Visual functions and multimodal imaging of patients with idiopathic focal choroidal excavation.

This study aimed to evaluate visual function and perform multimodal imaging on patients with focal choroidal excavation without any chorioretinal disease (idiopathic focal choroidal excavation [iFCE]). Seventeen eyes of 15 patients with iFCE (8 men, 7 women; mean ± standard deviation age, 56.0 ± 10.8 years) were assessed for visual function including visual acuity, metamorphopsia, aniseikonia, and retinal sensitivity. Multimodal imaging included optical coherence tomography (OCT), fundus autofluorescence (FAF), and OCT angiography. This study found that the maximum width and depth of the excavation were 597 ± 330 (238-1809) µm and 123 ± 45 (66-231) µm, respectively, and that FAF showed normal or hypoautofluorescence corresponding to iFCE. The fundus examination findings were stable during the follow-up period (96 ± 48 months). None of the eyes showed any abnormalities in central retinal sensitivity or aniseikonia. Metamorphopsia was detected using Amsler grid testing and M-CHARTS in two eyes. Therefore, this study is the first to quantitatively and qualitatively study metamorphopsia of patients with iFCE. Our results showed that most patients with iFCE did not have visual impairments, despite the presence of morphological changes in the outer retina and choroid.

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.

Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.

Applicability of Oculomics for Individual Risk Prediction: Repeatability and Robustness of Retinal Fractal Dimension Using DART and AutoMorph.

Purpose: To investigate whether fractal dimension (FD)-based oculomics could be used for individual risk prediction by evaluating repeatability and robustness. Methods: We used two datasets: "Caledonia," healthy adults imaged multiple times in quick succession for research (26 subjects, 39 eyes, 377 color fundus images), and GRAPE, glaucoma patients with baseline and follow-up visits (106 subjects, 196 eyes, 392 images). Mean follow-up time was 18.3 months in GRAPE; thus it provides a pessimistic lower bound because vasculature could change. FD was computed with DART and AutoMorph. Image quality was assessed with QuickQual, but no images were initially excluded. Pearson, Spearman, and intraclass correlation (ICC) were used for population-level repeatability. For individual-level repeatability, we introduce measurement noise parameter λ, which is within-eye standard deviation (SD) of FD measurements in units of between-eyes SD. Results: In Caledonia, ICC was 0.8153 for DART and 0.5779 for AutoMorph, Pearson/Spearman correlation (first and last image) 0.7857/0.7824 for DART, and 0.3933/0.6253 for AutoMorph. In GRAPE, Pearson/Spearman correlation (first and next visit) was 0.7479/0.7474 for DART, and 0.7109/0.7208 for AutoMorph (all P < 0.0001). Median λ in Caledonia without exclusions was 3.55% for DART and 12.65% for AutoMorph and improved to up to 1.67% and 6.64% with quality-based exclusions, respectively. Quality exclusions primarily mitigated large outliers. Worst quality in an eye correlated strongly with λ (Pearson 0.5350-0.7550, depending on dataset and method, all P < 0.0001). Conclusions: Repeatability was sufficient for individual-level predictions in heterogeneous populations. DART performed better on all metrics and might be able to detect small, longitudinal changes, highlighting the potential of robust methods.

Assessment of retinal and choroidal microvasculature in night shift medical workers by OCT angiography.

This study evaluated retinal and choroidal microvascular changes in night shift medical workers and its correlation with melatonin level. Night shift medical workers (group A, 25 workers) and non-night shift workers (group B, 25 workers) were recruited. The images of macula and optic nerve head were obtained by swept-source OCT-angiography. Vessel density of retina, choriocapillaris (CC), choriocapillaris flow deficit (CC FD), choroidal thickness (CT) and choroidal vascularity index (CVI) were measured. 6-sulfatoxymelatonin concentration was analyzed from the morning urine. CC FD and CVI were significantly decreased and CT was significantly increased in group A (all P < 0.05). 6-sulfatoxymelatonin concentration was significantly lower in group A (P < 0.05), which was significantly positively correlated with CC FD size (r = 0.318, P = 0.024) and CVI of the most regions (maximum r-value was 0.482, P < 0.001), and was significantly negatively associated with CT of all regions (maximum r-value was - 0.477, P < 0.001). In night shift medical workers, the reduction of melatonin was significantly correlated with CT thickening, CVI reduction and CC FD reduction, which suggested that they might have a higher risk of eye diseases. CC FD could be a sensitive and accurate indicator to reflect CC perfusion.

