Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Significance: Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. Aim: We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. Approach: We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). Results: The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. Conclusions: The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

Granzyme B degrades extracellular matrix and promotes inflammation and choroidal neovascularization.

Age-related macular degeneration (AMD) is a common retinal neurodegenerative disease among the elderly. Neovascular AMD (nAMD), a leading cause of AMD-related blindness, involves choroidal neovascularization (CNV), which can be suppressed by anti-angiogenic treatments. However, current CNV treatments do not work in all nAMD patients. Here we investigate a novel target for AMD. Granzyme B (GzmB) is a serine protease that promotes aging, chronic inflammation and vascular permeability through the degradation of the extracellular matrix (ECM) and tight junctions. Extracellular GzmB is increased in retina pigment epithelium (RPE) and mast cells in the choroid of the healthy aging outer retina. It is further increased in donor eyes exhibiting features of nAMD and CNV. Here, we show in RPE-choroidal explant cultures that exogenous GzmB degrades the RPE-choroid ECM, promotes retinal/choroidal inflammation and angiogenesis while diminishing anti-angiogenic factor, thrombospondin-1 (TSP-1). The pharmacological inhibition of either GzmB or mast-cell degranulation significantly reduces choroidal angiogenesis. In line with our in vitro data, GzmB-deficiency reduces the extent of laser-induced CNV lesions and the age-related deterioration of electroretinogram (ERG) responses in mice. These findings suggest that targeting GzmB, a serine protease with no known endogenous inhibitors, may be a potential novel therapeutic approach to suppress CNV in nAMD.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Polarization-Diversity Optical Coherence Tomography Assessment of Choroidal Nevi.

Purpose: The purpose of this study was to demonstrate the utility of polarization-diversity optical coherence tomography (PD-OCT), a noninvasive imaging technique with melanin-specific contrast, in the quantitative and qualitative assessment of choroidal nevi. Methods: Nevi were imaged with a custom-built 55-degree field-of-view (FOV) 400 kHz PD-OCT system. Imaging features on PD-OCT were compared to those on fundus photography, auto-fluorescence, ultrasound, and non-PD-OCT images. Lesions were manually segmented for size measurement and metrics for objective assessment of melanin distributions were calculated, including degree of polarization uniformity (DOPU), attenuation coefficient, and melanin occupancy rate (MOR). Results: We imaged 17 patients (mean age = 69.5 years, range = 37-90) with 11 pigmented, 3 non-pigmented, and 3 mixed pigmentation nevi. Nevi with full margin acquisition had an average longest basal diameter of 5.1 mm (range = 2.99-8.72 mm) and average height of 0.72 mm (range = 0.37 mm-2.09 mm). PD-OCT provided clear contrast of choroidal melanin content, distribution, and delineation of nevus margins for melanotic nevi. Pigmented nevi were found to have lower DOPU, higher attenuation coefficient, and higher MOR than non-pigmented lesions. Melanin content on PD-OCT was consistent with pigmentation on fundus in 15 of 17 nevi (88%). Conclusions: PD-OCT allows objective assessment of choroidal nevi melanin content and distribution. In addition, melanin-specific contrast by PD-OCT enables clear nevus margin delineation and may improve serial growth surveillance. Further investigation is needed to determine the clinical significance and prognostic value of melanin characterization by PD-OCT in the evaluation of choroidal nevi.

Segmentation-guided domain adaptation and data harmonization of multi-device retinal optical coherence tomography using cycle-consistent generative adversarial networks.

