assumes that stripe noise is identical across all the layers while the CSD model assumes that the stripe noise at different layers are different and have to be considered in the model. Compared to the CUD model, CSD is a more general solution for real OCTA images. An efficient solution (CSD+) is developed for model CSD to reduce the computational complexity. The models were extensively evaluated against state-of-the-art methods on both synthesized and real OCTA datasets. The experiments demonstrated not only the effectiveness of the CSD and CSD+ models in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) and CSD+ is twice faster than CSD, but also their beneficiary effect on the vessel segmentation of OCTA images. We expect our models will become a powerful tool for clinical applications.

Interplane bulk motion analysis and removal based on normalized cross-correlation in optical coherence tomography angiography

Bulk motion seriously degrades the image quality of optical coherence tomography angiography (OCTA). Conventional correction methods focus on in-plane displacement, while the bulk motion component perpendicular to B-scans also introduces noise. This work first presents an evaluation of this component using a specific scan protocol and an approximate expression derived from peak-normalized cross-correlation values, and then quantitatively assesses how interplane bulk motion noise reduce the sensitivity of cross-sectional angiograms. Finally, we developed a repetitive bulk motion correction method based on the estimated displacements and redundant volume scans. The correction does not require registration and angiogram reconstruction of low flow sensitivity frames, and the results of in vivo mice skin OCTA imaging experiments show that the proposed method can effectively reduce bulk motion noise caused by cardiac and respiratory motion and occasional shaking, and improve OCTA image quality, which has practical significance for clinical OCTA diagnosis and analysis.

Reconstruction of high-resolution 6×6-mm OCT angiograms using deep learning

Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3×3- or 6×6-mm. Compared to 3×3-mm angiograms with proper sampling density, 6×6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6×6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3×3-mm and 6×6-mm angiograms from the same eyes. The reconstructed 6×6-mm angiograms have significantly lower noise intensity, stronger contrast and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6×6-mm OCTA.

Sensorless adaptive-optics optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) can image the retinal blood flow but visualization of the capillary caliber is limited by the low lateral resolution. Adaptive optics (AO) can be used to compensate ocular aberrations when using high numerical aperture (NA), and thus improve image resolution. However, previously reported AO-OCTA instruments were large and complex, and have a small sub-millimeter field of view (FOV) that hinders the extraction of biomarkers with clinical relevance. In this manuscript, we developed a sensorless AO-OCTA prototype with an intermediate numerical aperture to produce depth-resolved angiograms with high resolution and signal-to-noise ratio over a 2 × 2 mm FOV, with a focal spot diameter of 6 µm, which is about 3 times finer than typical commercial OCT systems. We believe these parameters may represent a better tradeoff between resolution and FOV compared to large-NA AO systems, since the spot size matches better that of capillaries. The prototype corrects defocus, astigmatism, and coma using a figure of merit based on the mean reflectance projection of a slab defined with real-time segmentation of retinal layers. AO correction with the ability to optimize focusing in arbitrary retinal depths - particularly the plexuses in the inner retina - could be achieved in 1.35 seconds. The AO-OCTA images showed greater flow signal, signal-to-noise ratio, and finer capillary caliber compared to commercial OCTA. Projection artifacts were also reduced in the intermediate and deep capillary plexuses. The instrument reported here improves OCTA image quality without excessive sacrifice in FOV and device complexity, and thus may have potential for clinical translation.

High-resolution wide-field OCT angiography with a self-navigation method to correct microsaccades and blinks

In this study, we demonstrate a novel self-navigated motion correction method that suppresses eye motion and blinking artifacts on wide-field optical coherence tomographic angiography (OCTA) without requiring any hardware modification. Highly efficient GPU-based, real-time OCTA image acquisition and processing software was developed to detect eye motion artifacts. The algorithm includes an instantaneous motion index that evaluates the strength of motion artifact on en face OCTA images. Areas with suprathreshold motion and eye blinking artifacts are automatically rescanned in real-time. Both healthy eyes and eyes with diabetic retinopathy were imaged, and the self-navigated motion correction performance was demonstrated.