Deep Neural Networks for Automated Outer Plexiform Layer Subsidence Detection on Retinal OCT of Patients With Intermediate AMD.

Purpose: The subsidence of the outer plexiform layer (OPL) is an important imaging biomarker on optical coherence tomography (OCT) associated with early outer retinal atrophy and a risk factor for progression to geographic atrophy in patients with intermediate age-related macular degeneration (AMD). Deep neural networks (DNNs) for OCT can support automated detection and localization of this biomarker. Methods: The method predicts potential OPL subsidence locations on retinal OCTs. A detection module (DM) infers bounding boxes around subsidences with a likelihood score, and a classification module (CM) assesses subsidence presence at the B-scan level. Overlapping boxes between B-scans are combined and scored by the product of the DM and CM predictions. The volume-wise score is the maximum prediction across all B-scans. One development and one independent external data set were used with 140 and 26 patients with AMD, respectively. Results: The system detected more than 85% of OPL subsidences with less than one false-positive (FP)/scan. The average area under the curve was 0.94 ± 0.03 for volume-level detection. Similar or better performance was achieved on the independent external data set. Conclusions: DNN systems can efficiently perform automated retinal layer subsidence detection in retinal OCT images. In particular, the proposed DNN system detects OPL subsidence with high sensitivity and a very limited number of FP detections. Translational Relevance: DNNs enable objective identification of early signs associated with high risk of progression to the atrophic late stage of AMD, ideally suited for screening and assessing the efficacy of the interventions aiming to slow disease progression.

Influencing Factors on Pupillary Light Responses as a Biomarker for Local Retinal Function in a Large Normative Cohort.

Purpose: Investigating influencing factors on the pupillary light response (PLR) as a biomarker for local retinal function by providing epidemiological data of a large normative collective and to establish a normative database for the evaluation of chromatic pupil campimetry (CPC). Methods: Demographic and ophthalmologic characteristics were captured and PLR parameters of 150 healthy participants (94 women) aged 18 to 79 years (median = 46 years) were measured with L-cone- and rod-favoring CPC protocols. Linear-mixed effects models were performed to determine factors influencing the PLR and optical coherence tomography (OCT) data were correlated with the pupillary function volume. Results: Relative maximal constriction amplitude (relMCA) and latency under L-cone- and rod-favoring stimulation were statistically significantly affected by the stimulus eccentricity (P < 0.0001, respectively). Iris color and gender did not affect relMCA or latency significantly; visual hemifield, season, and daytime showed only minor influence under few stimulus conditions. Age had a statistically significant effect on latency under rod-specific stimulation with a latency prolongation ≥60 years. Under photopic and scotopic conditions, baseline pupil diameter declined significantly with increasing age (P < 0.0001, respectively). Pupillary function volume and OCT data were not correlated relevantly. Conclusions: Stimulus eccentricity had the most relevant impact on relMCA and latency of the PLR during L-cone- and rod-favoring stimulation. Latency is prolonged ≥60 years under scotopic conditions. Considering the large study collective, a representative normative database for relMCA and latency as valid readout parameters for L-cone- and rod-favoring stimulation could be established. This further validates the usability of the PLR in CPC as a biomarker for local retinal function.

Retinal blood flow association with age and weight in infants at risk for retinopathy of prematurity.

This prospective study evaluated the relationship between laser speckle contrast imaging (LSCI) ocular blood flow velocity (BFV) and five birth parameters: gestational age (GA), postmenstrual age (PMA) and chronological age (CA) at the time of measurement, birth weight (BW), and current weight (CW) in preterm neonates at risk for retinopathy of prematurity (ROP). 38 Neonates with BW < 2 kg, GA < 32 weeks, and PMA between 27 and 47 weeks underwent 91 LSCI sessions. Correlation tests and regression analysis were performed to quantify relationships between birth parameters and ocular BFV. Mean ocular BFV index in this cohort was 8.8 +/- 4.0 IU. BFV positively correlated with PMA (r = 0.3, p = 0.01), CA (r = 0.3, p = 0.005), and CW (r = 0.3, p = 0.02). BFV did not correlate with GA nor BW (r = - 0.2 and r = - 0.05, p > 0.05). Regression analysis with mixed models demonstrated that BFV increased by 1.2 for every kilogram of CW, by 0.34 for every week of CA, and by 0.36 for every week of PMA (p = 0.03, 0.004, 0.007, respectively). Our findings indicate that increased age and weight are associated with increased ocular BFV measured using LSCI in premature infants. Future studies investigating the associations between ocular BFV and ROP clinical severity must control for age and/or weight of the infant.