BACKGROUND: Medical images such as Optical Coherence Tomography (OCT) images acquired from different devices may show significantly different intensity profiles. An automatic segmentation model trained on images from one device may perform poorly when applied to images acquired using another device, resulting in a lack of generalizability. This study addresses this issue using domain adaptation methods improved by Cycle-Consistent Generative Adversarial Networks (CycleGAN), especially when the ground-truth labels are only available in the source domain. METHODS: A two-stage pipeline is proposed to generate segmentation in the target domain. The first stage involves the training of a state-of-the-art segmentation model in the source domain. The second stage aims to adapt the images from the target domain to the source domain. The adapted target domain images are segmented using the model in the first stage. Ablation tests were performed with integration of different loss functions, and the statistical significance of these models is reported. Both the segmentation performance and the adapted image quality metrics were evaluated. RESULTS: Regarding the segmentation Dice score, the proposed model ssppg achieves a significant improvement of 46.24% compared to without adaptation and reaches 87.4% of the upper limit of the segmentation performance. Furthermore, image quality metrics, including FID and KID scores, indicate that adapted images with better segmentation also have better image qualities. CONCLUSION: The proposed method demonstrates the effectiveness of segmentation-driven domain adaptation in retinal imaging processing. It reduces the labor cost of manual labeling, incorporates prior anatomic information to regulate and guide domain adaptation, and provides insights into improving segmentation qualities in image domains without labels.

Image-based cross-calibration method for multiple spectrometer-based OCT.

A fast and practical computational cross-calibration of multiple spectrometers is described. A signal correlation matrix (CM) can be constructed from paired B-scans in a multiple-spectrometer optical coherence tomography (OCT), where the wavelength-corresponding pixels are indicated by high cross correlation. The CM can be used to either guide the physical alignment of spectrometers or to numerically match the spectra in the post-process. The performance is comparable to the previously reported optimization approach, as demonstrated by the mirror tests, qualitative comparison of OCT and optical coherence tomography angiography (OCTA) images, and quantitative comparison of image metrics.

Corneal imaging with blue-light optical coherence microscopy.

Corneal imaging is important for the diagnostic and therapeutic evaluation of many eye diseases. Optical coherence tomography (OCT) is extensively used in ocular imaging due to its non-invasive and high-resolution volumetric imaging characteristics. Optical coherence microscopy (OCM) is a technical variation of OCT that can image the cornea with cellular resolution. Here, we demonstrate a blue-light OCM as a low-cost and easily reproducible system to visualize corneal cellular structures such as epithelial cells, endothelial cells, keratocytes, and collagen bundles within stromal lamellae. Our blue-light OCM system achieved an axial resolution of 12 µm in tissue over a 1.2 mm imaging depth, and a lateral resolution of 1.6 µm over a field of view of 750 µm × 750 µm.

Numerical calibration method for a multiple spectrometer-based OCT system.

The present paper introduces a numerical calibration method for the easy and practical implementation of multiple spectrometer-based spectral-domain optical coherence tomography (SD-OCT) systems. To address the limitations of the traditional hardware-based spectrometer alignment across more than one spectrometer, we applied a numerical spectral calibration algorithm where the pixels corresponding to the same wavelength in each unit are identified through spatial- and frequency-domain interferometric signatures of a mirror sample. The utility of dual spectrometer-based SD-OCT imaging is demonstrated through in vivo retinal imaging at two different operation modes with high-speed and dual balanced acquisitions, respectively, in which the spectral alignment is critical to achieve improved retinal image data without any artifacts caused by misalignment of the spectrometers.

Clinical explainable differential diagnosis of polypoidal choroidal vasculopathy and age-related macular degeneration using deep learning.

BACKGROUND: This study aims to achieve an automatic differential diagnosis between two types of retinal pathologies with similar pathological features - Polypoidal choroidal vasculopathy (PCV) and wet age-related macular degeneration (AMD) from volumetric optical coherence tomography (OCT) images, and identify clinically-relevant pathological features, using an explainable deep-learning-based framework. METHODS: This is a retrospective study with data from a cross-sectional cohort. The OCT volume of 73 eyes from 59 patients was included in this study. Disease differentiation was achieved through single-B-scan-based classification followed by a volumetric probability prediction aggregation step. We compared different labeling strategies with and without identifying pathological B-scans within each OCT volume. Clinical interpretability was achieved through normalized aggregation of B-scan-based saliency maps followed by maximum-intensity-projection onto the en face plane. We derived the PCV score from the proposed differential diagnosis framework with different labeling strategies. The en face projection of saliency map was validated with the pathologies identified in Indocyanine green angiography (ICGA). RESULTS: Model trained with both labeling strategies achieved similar level differentiation power (>90%), with good correspondence between pathological features detected from the projected en face saliency map and ICGA. CONCLUSIONS: This study demonstrated the potential clinical application of non-invasive differential diagnosis using AI-driven OCT-based analysis, with minimal requirement of labeling efforts, along with clinical explainability achieved through automatically detected disease-related pathologies.

