OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods.

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

Functional OCT reveals anisotropic changes of retinal flicker-evoked vasodilation.

The purpose of this study is to verify the effect of anisotropic property of retinal biomechanics on vasodilation measurement. A custom-built optical coherence tomography (OCT) was used for time-lapse imaging of flicker stimulation-evoked vessel lumen changes in mouse retinas. A comparative analysis revealed significantly larger (18.21%) lumen dilation in the axial direction compared to the lateral (10.77%) direction. The axial lumen dilation predominantly resulted from the top vessel wall movement toward the vitreous direction, whereas the bottom vessel wall remained stable. This observation indicates that the traditional vasodilation measurement in the lateral direction may result in an underestimated value.

Longitudinal imaging of vitreal hyperreflective foci in mice with acute optic nerve damage using visible-light optical coherence tomography.

Hyperreflective foci (HRFs) appear in optical coherence tomography (OCT) images of the retina and vitreous of patients with various ocular diseases. HRFs are hypothesized to be immune cells that appear in response to ischemia or tissue damage. To accurately identify HRFs and establish their clinical significance, it is necessary to replicate the detection of similar patterns in vivo in a small animal model. We combined visible-light OCT with temporal speckle averaging (TSA) to visualize and track vitreal HRFs (VHRFs) densities for three days after an optic nerve crush (ONC) injury. Resulting vis-OCT images revealed that VHRF density significantly increased approximately 10-fold at 12 h after ONC and returned to baseline three days after ONC. Additional immunohistochemistry results confirmed these VHRFs as inflammatory cells induced from optic nerve damage.

Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granu la ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.

Unsupervised OCT image despeckling with ground-truth- and repeated-scanning-free features.

Optical coherence tomography (OCT) can resolve biological three-dimensional tissue structures, but it is inevitably plagued by speckle noise that degrades image quality and obscures biological structure. Recently unsupervised deep learning methods are becoming more popular in OCT despeckling but they still have to use unpaired noisy-clean images or paired noisy-noisy images. To address the above problem, we propose what we believe to be a novel unsupervised deep learning method for OCT despeckling, termed Double-free Net, which eliminates the need for ground truth data and repeated scanning by sub-sampling noisy images and synthesizing noisier images. In comparison to existing unsupervised methods, Double-free Net obtains superior denoising performance when trained on datasets comprising retinal and human tissue images without clean images. The efficacy of Double-free Net in denoising holds significant promise for diagnostic applications in retinal pathologies and enhances the accuracy of retinal layer segmentation. Results demonstrate that Double-free Net outperforms state-of-the-art methods and exhibits strong convenience and adaptability across different OCT images.

A novel vector field analysis for quantitative structure changes after macular epiretinal membrane surgery.

The aim of this study was to introduce novel vector field analysis for the quantitative measurement of retinal displacement after epiretinal membrane (ERM) removal. We developed a novel framework to measure retinal displacement from retinal fundus images as follows: (1) rigid registration of preoperative retinal fundus images in reference to postoperative retinal fundus images, (2) extraction of retinal vessel segmentation masks from these retinal fundus images, (3) non-rigid registration of preoperative vessel masks in reference to postoperative vessel masks, and (4) calculation of the transformation matrix required for non-rigid registration for each pixel. These pixel-wise vector field results were summarized according to predefined 24 sectors after standardization. We applied this framework to 20 patients who underwent ERM removal to obtain their retinal displacement vector fields between retinal fundus images taken preoperatively and at postoperative 1, 4, 10, and 22 months. The mean direction of displacement vectors was in the nasal direction. The mean standardized magnitudes of retinal displacement between preoperative and postoperative 1 month, postoperative 1 and 4, 4 and 10, and 10 and 22 months were 38.6, 14.9, 7.6, and 5.4, respectively. In conclusion, the proposed method provides a computerized, reproducible, and scalable way to analyze structural changes in the retina with a powerful visualization tool. Retinal structural changes were mostly concentrated in the early postoperative period and tended to move nasally.

Anatomical Features can Affect OCT Measures Used for Clinical Decisions and Clinical Trial Endpoints.