Optical coherence elastography under homolateral parallel acoustic radiation force excitation for ocular elasticity quantification.

Alteration in the elastic properties of biological tissues may indicate changes in the structure and components. Acoustic radiation force optical coherence elastography (ARF-OCE) can assess the elastic properties of the ocular tissues non-invasively. However, coupling the ultrasound beam and the optical beam remains challenging. In this Letter, we proposed an OCE method incorporating homolateral parallel ARF excitation for measuring the elasticity of the ocular tissues. An acoustic-optic coupling unit was established to reflect the ultrasound beam while transmitting the light beam. The ARF excited the ocular tissue in the direction parallel to the light beam from the same side of the light beam. We demonstrated the method on the agar phantoms, the porcine cornea, and the porcine retina. The results show that the ARF-OCE method can measure the elasticity of the cornea and the retina, resulting in higher detection sensitivity and a more extensive scanning range.

iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images.

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10-8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images.

[Multifocal electroretinography in the diagnosis and monitoring of early and intermediate stages of age-related macular degeneration]. / Mul'tifokal'naya elektroretinografiya v diagnostike i monitoringe rannei i promezhutochnoi stadii vozrastnoi makulyarnoi degeneratsii.

Multifocal electroretinography is a valuable diagnostic method for the objective localization and quantitative assessment of functional disorders of the central retina in age-related macular degeneration. It is used to detect early changes, monitor the course of the disease and treatment outcomes. In many cases, multifocal electroretinography is a more sensitive method for detecting functional disorders at the early/intermediate stage of age-related macular degeneration compared to morphological (optical coherence tomography) and subjective (visual acuity, perimetry) testing methods.

Correlation between vessel density and thickness in the retina and choroid of severe non-proliferative diabetic retinopathy patients.

Objectives: To explore the correlation between the vessel density (VD) of the retina and choroid vascular plexuses and the thicknesses of their respective retinal layers and choroid membranes in participants with severe non-proliferative diabetic retinopathy (NPDR). Methods: We retrospectively analyzed the data of 42 eyes of 42 participants with diabetes mellitus (DM) and severe NPDR. In addition, 41 eyes of 41 healthy controls were evaluated. Measurements were taken for both groups using optical coherence tomography angiography (OCTA), including the area and perimeter of the foveal vascular zone (FAZ) and the vascular density (VD) in the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choroid capillary (CC). These measurements were compared with the retinal thickness (RT) of the inner/intermediate retinal layers and choroidal thickness (CT). The study evaluated the correlation between RT or CT and VD in the respective vascular networks, namely superficial capillary plexus (SCP), deep capillary plexus (DCP), or CC. Results: The inner RT and VD in all plexuses were significantly lower in the severe NPDR group than in the healthy controls. Furthermore, the FAZ area and perimeter were larger in the severe NPDR group. Inner RT was correlated with VD in the SCP group (r=0.67 and r=0.71 in the healthy control and severe NPDR groups, respectively; p<0.05). CT negatively correlated with VD in the CC (r=-0.697 and r=-0.759 in the healthy control and severe NPDR groups, respectively; p<0.05). Intermediate RT significantly correlated with VD in the DCP of the severe NPDR group (r=-0.55, p<0.05), but not in the healthy control group. Conclusions: Retinal or choroidal thickness strongly correlated with VD. Therefore, patients with severe NPDR must consider the distinct anatomical and functional entities of the various retinal layers and the choroid.

Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study.

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

Migraine eye: correlation between migraine and the retina.

Background: Activation of the trigeminal vascular system in migraine releases vasoactive neurotransmitters, causing abnormal vasoconstriction, which may affect the ocular system, leading to retinal damage. The purpose of our study was to determine whether there are differences in each retinal layer between migraine patients and healthy subjects. Methods: A case-control study recruited 38 migraine patients and 38 age- and sex-matched controls. Optical coherence tomography was used to measure the thickness of the peripapillary and macular retinal nerve fiber layer (pRNFL and mRNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), and inner nuclear layer (INL). Results: The mean ages of the migraine patients and controls were 36.29 ± 9.45 and 36.45 ± 9.27 years, respectively. Thirty-four patients (89.48%) in both groups were female. The mean disability score was 19.63 ± 20.44 (indicating severe disability). The superior-outer INL of migraine patients were thicker than controls. Thickness of the GCL at temporal-outer sector and mRNFL at the superior-outer sector of the headache-side eyes was reduced. However, the INL of the headache-side-eye showed negative correlation with the disability score. This is the first study having found thinning of the GCL and mRNFL of the headache-side eyes. The INL was also thickened in migraines but showed negative correlation with the disability score. Conclusions: Increased INL thickness in migraine patients may result from inflammation. The more severe cases with a high disability score might suffered progressive retinal neuronal loss, resulting in thinner INL than less severe cases.