A feasibility study of OCT for anatomical and vascular phenotyping of mouse embryo

The embryo phenotyping of genetic murine model is invaluable when investigating functions of genes underlying embryonic development and birth defect. Although traditional imaging technologies such as ultrasound are very useful for evaluating phenotype of murine embryos, the use of advanced techniques for phenotyping is desirable to obtain more information from genetic research. This letter tests the feasibility of optical coherence tomography (OCT) as a high-throughput phenotyping tool for murine embryos. Three-dimensional OCT imaging is performed for live and cleared mouse embryos in the late developmental stage (embryonic day 17.5). By using a dynamic focusing method and OCT angiography (OCTA) approach, our OCT imaging of the embryo exhibits rapid and clean visualization of organ structures deeper than 5 mm and complex microvasculature of perfused blood vessels in the murine embryonic body. This demonstration suggests that OCT imaging can be useful for comprehensively assessing embryo anatomy and angiography of genetically engineered mice.

Real-time en-face Gabor optical coherence tomographic angiography on human skin using CUDA GPU

We recently proposed an optical coherence tomographic angiography (OCTA) algorithm, Gabor optical coherence tomographic angiography (GOCTA), which can extract microvascular signals from a spectral domain directly with lower computational complexity compared to other algorithms. In this manuscript, we combine a programmable swept source, an OCT complex signal detecting unit, and graphics process units (GPU) to achieve a real-time en-face GOCTA system for human skin microvascular imaging. The programmable swept source can balance the A-scan rate and the spectral tuning range; the polarization-modulation based complex signal detecting unit can double the imaging depth range, and the GPU can accelerate data processing. C++ and CUDA are used as the programming platform where five parallel threads are created for galvo-driving signal generation, data acquisition, data transfer, data processing, and image display, respectively. Two queues (for the raw data and en-face images, respectively) are used to improve the data exchange efficiency among different devices. In this study, the data acquisition time and data processing time for each 3D complex volume (256×304×608 pixels,) are 405.3 and 173.7 milliseconds respectively. To the best of our knowledge, this is the first time to show en-face microvascular images covering 3×3 mm² at a refresh rate of 2.5 Hz.

Gabor optical coherence tomographic angiography (GOCTA) (Part II): theoretical basis of sensitivity improvement and optimization for processing speed

We previously proposed a Gabor optical coherence tomography angiography (GOCTA) algorithm for spectral domain optical coherence tomography (SDOCT) to extract microvascular signals from spectral fringes directly, with speed improvement of 4 to 20 times over existing methods. In this manuscript, we explored the theoretical basis of GOCTA with comparison of experimental data using solid and liquid displacement sample targets, demonstrating that the majority of the GOCTA sensitivity advantage over speckle variance based techniques was in the small displacement range (< 10 ∼ 20 µm) of the moving target (such as red blood cells). We further normalized GOCTA signal by root-mean-square (RMS) of original fringes, achieving a more uniform image quality, especially at edges of blood vessels where slow flow could occur. Furthermore, by transecting the spectral fringes and using skipped convolution, the data processing speed could be further improved. We quantified the trade-off in signal-to-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) under various sub-spectral bands and found an optimized condition using 1/4 spectral band for minimal angiography image quality degradation, yet achieving a further 26.7 and 34 times speed improvement on GPU and CPU, respectively. Our optimized GOCTA algorithm has a speed advantage of over 140 times compared to existing speckle variance OCT (SVOCT) method.

Detection of Reduced Retinal Vessel Density in Eyes with Geographic Atrophy Secondary to Age-Related Macular Degeneration Using Projection-Resolved Optical Coherence Tomography Angiography

PURPOSE

To compare retinal vessel density in eyes with geographic atrophy (GA) secondary to age-related macular degeneration (AMD) to age-matched healthy eyes by using projection-resolved optical coherence tomography angiography (PR-OCTA).