Purpose: To understand the association between anatomical parameters of healthy eyes and optical coherence tomography (OCT) circumpapillary retinal nerve fiber layer (cpRNFL) thickness measurements. Methods: OCT cpRNFL thickness was obtained from 396 healthy eyes in a commercial reference database (RDB). The temporal quadrant (TQ), superior quadrant (SQ), inferior quadrant (IQ), and global (G) cpRNFL thicknesses were analyzed. The commercial OCT devices code these values based on percentiles (red, <1%; yellow, ≥1% and <5%), after taking age and disc area into consideration. Four anatomical parameters were assessed: fovea-to-disc distance, an estimate of axial length, and the locations of the superior and the inferior peaks of the cpRNFL thickness curve. Pearson correlation values were obtained for the parameters and the thickness measures of each of the four cpRNFL regions, and t-tests were performed between the cpRNFL thicknesses coded as abnormal (red or yellow, <5%) versus normal (≥5%). Results: For each of the four anatomical parameters, the correlation with the thickness of one or more of the TQ, SQ, IQ, and G regions exceeded the correlation with age or disc area. All four parameters were significantly (P < 0.001) associated with the abnormal cpRNFL values. The significant parameters were not the same for the different regions; for example, a parameter could be negatively correlated for the TQ but positively correlated with the SQ or IQ. Conclusions: In addition to age and disc area, which are used for inferences in normative databases, four anatomical parameters are associated with cpRNFL thickness. Translational Relevance: Taking these additional anatomical parameters into consideration should aid diagnostic accuracy.

Hemoglobin levels are associated with retinal vascular caliber in a middle-aged birth cohort.

Vascular and neural structures of the retina can be visualized non-invasively and used to predict ocular and systemic pathologies. We set out to evaluate the association of hemoglobin (Hb) levels within the national reference interval with retinal vascular caliber, optical coherence tomography (OCT) and visual field (VF) parameters in the Northern Finland 1966 Birth Cohort (n = 2319, 42.1% male, average age 47 years). The studied parameters were evaluated in Hb quintiles and multivariable linear regression models. The lowest Hb quintile of both sexes presented the narrowest central retinal vein equivalent (CRVE) and the healthiest cardiometabolic profile compared to the other Hb quintiles. In the regression models, CRVE associated positively with Hb levels in both sexes, (Bmales = 0.068 [0.001; 0.135], Bfemales = 0.087 [0.033; 0.140]), after being adjusted for key cardiometabolic and inflammatory parameters, smoking status, and fellow vessel caliber. No statistically significant associations of Hb levels with central retinal artery equivalent, OCT or VF parameters were detected. In conclusion, Hb levels were positively and specifically associated with CRVE, indicating that Hb levels are an independent factor affecting CRVE and the effect is in parallel with established risk factors for cardiometabolic diseases.

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

Purpose: The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs). Methods: We developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset. After preprocessing, including cropping black regions, data augmentation, and normalization, a Swin-MCSFNet classifier based on the Swin-Transformer for multiple color-space fusion was proposed to grade the quality of RIs. The generalizability of Swin-MCSFNet was validated across multiple data centers. Additionally, for enhanced interpretability, a Score-CAM-generated heatmap was applied to provide visual explanations. Results: Experimental results reveal that the proposed Swin-MCSFNet achieves promising performance, yielding a micro-receiver operating characteristic (ROC) of 0.93 and ROC scores of 0.96, 0.81, and 0.96 for the "Good," "Usable," and "Reject" categories, respectively. These scores underscore the accuracy of RIQA based on Swin-MCSF in distinguishing among the three categories. Furthermore, heatmaps generated across different RIQA classification scores and various color spaces suggest that regions in the retinal images from multiple color spaces contribute significantly to the decision-making process of the Swin-MCSFNet classifier. Conclusions: Our study demonstrates that the proposed Swin-MCSFNet outperforms other methods in experiments conducted on multiple datasets, as evidenced by the superior performance metrics and insightful Score-CAM heatmaps. Translational Relevance: This study constructs a new retinal image quality evaluation system, which will contribute to the subsequent research of retinal images.

The Impact of Photopigment Bleaching on the Human Rod Photoreceptor Subretinal Space Measured Via Optical Coherence Tomography.