Synaptic Protein Loss in Extracellular Vesicles Reflects Brain and Retinal Atrophy in People With Multiple Sclerosis.

OBJECTIVES: To assess whether the rate of change in synaptic proteins isolated from neuronally enriched extracellular vesicles (NEVs) is associated with brain and retinal atrophy in people with multiple sclerosis (MS). METHODS: People with MS were followed with serial blood draws, MRI (MRI), and optical coherence tomography (OCT) scans. NEVs were immunocaptured from plasma, and synaptopodin and synaptophysin proteins were measured using ELISA. Subject-specific rates of change in synaptic proteins, as well as brain and retinal atrophy, were determined and correlated. RESULTS: A total of 50 people with MS were included, 46 of whom had MRI and 45 had OCT serially. The rate of change in NEV synaptopodin was associated with whole brain (rho = 0.31; p = 0.04), cortical gray matter (rho = 0.34; p = 0.03), peripapillary retinal nerve fiber layer (rho = 0.37; p = 0.01), and ganglion cell/inner plexiform layer (rho = 0.41; p = 0.006) atrophy. The rate of change in NEV synaptophysin was also correlated with whole brain (rho = 0.31; p = 0.04) and cortical gray matter (rho = 0.31; p = 0.049) atrophy. DISCUSSION: NEV-derived synaptic proteins likely reflect neurodegeneration and may provide additional circulating biomarkers for disease progression in MS.

Point based weakly semi-supervised biomarker detection with cross-scale and label assignment in retinal OCT images.

BACKGROUND AND OBJECTIVE: Optical coherence tomography (OCT) is currently one of the most advanced retinal imaging methods. Retinal biomarkers in OCT images are of clinical significance and can assist ophthalmologists in diagnosing lesions. Compared with fundus images, OCT can provide higher resolution segmentation. However, image annotation at the bounding box level needs to be performed by ophthalmologists carefully and is difficult to obtain. In addition, the large variation in shape of different retinal markers and the inconspicuous appearance of biomarkers make it difficult for existing deep learning-based methods to effectively detect them. To overcome the above challenges, we propose a novel network for the detection of retinal biomarkers in OCT images. METHODS: We first address the issue of labeling cost using a novel weakly semi-supervised object detection method with point annotations which can reduce bounding box-level annotation efforts. To extend the method to the detection of biomarkers in OCT images, we propose multiple consistent regularizations for point-to-box regression network to deal with the shortage of supervision, which aims to learn more accurate regression mappings. Furthermore, in the subsequent fully supervised detection, we propose a cross-scale feature enhancement module to alleviate the detection problems caused by the large-scale variation of biomarkers. We also propose a dynamic label assignment strategy to distinguish samples of different importance more flexibly, thereby reducing detection errors due to the indistinguishable appearance of the biomarkers. RESULTS: When using our detection network, our regressor also achieves an AP value of 20.83 s when utilizing a 5 % fully labeled dataset partition, surpassing the performance of other comparative methods at 5 % and 10 %. Even coming close to the 20.87 % result achieved by Point DETR under 20 % full labeling conditions. When using Group R-CNN as the point-to-box regressor, our detector achieves 27.21 % AP in the 50 % fully labeled dataset experiment. 7.42 % AP improvement is achieved compared to our detection network baseline Faster R-CNN. CONCLUSIONS: The experimental findings not only demonstrate the effectiveness of our approach with minimal bounding box annotations but also highlight the enhanced biomarker detection performance of the proposed module. We have included a detailed algorithmic flow in the supplementary material.

Noninvasive imaging-guided ultrasonic neurostimulation with arbitrary 2D patterns and its application for high-quality vision restoration.