DESIGN

Prospective cross-sectional study.

METHODS

Study participants underwent macular 3- × 3-mm OCTA scans with spectral domain OCTA. Reflectance-compensated retinal vessel densities were calculated on projection-resolved superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Quantitative analysis using normalized deviation compared the retinal vessel density in GA regions, 500-μm GA rim regions, and non-GA regions to similar macular locations in control eyes.

RESULTS

Ten eyes with GA and 10 control eyes were studied. Eyes with GA had significantly lower vessel density in the SVC (54.8 ± 2.4% vs. 60.8 ± 3.1%; P < 0.001), ICP (34.0 ± 1.5% vs. 37.3 ± 1.7%; P = 0.003) and DCP (24.4 ± 2.3% vs. 28.0 ± 2.3%; P < 0.001) than control eyes. Retinal vessel density within the GA region decreased significantly in SVC, ICP, and DCP. Retinal vessel density in the GA rim region decreased in SVC and ICP but not in DCP. The non-GA region did not significantly deviate from normal controls. Eyes with GA had significantly reduced photoreceptor layer thickness; but similar nerve fiber layer, ganglion cell complex, inner nuclear layer, and outer plexiform layer thickness.

CONCLUSIONS

Eyes with GA have reduced retinal vessel density in SVC, ICP, and DCP compared to those in controls. Loss is greatest within regions of GA. Vessel density may be more sensitive than retinal layer thickness measurement in the detection of inner retinal change in eyes with GA.

Handheld spectrally encoded coherence tomography and reflectometry for motion-corrected ophthalmic optical coherence tomography and optical coherence tomography angiography

Optical coherence tomography (OCT) is the gold standard for quantitative ophthalmic imaging. The majority of commercial and research systems require patients to fixate and be imaged in a seated upright position, which limits the ability to perform ophthalmic imaging in bedridden or pediatric patients. Handheld OCT devices overcome this limitation, but image quality often suffers due to a lack of real-time aiming and patient eye and photographer motion. We describe a handheld spectrally encoded coherence tomography and reflectometry (SECTR) system that enables simultaneous en face reflectance and cross-sectional OCT imaging. The handheld probe utilizes a custom double-pass scan lens for fully telecentric OCT scanning with a compact optomechanical design and a rapid-prototyped enclosure to reduce the overall system size and weight. We also introduce a variable velocity scan waveform that allows for simultaneous acquisition of densely sampled OCT angiography (OCTA) volumes and widefield reflectance images, which enables high-resolution vascular imaging with precision motion-tracking for volumetric motion correction and multivolumetric mosaicking. Finally, we demonstrate in vivo human retinal OCT and OCT angiography (OCTA) imaging using handheld SECTR on a healthy volunteer. Clinical translation of handheld SECTR will allow for high-speed, motion-corrected widefield OCT and OCTA imaging in bedridden and pediatric patients who may benefit ophthalmic disease diagnosis and monitoring.

Motion Correction in Optical Resolution Photoacoustic Microscopy

In this paper, we are proposing a novel motion correction algorithm for high-resolution OR-PAM imaging. Our algorithm combines a modified demons-based tracking approach with a newly developed multi-scale vascular feature matching method to track motion between adjacent B-scan images without needing any reference object. We first applied this algorithm to correct motion artifacts within one three-dimensional (3D) data segment of rat iris obtained with OR-PAM imaging. We then extended the application of this algorithm to correct motions to obtain vasculature imaging in the whole mouse back. In here, we stitched five adjacent 3D data segments (large field-of-view) obtained while changing the focus of OR-PAM differently for each subarea. The results showed that the motion artifacts of both large blood vessels and microvessels could be accurately corrected in both cases. Compared to the manually stitching method and the traditional SIFT algorithm, the algorithm proposed in this paper has better performance in stitching adjacent data segments. The high accuracy of the motion correction algorithm makes it valuable in OR-PAM for high-resolution imaging of large animals and for quantitative functional imaging.