Purpose: The purpose of this study was to investigate rod photopigment bleaching-driven intrinsic optical signals (IOS) in the human outer retina and its measurement repeatability based on a commercial optical coherence tomography (OCT) platform. Methods: The optical path length of the rod photoreceptor subretinal space (SRS), that is, the distance between signal bands of rod outer segment tips and retinal pigment epithelium, was measured in 15 healthy subjects in ambient light and during a long-duration bleaching white-light exposure. Results: On 2 identical study days (day 1 and day 2 [D1 and D2]), light stimulation resulted in a significant decrease in rod SRS by 21.3 ± 7.6% and 19.8 ± 8.5% (both P < 0.001), respectively. The test-retest reliability of the SRS maximum change of an individual subject was moderate for single measures (intraclass correlation coefficient [ICC] = 0.730, 95% confidence interval [CI] = 0.376, 0.900, P < 0.001) and good for average measures (ICC = 0.844, 95% CI = 0.546, 0.947, P < 0.001). The mean area under the stimulus response curve with values of 14.8 ± 9.4 and 15.5 ± 7.5 µm × minutes (P = 0.782) showed excellent agreement between the stimulus response on D1 and D2. Intermittent dark adaptation of the retina led to an initial increase of the SRS by 6.1% (P = 0.018) and thereafter showed a decrease toward baseline, despite continued dark adaptation. Conclusions: The data indicate the potential of commercial OCT in measuring slow IOS in the outer retina suggesting that the rod SRS could serve as a biomarker for photoreceptor function. The presented approach could provide an easily implementable clinical tool for the early detection of diseases affecting photoreceptor health.

Role of Optical Coherence Tomography in Predicting Visual Outcome after Surgery for Sellar and Supra-Sellar Tumors.

BACKGROUND: Almost one-fifth of patients undergoing surgery for sellar/supra-sellar tumors do not gain a significant improvement in their vision. Various methods have been described to predict prospective visual outcomes in them, although they lack uniformity. OBJECTIVE: The study was conducted to predict visual outcomes following surgery for sellar and supra-sellar tumors compressing the anterior optic pathway based on pre-operative optical coherence tomography (OCT) parameters. METHODS AND MATERIALS: This was a record-based observational descriptive longitudinal study done in a tertiary care center in India. Thirty-seven patients (74 eyes) diagnosed with sellar supra-sellar lesions were included in the study. Patients' ophthalmic evaluations, done pre-operatively and 3 months post-operatively, were reviewed. Spectral-domain OCT and segmentation were done using the automated segmentation technology of Spectralis software. The thickness of the respective layers was measured. RESULTS AND CONCLUSIONS: The mean age of the study population was 42.68 years. Eyes with a pre-operative visual acuity component of VIS (visual impairment score) ≤61, pre-operative ganglion cell layer thickness ≥26.31 um, a pre-operative inner plexiform layer thickness of ≥25.69 um, a pre-operative ganglion cell inner plexiform layer thickness of 52.00 um, pre-operative ganglion cell complex thickness ≥84.47 µm, and a pre-operative inner retinal layer thickness of ≥205.25 µm were more likely to have an improved visual outcome. Eyes with a pre-operative duration of visual symptoms of ≥15 months, VIS ≥126.50, a pre-operative decimal visual acuity of <0.035, a pre-operative visual field index of ≤8%, a pre-operative macular thickness of ≤287.06 um, a pre-operative macular RNFL (retinal nerve fiber layer) thickness ≤66.00 µm, and a pre-operative peri-papillary RNFL thickness ≤64.62 µm were unlikely to have visual improvement.

Adaptive Optics Retinal Imaging in RDH12-Associated Early Onset Severe Retinal Dystrophy.

Purpose: RDH12 is among the most common genes found in individuals with early-onset severe retinal (EOSRD). Adaptive optics scanning light ophthalmoscopy (AOSLO) enables resolution of individual rod and cone photoreceptors in the retina. This study presents the first AOSLO imaging of individuals with RDH12-associated EOSRD. Methods: Case series of patients who attended Moorfields Eye Hospital (London, UK). Spectral-domain optical coherence tomography, near-infrared reflectance (NIR), and blue autofluorescence imaging were analyzed. En face image sequences of photoreceptors were recorded using either of two AOSLO modalities. Cross-sectional analysis was undertaken for seven patients and longitudinal analysis for one patient. Results: Nine eyes from eight patients are presented in this case series. The mean age at the time of the assessment was 11.2 ± 6.5 years of age (range 7-29). A subfoveal continuous ellipsoid zone (EZ) line was present in eight eyes. Posterior pole AOSLO revealed patches of cone mosaics. Average cone densities at regions of interest 0.5° to the fovea ranged from 12,620 to 23,660 cells/mm2, whereas intercell spacing ranged from 7.0 to 9.7 µm. Conclusions: This study demonstrates that AOSLO can provide useful high-quality images in patients with EOSRD, even during childhood, with nystagmus, and early macular atrophy. Cones at the posterior pole can appear as scattered islands or, possibly later in life, as a single subfoveal conglomerate. Detailed image analysis suggests that retinal pigment epithelial stress and dysfunction may be the initial step toward degeneration, with NIR being a useful tool to assess retinal well-being in RDH12-associated EOSRD.