ADAPTIVE OPTICS OPTICAL COHERENCE TOMOGRAPHY IN A CASE OF ACUTE ZONAL OCCULT OUTER RETINOPATHY.

PURPOSE: To report a case of acute zonal occult outer retinopathy in which adaptive optics (AO) facilitated visualization of abnormal photoreceptors previously thought to be in an area of normal retina on conventional optical coherence tomography (OCT). METHODS: Case report. RESULTS: A 51-year-old woman presents with 11-month history of photopsias and scotoma in the temporal visual field of her left eye. Ocular imaging including fluorescein angiography, fundus autofluorescence and OCT suggested the diagnosis of acute zonal occult outer retinopathy in the left eye. Adaptive optics optical coherence tomography (AO-OCT) revealed photoreceptor abnormalities not previously identified in conventional OCT, in areas apparently normal on multimodal imaging. On enface and cross-sectional AO-OCT, round and evenly spaced hyperreflectivity corresponding to normal cone mosaic (Pattern 1) was adjacent to unevenly and disrupted cone hyperreflectivity (Pattern 2) and areas with hyporeflectivity or no cone reflectivity (Pattern 3). Cross-sectional AO-OCT of Patterns 2 and 3 also revealed attenuation of ellipsoid zone with loss of interdigitation zone. CONCLUSION: Adaptive optics OCT documented cone photoreceptors in finer details than conventional OCT and revealed early changes in a patient with acute zonal occult outer retinopathy, in an area of the retina thought to be normal on conventional multimodal imaging. These findings may provide important insight into pathogenesis and progression of the disease.

Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification.

Optical coherence tomography (OCT) and OCT angiography (OCT-A) may benefit the screening of diabetic retinopathy (DR). This study investigated the effect of laterally subsampling OCT/OCT-A en face scans by up to a factor of 8 when using deep neural networks for automated referable DR classification. There was no significant difference in the classification performance across all evaluation metrics when subsampling up to a factor of 3, and only minimal differences up to a factor of 8. Our findings suggest that OCT/OCT-A can reduce the number of samples (and hence the acquisition time) for a volume for a given field of view on the retina that is acquired for rDR classification.

Phase-corrected buffer averaging for enhanced OCT angiography using FDML laser.

Megahertz-rate optical coherence tomography angiography (OCTA) is highly anticipated as an ultrafast imaging tool in clinical settings. However, shot-noise-limited sensitivity is inevitably reduced in high-speed imaging systems. In this Letter, we present a coherent buffer averaging technique for use with a Fourier-domain mode-locked (FDML) laser to improve OCTA contrast at 1060 nm MHz-rate retinal imaging. Full characterization of spectral variations among the FDML buffers and a numerical correction method are also presented, with the results demonstrating a 10-fold increase in the phase alignment among buffers. Coherent buffer averaging provided better OCTA contrast than the conventional multi-frame averaging approach with a faster acquisition time.

Investigation of the Peripapillary Choriocapillaris in Normal Tension Glaucoma, Primary Open-angle Glaucoma, and Control Eyes.