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments.

High dynamic range optical coherence tomography angiography (HDR-OCTA)

The dynamic range of current optical coherence tomography (OCT) angiography (OCTA) images is limited by the fixed scanning intervals. High speed OCT devices introduce the possibility of extending the flow signal dynamic range. In this study, we created a novel scanning pattern for achieving high dynamic range (HDR)-OCTA with a superior scanning efficiency. We implemented a bidirectional, interleaved scanning pattern that is sensitive to different flow speeds by adjustable adjacent inter-scan time intervals. We found that an improved flow dynamic range can be achieved by generating 3 different B-scan time intervals using 3 repetitions.

Quality improvement of OCT angiograms with elliptical directional filtering

We present a method of OCT angiography (OCTA) data filtering for noise suppression and improved visualization of the retinal vascular networks in en face projection images. In our approach, we use a set of filters applied in three orthogonal axes in the three-dimensional (3-D) data sets. Minimization of artifacts generated in B-scan-wise data processing is accomplished by filtering the cross-sections along the slow scanning axis. A-scans are de-noised by axial filtering. The core of the method is the application of directional filtering to the C-scans, i.e. one-pixel thick sections of the 3-D data set, perpendicular to the direction of the scanning OCT beam. The method uses a concept of structuring, directional kernels of shapes matching the geometry of the image features. We use rotating ellipses to find the most likely local orientation of the vessels and use the best matching ellipses for median filtering of the C-scans. We demonstrate our approach in the imaging of a normal human eye with laboratory-grade spectral-domain OCT setup. The "field performance" is demonstrated in imaging of diabetic retinopathy cases with a commercial OCT device. The absolute complex differences method is used for the generation of OCTA images from the data collected in the most noise-wise unfavorable OCTA scanning regime-two frame scanning.

Invariant features-based automated registration and montage for wide-field OCT angiography

The field of view of optical coherence tomography angiography (OCTA) images of the retina can be increased by montaging consecutive scans acquired at different retinal regions. Given the dramatic variation in aberrations throughout the entire posterior pole region, it is challenging to achieve seamless merging with apparent capillary continuity across the boundaries between adjacent angiograms. For this purpose, we propose herein a method that performs automated registration of contiguous OCTA images based on invariant features and uses a novel montage algorithm. The invariant features were used to register the overlapping areas between adjacently located scans by estimating the affine transformation matrix needed to accurately stitch them. Then, the flow signal was compensated to homogenize the angiograms with different brightness and the joints were blended to generate a seamless, montaged wide-field angiogram. We evaluated the algorithm on normal and diabetic retinopathy eyes. The proposed method could montage the angiograms seamlessly and provided a wide-field of view of retinal vasculature.

Enhanced Quantification of Retinal Perfusion by Improved Discrimination of Blood Flow From Bulk Motion Signal in OCTA

Purpose

Quantification of optical coherence tomography angiography (OCTA) is confounded by the prevalence of bulk motion. We have previously developed a regression-based bulk motion subtraction (rb-BMS) algorithm that estimates bulk motion velocity and corrects for its effect on flow signal. Here, we aim to investigate its ability to improve the reliability of capillary density (CD) quantification.

Methods

Two spectral-domain systems (70-kHz Avanti/AngioVue and 68-kHz Cirrus/AngioPlex) acquired 6 × 6-mm OCTA scans. The rb-BMS algorithm was applied on each OCTA volume. Regression analysis of angiographic versus reflectance signal of avascular A-lines in B-frames was used to set an optimized reflectance-adjusted threshold for discriminating vascular versus nonvascular voxels. The CD was calculated from en face maximum projections of the superficial vascular complex in macular scans and the nerve fiber layer plexus in disc scans, excluding large vessels. The retinal signal strength (RSS) was calculated by averaging the logarithmic-scale OCT reflectance signal, and its correlation with CD was investigated.