Comparative In Vivo Imaging of Retinal Structures in Tree Shrews, Humans, and Mice.

Rodent models, such as mice and rats, are commonly used to examine retinal ganglion cell damage in eye diseases. However, as nocturnal animals, rodent retinal structures differ from primates, imposing significant limitations in studying retinal pathology. Tree shrews (Tupaia belangeri) are small, diurnal paraprimates that exhibit superior visual acuity and color vision compared with mice. Like humans, tree shrews have a dense retinal nerve fiber layer (RNFL) and a thick ganglion cell layer (GCL), making them a valuable model for investigating optic neuropathies. In this study, we applied high-resolution visible-light optical coherence tomography to characterize the tree shrew retinal structure in vivo and compare it with that of humans and mice. We quantitatively characterize the tree shrew's retinal layer structure in vivo, specifically examining the sublayer structures within the inner plexiform layer (IPL) for the first time. Next, we conducted a comparative analysis of retinal layer structures among tree shrews, mice, and humans. We then validated our in vivo findings in the tree shrew inner retina using ex vivo confocal microscopy. The in vivo and ex vivo analyses of the shrew retina build the foundation for future work to accurately track and quantify the retinal structural changes in the IPL, GCL, and RNFL during the development and progression of human optic diseases.

HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features.

Retinal segmentation is a crucial step in the early warning of human health conditions. However, retinal blood vessels possess complex curvature, irregular distribution, and contain multi-scale fine structures, which make the limited receptive field of regular convolution challenging to process their vascular details efficiently. Additionally, the encoder-decoder based network leads to irreversible spatial information loss because of multiple downsampling, resulting in over-segmentation and missed segmentation of the vessels. For this reason, we develop a high-resolution network based on Deformable Convolution v3, called HRD-Net. By constructing a high-resolution representation, the network allows special attention to be paid to the details of tiny blood vessels. The proposed feature enhancement cascade module based on Deformable Convolution v3 can flexibly adapt and capture the ever-changing morphology and intricate connections of retinal blood vessels, ensuring the continuity of vessel segmentation. In the output phase of the network, the proposed global aggregation module integrates full-resolution feature maps while suppressing redundant features, achieving an effective fusion of high-level semantic information and spatial detail information. In addition, we have re-examined the selection criteria for activation and normalization methods, and also refine the network architectures from a spatial domain perspective to release redundant computational loads. Testing on the DRIVE, STARE, and CHASE_DB1 datasets indicates that HRD-Net, with fewer parameters, outperforms existing segmentation methods on several evaluation metrics such as F1, ACC, SE, SP, AUC, and IOU.

RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps.

Optical coherence tomography imaging provides a crucial clinical measurement for diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer (RNFL) thickness (RNFLT) map. Researchers have been increasingly using neural models to extract meaningful features from the RNFLT map, aiming to identify biomarkers for glaucoma and its progression. However, accurately representing the RNFLT map features relevant to glaucoma is challenging due to significant variations in retinal anatomy among individuals, which confound the pathological thinning of the RNFL. Moreover, the presence of artifacts in the RNFLT map, caused by segmentation errors in the context of degraded image quality and defective imaging procedures, further complicates the task. In this paper, we propose a general framework called RNFLT2Vec for unsupervised learning of vectorized feature representations from RNFLT maps. Our method includes an artifact correction component that learns to rectify RNFLT values at artifact locations, producing a representation reflecting the RNFLT map without artifacts. Additionally, we incorporate two regularization techniques to encourage discriminative representation learning. Firstly, we introduce a contrastive learning-based regularization to capture the similarities and dissimilarities between RNFLT maps. Secondly, we employ a consistency learning-based regularization to align pairwise distances of RNFLT maps with their corresponding thickness distributions. Through extensive experiments on a large-scale real-world dataset, we demonstrate the superiority of RNFLT2Vec in three different clinical tasks: RNFLT pattern discovery, glaucoma detection, and visual field prediction. Our results validate the effectiveness of our framework and its potential to contribute to a better understanding and diagnosis of glaucoma.