PRECIS: The peripapillary choriocapillaris (CC) was observed to be significantly impaired in normal tension glaucoma (NTG) subjects compared with normal controls using optical coherence tomography angiography (OCTA). PURPOSE: The aim was to quantitatively evaluate the peripapillary CC in NTG, primary open-angle glaucoma (POAG), and control eyes using OCTA. MATERIALS AND METHODS: Ninety eyes (30 controls, 30 NTG, and 30 POAG) from 73 patients were imaged using the Zeiss Plex Elite 9000. Five repeat 3×3 mm OCTA scans were acquired both nasally and temporally to the optic disc and subsequently averaged. Four CC flow deficit (FD) measures were calculated using the fuzzy C-means approach: FD density (FDD), mean FD size (MFDS), FD number (FDN), and FD area (FDA). RESULTS: Temporal NTG CC parameters were associated with visual field index and mean deviation (P<0.05). The control group showed a significantly lower nasal FDD (nasal: 3.79±1.26%, temporal: 4.48±1.73%, P=0.03), FDN (nasal: 156.43±38.44, temporal: 178.40±45.68, P=0.02), and FDA (nasal: 0.22±0.08, temporal: 0.26±0.10, P=0.03) when compared with temporal optic disc. The NTG group showed a significantly higher FDD (NTG: 5.04±2.38%, control: 3.79±1.26%, P=0.03), FDN (NTG: 185.90±56.66, control: 156.43±38.44, P=0.04), and FDA (NTG: 0.30±0.14 mm2, control: 0.22±0.08 mm2, P=0.03) nasal to the optic disc compared with controls. CONCLUSIONS: Association between CC parameters and glaucoma severity in NTG, but not POAG subjects, suggests vascular abnormalities may be a potential factor in the multifactorial process of glaucoma damage in NTG patients.

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.

Semi-supervised deep learning based 3D analysis of the peripapillary region.

Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch's membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.

Quantitative multi-contrast in vivo mouse imaging with polarization diversity optical coherence tomography and angiography.

Retinal microvasculature and the retinal pigment epithelium (RPE) play vital roles in maintaining the health and metabolic activity of the eye. Visualization of these retina structures is essential for pre-clinical studies of vision-robbing diseases, such as age-related macular degeneration (AMD). We have developed a quantitative multi-contrast polarization diversity OCT and angiography (QMC-PD-OCTA) system for imaging and visualizing pigment in the RPE using degree of polarization uniformity (DOPU), along with flow in the retinal capillaries using OCT angiography (OCTA). An adaptive DOPU averaging kernel was developed to increase quantifiable values from visual data, and QMC en face images permit simultaneous visualization of vessel location, depth, melanin region thickness, and mean DOPU values, allowing rapid identification and differentiation of disease symptoms. The retina of five different mice strains were measured in vivo, with results demonstrating potential for pre-clinical studies of retinal disorders.

Non-invasive cellular-resolution retinal imaging with two-photon excited fluorescence.

Two-photon excited fluorescence (TPEF) imaging of the retina is a developing technique that provides non-invasive compound-specific measurements from the retina. In this report, we demonstrate high-resolution TPEF imaging of the mouse retina using sensorless adaptive optics (SAO) and optical coherence tomography (OCT). A single near-infrared light source was used for simultaneous multi-modal imaging with OCT and TPEF. The image-based SAO could be performed using the en face OCT or the TPEF for aberration correction. Our results demonstrate OCT and TPEF for angiography. Also, we demonstrate non-invasive cellular-resolution imaging of fluorescently labelled cells and the Retinal Pigment Epithelium (RPE) mosaic.

Multi-scale and -contrast sensorless adaptive optics optical coherence tomography.

BACKGROUND: The roles of the retinal microvasculature and the retinal pigment epithelium (RPE) in maintaining the health and metabolic activity of the retina lend great clinical value to their high-resolution visualization. METHODS: By integrating polarization diversity detection (PDD) into multi-scale and -contrast sensorless adaptive optics optical coherence tomography (MSC-SAO-OCT), we have developed a novel multi-contrast SAO OCT system for imaging pigment in the RPE as well as flow in the retinal capillaries using OCT angiography (OCTA). Aberration correction was performed based on the image quality using transmissive deformable optical elements. RESULTS: MSC-SAO-OCTA imaging was performed at multiple fields-of-view (FOVs) with adjustable numerical aperture (NA). Retinal flow and RPE structural images for in vivo healthy and pathological human posterior eyes were demonstrated to show clinical feasibility of the system. CONCLUSIONS: High-resolution imaging of retinal vasculature at both large and small FOVs, as well as characterization of RPE topology and deformation, enables more sophisticated and concise investigation of retinal pathologies for in vivo human imaging. MSC imaging may permit detection and analysis of even subtle deformations in the RPE layer using a single instrument.