Results

Eight healthy eyes were scanned with each instrument on 2 separate days. The rb-BMS algorithm improved within-visit repeatability and between-visit reproducibility of CD compared with a global-threshold measurement algorithm. Using the rb-BMS algorithm, the CD results were less affected by RSS and the population variation was reduced. Motion-induced line artifacts were also reduced.

Conclusions

The rb-BMS algorithm improved the reliability of perfusion quantification in OCTA on both Food and Drug Administration-cleared spectral-domain OCTA systems.

Translational Relevance

The rb-BMS method helped reduce the inter-scan variability by generating accurate vessel maps, improving the reliability of retinal perfusion quantification.

Maximum value projection produces better en face OCT angiograms than mean value projection

Optical coherence tomography angiography (OCTA) images rely on en face data projections for both qualitative and quantitative interpretation. Both maximum value and mean value projections are commonly used, and many researchers consider them essentially interchangeable approaches. On the contrary, we find that maximum value projection achieves a consistently higher signal-to-noise ratio and higher image contrast across multiple vascular layers, in both healthy eyes and for each disease examined.

Intraframe motion correction for raster-scanned adaptive optics images using strip-based cross-correlation lag biases

In retinal raster imaging modalities, fixational eye movements manifest as image warp, where the relative positions of the beam and retina change during the acquisition of single frames. To remove warp artifacts, strip-based registration methods-in which fast-axis strips from target images are registered to a reference frame-have been applied in adaptive optics (AO) scanning light ophthalmoscopy (SLO) and optical coherence tomography (OCT). This approach has enabled object tracking and frame averaging, and methods have been described to automatically select reference frames with minimal motion. However, inconspicuous motion artifacts may persist in reference frames and propagate themselves throughout the processes of registration, tracking, and averaging. Here we test a previously proposed method for removing movement artifacts in reference frames, using biases in stripwise cross-correlation statistics. We applied the method to synthetic retinal images with simulated eye motion artifacts as well as real AO-SLO images of the cone mosaic and volumetric AO-OCT images, both affected by eye motion. In the case of synthetic images, the method was validated by direct comparison with motion-free versions of the images. In the case of real AO images, performance was validated by comparing the correlation of uncorrected images with that of corrected images, by quantifying the effect of motion artifacts on the image power spectra, and by qualitative examination of AO-OCT B-scans and en face projections. In all cases, the proposed method reduced motion artifacts and produced more faithful images of the retina.

Automated motion-artifact correction in an OCTA image using tensor voting approach

Optical coherence tomography angiography (OCTA) is a promising tool for imaging subsurface microvascular networks owing to its micron-level resolution and high sensitivity. However, it is not uncommon that OCTA imaging suffers from strip artifacts induced by tissue motion. Although various algorithms for motion correction have been reported, a method that enables motion correction on a single en face OCTA image remains a challenge. In this study, we propose a motion correction approach based on microvasculature detection and broken gap filling. Unlike previous methods using registration to restore disturbed vasculature during motion artifact removal, tensor voting is performed in an individual projected image to connect the broken vasculature. Both simulation and in vivo 3D OCTA imaging of the mouse bladder are performed to validate the effectiveness of this method. A comparison of in vivo images before and after motion correction shows that our method effectively corrects tissue motion artifacts while preserving the continuity of vasculature network. Furthermore, in vivo results of this technique are presented to demonstrate its utility for imaging tumor angiogenesis in the mouse bladder.