Retinal degeneration, a leading cause of irreversible low vision and blindness globally, can be partially addressed by retina prostheses which stimulate remaining neurons in the retina. However, existing electrode-based treatments are invasive, posing substantial risks to patients and healthcare providers. Here, we introduce a completely noninvasive ultrasonic retina prosthesis, featuring a customized ultrasound two-dimensional array which allows for simultaneous imaging and stimulation. With synchronous three-dimensional imaging guidance and auto-alignment technology, ultrasonic retina prosthesis can generate programmed ultrasound waves to dynamically and precisely form arbitrary wave patterns on the retina. Neuron responses in the brain's visual center mirrored these patterns, evidencing successful artificial vision creation, which was further corroborated in behavior experiments. Quantitative analysis of the spatial-temporal resolution and field of view demonstrated advanced performance of ultrasonic retina prosthesis and elucidated the biophysical mechanism of retinal stimulation. As a noninvasive blindness prosthesis, ultrasonic retina prosthesis could lead to a more effective, widely acceptable treatment for blind patients. Its real-time imaging-guided stimulation strategy with a single ultrasound array, could also benefit ultrasound neurostimulation in other diseases.

Variability of relationship between inner-retinal structural changes and visual dysfunction in optic neuropathy.

Optical coherence tomography (OCT) displays the retinal nerve fiber layer (RNFL) or macular ganglion cell and inner plexiform layer (GCIPL) thickness below 1st percentile in red color. This finding generally indicates severe inner-retinal structural changes and suggests poor visual function. Nevertheless, some individuals show preserved visual function despite these circumstances. This study aimed to identify the correlation between best-corrected visual acuity (BCVA) and inner-retinal thickness based on OCT parameters in various optic neuropathy patients with extremely low RNFL/GCIPL thickness, and determine the limitation of OCT for predicting visual function in these patients. 131 patients were included in the study. The mean BCVA in logMAR was 0.55 ± 0.70 with a broad range from - 0.18 to 3.00. Among the OCT parameters, temporal GCIPL (r = - 0.412) and average GCIPL (r = - 0.366) exhibited the higher correlations with BCVA. Etiological comparisons of optic neuropathies revealed significantly lower BCVA in LHON (all p < 0.05). Idiopathic optic neuritis (ON) and MOGAD exhibited better and narrower BCVA distributions compared to the other optic neuropathies. OCT had limited utility in reflecting BCVA, notwithstanding significant inner-retinal thinning after optic nerve injuries. Caution is needed in interpreting OCT findings, especially as they relate to the etiology of optic neuropathy.

Model-free robust motion control for biological optical microscopy using time-delay estimation with an adaptive RBFNN compensator.

The field of large numerical aperture microscopy has witnessed significant advancements in spatial and temporal resolution, as well as improvements in optical microscope imaging quality. However, these advancements have concurrently raised the demand for enhanced precision, extended range, and increased load-bearing capacity in objective motion carrier (OMC). To address this challenge, this study introduces an innovative OMC that employs a ball screw mechanism as its primary driving component. Furthermore, a robust nonlinear motion control strategy has been developed, which integrates fast nonsingular terminal sliding mode, experimental estimation techniques, and adaptive radial basis neural network, to mitigate the impact of nonlinear friction within the ball screw mechanism on motion precision. The stability of the closed-loop control system has been rigorously demonstrated through Lyapunov theory. Compared with other enhanced sliding mode control strategies, the maximum error and root mean square error of this controller are improved by 33% and 34% respectively. The implementation of the novel OMC has enabled the establishment of a high-resolution bio-optical microscope, which has proven its effectiveness in the microscopic imaging of retinal organoids.

Total variation regularized tensor ring decomposition for OCT image denoising and super-resolution.

This paper suggests a novel hybrid tensor-ring (TR) decomposition and first-order tensor-based total variation (FOTTV) model, known as the TRFOTTV model, for super-resolution and noise suppression of optical coherence tomography (OCT) images. OCT imaging faces two fundamental problems undermining correct OCT-based diagnosis: significant noise levels and low sampling rates to speed up the capturing process. Inspired by the effectiveness of TR decomposition in analyzing complicated data structures, we suggest the TRFOTTV model for noise suppression and super-resolution of OCT images. Initially, we extract the nonlocal 3D patches from OCT data and group them to create a third-order low-rank tensor. Subsequently, using TR decomposition, we extract the correlations among all modes of the grouped OCT tensor. Finally, FOTTV is integrated into the TR model to enhance spatial smoothness in OCT images and conserve layer structures more effectively. The proximal alternating minimization and alternative direction method of multipliers are applied to solve the obtained optimization problem. The effectiveness of the suggested method is verified by four OCT datasets, demonstrating superior visual and numerical outcomes compared to state-of-the-art procedures.