Anomaly-guided weakly supervised lesion segmentation on retinal OCT images.

The availability of big data can transform the studies in biomedical research to generate greater scientific insights if expert labeling is available to facilitate supervised learning. However, data annotation can be labor-intensive and cost-prohibitive if pixel-level precision is required. Weakly supervised semantic segmentation (WSSS) with image-level labeling has emerged as a promising solution in medical imaging. However, most existing WSSS methods in the medical domain are designed for single-class segmentation per image, overlooking the complexities arising from the co-existence of multiple classes in a single image. Additionally, the multi-class WSSS methods from the natural image domain cannot produce comparable accuracy for medical images, given the challenge of substantial variation in lesion scales and occurrences. To address this issue, we propose a novel anomaly-guided mechanism (AGM) for multi-class segmentation in a single image on retinal optical coherence tomography (OCT) using only image-level labels. AGM leverages the anomaly detection and self-attention approach to integrate weak abnormal signals with global contextual information into the training process. Furthermore, we include an iterative refinement stage to guide the model to focus more on the potential lesions while suppressing less relevant regions. We validate the performance of our model with two public datasets and one challenging private dataset. Experimental results show that our approach achieves a new state-of-the-art performance in WSSS for lesion segmentation on OCT images.

PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation.

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation.

PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder.

Anomaly detection is an important yet challenging task in medical image analysis. Most anomaly detection methods are based on reconstruction, but the performance of reconstruction-based methods is limited due to over-reliance on pixel-level losses. To address the limitation, we propose a patch-wise contrastive learning-based auto-encoder for medical anomaly detection. The key contribution is the patch-wise contrastive learning loss that provides supervision on local semantics to enforce semantic consistency between corresponding input-output patches. Contrastive learning pulls corresponding patch pairs closer while pushing non-corresponding ones apart between input and output, enabling the model to learn local normal features better and improve discriminability on anomalous regions. Additionally, we design an anomaly score based on local semantic discrepancies to pinpoint abnormalities by comparing feature difference rather than pixel variations. Extensive experiments on three public datasets (i.e., brain MRI, retinal OCT, and chest X-ray) achieve state-of-the-art performance, with our method achieving over 99% AUC on retinal and brain images. Both the contrastive patch-wise supervision and patch-discrepancy score provide targeted advancements to overcome the weaknesses in existing approaches.

Association of Retinal Nerve Fiber Layer Thickness with Brain Microstructural Changes in Participants with White Matter Hyperintensities.

PURPOSE: White matter hyperintensity (WMH) is suggested to cause stroke and dementia in older adults. Retinal structural thicknesses revealed by optical coherence tomography (OCT) are associated with structural changes in the brain. We aimed to explore the association between the peripapillary retinal nerve fiber layer (RNFL) and cerebral microstructural changes in participants with white matter hyperintensities (WMH). METHODS: Seventy-four participants (37 controls, healthy control (HC), and 37 older adults with WMH) underwent retinal and brain imaging using OCT and magnetic resonance imaging (MRI) respectively. Peripapillary RNFL thickness was assessed by the OCT. Gray matter volume (GMV) was assessed from a T1-weighted MRI. White matter integrity was assessed with diffusion tensor imaging (DTI) while WMH severity was assessed with the Fazekas scale. All participants underwent a neuropsychological examination (Mini-Mental State Examination, MMSE). RESULTS: Older adults with WMH showed thinner peripapillary RNFL (p = 0.004) thickness when compared with the control group after adjusting for age, hypertension and gender. In our older adults with WMH, RNFL thickness correlated with fractional anisotropy (FA) in the superior longitudinal fasciculus (SLF) (Rho = -0.331, p < 0.001). In older adults with WMH, RNFL was significantly associated with MMSE scores (Rho = 0.422, p < 0.001) and Fazekas scores (Rho = -0.381, p = 0.022) respectively. CONCLUSIONS: We suggest neurodegeneration of peripapillary RNFL in older adults with WMH was associated with cerebral microstructural volume, impaired cerebral axonal damage, and cognitive performances. OCT metrics may provide evidence of neurodegeneration that may underpin WMH and cerebral microstructural changes in the brain. CLINICAL TRIAL REGISTRATION: This study was registered online at the China Clinical Trial Registration Center (registration number: ChiCTR-ROC-17011819).