Spectrally encoded coherence tomography and reflectometry: Simultaneous en face and cross-sectional imaging at 2 gigapixels per second

Non-invasive biological imaging is crucial for understanding in vivo structure and function. Optical coherence tomography (OCT) and reflectance confocal microscopy are two of the most widely used optical modalities for exogenous contrast-free, high-resolution, three-dimensional imaging in non-fluorescent scattering tissues. However, sample motion remains a critical barrier to raster-scanned acquisition and reconstruction of wide-field anatomically accurate volumetric datasets. We introduce spectrally encoded coherence tomography and reflectometry (SECTR), a high-speed, multimodality system for simultaneous OCT and spectrally encoded reflectance (SER) imaging. SECTR utilizes a robust system design consisting of shared optical relays, scanning mirrors, swept laser and digitizer to achieve the fastest reported in vivo multimodal imaging rate of 2 gigapixels per second. Our optical design and acquisition scheme enable spatiotemporally co-registered acquisition of OCT cross-sections simultaneously with en face SER images for multivolumetric mosaicking. Complementary axial and lateral translation and rotation are extracted from OCT and SER data, respectively, for full volumetric estimation of sample motion with micron spatial and millisecond temporal resolution.

Spectrally encoded coherence tomography and reflectometry: Simultaneous en face and cross-sectional imaging at 2 gigapixels per second

Non-invasive biological imaging is crucial for understanding in vivo structure and function. Optical coherence tomography (OCT) and reflectance confocal microscopy are two of the most widely used optical modalities for exogenous contrast-free, high-resolution, three-dimensional imaging in non-fluorescent scattering tissues. However, sample motion remains a critical barrier to raster-scanned acquisition and reconstruction of wide-field anatomically accurate volumetric datasets. We introduce spectrally encoded coherence tomography and reflectometry (SECTR), a high-speed, multimodality system for simultaneous OCT and spectrally encoded reflectance (SER) imaging. SECTR utilizes a robust system design consisting of shared optical relays, scanning mirrors, swept laser and digitizer to achieve the fastest reported in vivo multimodal imaging rate of 2 gigapixels per second. Our optical design and acquisition scheme enable spatiotemporally co-registered acquisition of OCT cross-sections simultaneously with en face SER images for multivolumetric mosaicking. Complementary axial and lateral translation and rotation are extracted from OCT and SER data, respectively, for full volumetric estimation of sample motion with micron spatial and millisecond temporal resolution.

Eye-motion-corrected optical coherence tomography angiography using Lissajous scanning

To correct eye motion artifacts in en face optical coherence tomography angiography (OCT-A) images, a Lissajous scanning method with subsequent software-based motion correction is proposed. The standard Lissajous scanning pattern is modified to be compatible with OCT-A and a corresponding motion correction algorithm is designed. The effectiveness of our method was demonstrated by comparing en face OCT-A images with and without motion correction. The method was further validated by comparing motion-corrected images with scanning laser ophthalmoscopy images, and the repeatability of the method was evaluated using a checkerboard image. A motion-corrected en face OCT-A image from a blinking case is presented to demonstrate the ability of the method to deal with eye blinking. Results show that the method can produce accurate motion-free en face OCT-A images of the posterior segment of the eye in vivo.

Gabor optical coherence tomographic angiography (GOCTA) (Part I): human retinal imaging in vivo

Recently, parallel high A-line speed and wide field imaging for optical coherence tomography angiography (OCTA) has become more prevalent, resulting in a dramatic increase of data quantity which poses a challenge for real time imaging even for GPU in data processing. In this manuscript, we propose a new OCTA processing technique, Gabor optical coherence tomographic angiography (GOCTA), for label-free human retinal angiography imaging. In spectral domain optical coherence tomography (SDOCT), k-space resampling and Fourier transform (FFT) are required for the entire data set of interference fringes to calculate blood flow information in previous OCTA algorithms, which are computationally intensive. As adults' eye anterior-posterior radii are nearly constant, only 3 A-scan lines need to be processed to obtain the gross orientation of the retina by using a sphere model. Subsequently, the en face microvascular images can be obtained by using the GOCTA algorithm from interference fringes directly without the steps of k-space resampling, numerical dispersion compensation, FFT, and maximum (mean) projection, resulting in a significant improvement of the data processing speed by 4 to 20 times faster than the existing methods. GOCTA is potentially suitable for SDOCT systems in en face preview applications requiring real-time microvascular imaging.

Extended axial imaging range, widefield swept source optical coherence tomography angiography

We developed a high-speed, swept source OCT system for widefield OCT angiography (OCTA) imaging. The system has an extended axial imaging range of 6.6 mm. An electrical lens is used for fast, automatic focusing. The recently developed split-spectrum amplitude and phase-gradient angiography allow high-resolution OCTA imaging with only two B-scan repetitions. An improved post-processing algorithm effectively removed trigger jitter artifacts and reduced noise in the flow signal. We demonstrated high contrast 3 mm×3 mm OCTA image with 400×400 pixels acquired in 3 seconds and high-definition 8 mm×6 mm and 12 mm×6 mm OCTA images with 850×400 pixels obtained in 4 seconds. A widefield 8 mm×11 mm OCTA image is produced by montaging two 8 mm×6 mm scans. An ultra-widefield (with a maximum of 22 mm along both vertical and horizontal directions) capillary-resolution OCTA image is obtained by montaging six 12 mm×6 mm scans.

Sensitivity and Specificity of OCT Angiography to Detect Choroidal Neovascularization

PURPOSE

To determine the sensitivity and specificity of optical coherence tomography angiography (OCTA) in the detection of choroidal neovascularization (CNV) in age-related macular degeneration (AMD).

DESIGN

Prospective case series.

SUBJECTS

Prospective series of seventy-two eyes were studied, which included eyes with treatment-naive CNV due to AMD, non-neovascular AMD, and normal controls.

METHODS

All eyes underwent OCTA with a spectral domain (SD) OCT (Optovue, Inc.). The 3D angiogram was segmented into separate en face views including the inner retinal angiogram, outer retinal angiogram, and choriocapillaris angiogram. Detection of abnormal flow in the outer retina served as candidate CNV with OCTA. Masked graders reviewed structural OCT alone, en face OCTA alone, and en face OCTA combined with cross-sectional OCTA for the presence of CNV.

MAIN OUTCOME MEASURE

The sensitivity and specificity of CNV detection compared to the gold standard of fluorescein angiography (FA) and OCT was determined for structural SD-OCT alone, en face OCTA alone, and with en face OCTA combined with cross-sectional OCTA.

RESULTS

Of 32 eyes with CNV, both graders identified 26 true positives with en face OCTA alone, resulting in a sensitivity of 81.3%. Four of the 6 false negatives had large subretinal hemorrhage (SRH) and sensitivity improved to 94% for both graders if eyes with SRH were excluded. The addition of cross-sectional OCTA along with en face OCTA improved the sensitivity to 100% for both graders. Structural OCT alone also had a sensitivity of 100%. The specificity of en face OCTA alone was 92.5% for grader A and 97.5% for grader B. The specificity of structural OCT alone was 97.5% for grader A and 85% for grader B. Cross-sectional OCTA combined with en face OCTA had a specificity of 97.5% for grader A and 100% for grader B.

CONCLUSIONS

Sensitivity and specificity for CNV detection with en face OCTA combined with cross-sectional OCTA approaches that of the gold standard of FA with OCT, and it is better than en face OCTA alone. Structural OCT alone has excellent sensitivity for CNV detection. False positives from structural OCT can be mitigated with the addition of flow information with OCTA.

Automatic motion correction for in vivo human skin optical coherence tomography angiography through combined rigid and nonrigid registration

When using optical coherence tomography angiography (OCTA), the development of artifacts due to involuntary movements can severely compromise the visualization and subsequent quantitation of tissue microvasculatures. To correct such an occurrence, we propose a motion compensation method to eliminate artifacts from human skin OCTA by means of step-by-step rigid affine registration, rigid subpixel registration, and nonrigid B-spline registration. To accommodate this remedial process, OCTA is conducted using two matching all-depth volume scans. Affine transformation is first performed on the large vessels of the deep reticular dermis, and then the resulting affine parameters are applied to all-depth vasculatures with a further subpixel registration to refine the alignment between superficial smaller vessels. Finally, the coregistration of both volumes is carried out to result in the final artifact-free composite image via an algorithm based upon cubic B-spline free-form deformation. We demonstrate that the proposed method can provide a considerable improvement to the final en face OCTA images with substantial artifact removal. In addition, the correlation coefficients and peak signal-to-noise ratios of the corrected images are evaluated and compared with those of the original images, further validating the effectiveness of the proposed method. We expect that the proposed method can be useful in improving qualitative and quantitative assessment of the OCTA images of scanned tissue beds.

Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography

We developed an algorithm to remove decorrelation noise due to bulk motion in optical coherence tomography angiography (OCTA) of the posterior eye. In this algorithm, OCTA B-frames were divided into segments within which the bulk motion velocity could be assumed to be constant. This velocity was recovered using linear regression of decorrelation versus the logarithm of reflectance in axial lines (A-lines) identified as bulk tissue by percentile analysis. The fitting parameters were used to calculate a reflectance-adjusted upper bound threshold for bulk motion decorrelation. Below this threshold, voxels are identified as non-flow tissue, their flow values are set to zeros. Above this threshold, the voxels are identified as flow voxels and bulk motion velocity is subtracted from each using a nonlinear decorrelation-velocity relationship previously established in laboratory flow phantoms. Compared to the simpler median-subtraction method, the regression-based bulk motion subtraction improved angiogram signal-to-noise ratio, contrast, vessel density repeatability, and bulk motion noise cleanup in the foveal avascular zone, while preserving the connectivity of the vascular networks in the angiogram.

Handheld optical coherence tomography angiography

We developed a handheld optical coherence tomography angiography (OCTA) system using a 100-kHz swept-source laser. The handheld probe weighs 0.4 kg and measures 20.6 × 12.8 × 4.6 cm³. The system has dedicated features for handheld operation. The probe is equipped with a mini iris camera for easy alignment. Real-time display of the en face OCT and cross-sectional OCT images in the system allows accurately locating the imaging target. Fast automatic focusing was achieved by an electrically tunable lens controlled by a golden-section search algorithm. An extended axial imaging range of 6 mm allows easy alignment. A registration algorithm using cross-correlation to register adjacent OCT B-frames with propagation from the central frame was used to effectively minimize motion artifacts in volumetric OCTA images captured in relatively short durations of 1 and 2.1 seconds. 2.5 × 2.5 mm (200 × 200 pixels) and 3.5 × 3.5 mm (300 × 300 pixels) retinal angiograms were demonstrated on two awake adult human subjects without the use of any mydriatic eye drops.

Strip-based registration of serially acquired optical coherence tomography angiography

The visibility of retinal microvasculature in optical coherence tomography angiography (OCT-A) images is negatively affected by the small dimension of the capillaries, pulsatile blood flow, and motion artifacts. Serial acquisition and time-averaging of multiple OCT-A images can enhance the definition of the capillaries and result in repeatable and consistent visualization. We demonstrate an automated method for registration and averaging of serially acquired OCT-A images. Ten OCT-A volumes from six normal control subjects were acquired using our prototype 1060-nm swept source OCT system. The volumes were divided into microsaccade-free en face angiogram strips, which were affine registered using scale-invariant feature transform keypoints, followed by nonrigid registration by pixel-wise local neighborhood matching. The resulting averaged images were presented of all the retinal layers combined, as well as in the superficial and deep plexus layers separately. The contrast-to-noise ratio and signal-to-noise ratio of the angiograms with all retinal layers (reported as average ± standard deviation ) increased from 0.52 ± 0.22 and 19.58 ± 4.04 ?? dB for a single image to 0.77 ± 0.25 and 25.05 ± 4.73 ?? dB , respectively, for the serially acquired images after registration and averaging. The improved visualization of the capillaries can enable robust quantification and study of minute changes in retinal microvasculature.