Dataset Information

Evaluation of retinal nerve fiber layer defect using wide-field en-face swept-source OCT images by applying the inner limiting membrane flattening.

ABSTRACT:

Purpose

The assessment of retinal nerve fiber layer defects (RNFLDs) is a useful part of glaucoma care. Here, we obtained en-face images of retinal layers below the inner limiting membrane (ILM) with swept source-optical coherence tomography (SS-OCT), and measured RNFLD angle with new software.

Methods

This study included 105 eyes of 105 normal tension glaucoma (NTG) patients (age, 59.8 ± 13.2). Exclusion criteria were best-corrected visual acuity < 0.5, axial length > 28 mm, non-glaucoma ocular disease, and systemic disease affecting the visual field. We obtained 12 x 9 mm 3D volume scans centered on the macula with SS-OCT (DRI OCT-1, Topcon), and from these scans, created 3 averaged en-face images, each comprising 7 horizontal en-face images (total thickness: 18.2 ?m). We labeled these averaged images, according to their depth below the ILM, as en-face images 1 (shallowest), 2 (middle) and 3 (deepest). In each image, a circle was drawn centered on the disc, with a radius of half the distance between the centers of the disc and macula. The investigator marked points where the edge of the RNFLD intersected this circle, and RNFLD angle (RNFLDA) was calculated with new software. Finally, we analyzed the association between RNFLDA, cpRNFLT, weighted RGC count (wrgc) and Humphrey field analyzer (HFA)-measured mean deviation (MD) and hemifield total deviation (TD), both overall and in each hemifield.

Results

En-face image 2 had the highest interclass reproducibility for measuring RNFLDA (intra-rater intraclass correlation coefficient (ICC): 0.988, inter-rater ICC: 0.962). The correlation coefficients with RNFLDA were: HFA MD, -0.60; superior TD, -0.73; inferior TD, -0.69; overall cpRNFLT, -0.27; superior hemifield cpRNFLT, -0.39; and inferior hemifield cpRNFLT, -0.53 (all p<0.001).

Conclusions

RNFLDA measured in SS-OCT images had high reproducibility and was correlated to glaucoma severity. Our new method may be a valuable future part of glaucoma care.

SUBMITTER: Miura N

PROVIDER: S-EPMC5659603 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of retinal nerve fiber layer defect using wide-field en-face swept-source OCT images by applying the inner limiting membrane flattening.

Miura Naoki N Omodaka Kazuko K Kimura Koudai K Matsumoto Akiko A Kikawa Tsutomu T Takahashi Seri S Takada Naoko N Takahashi Hidetoshi H Maruyama Kazuichi K Akiba Masahiro M Yuasa Tetsuya T Nakazawa Toru T

PloS one 20171027 10

<h4>Purpose</h4>The assessment of retinal nerve fiber layer defects (RNFLDs) is a useful part of glaucoma care. Here, we obtained en-face images of retinal layers below the inner limiting membrane (ILM) with swept source-optical coherence tomography (SS-OCT), and measured RNFLD angle with new software.<h4>Methods</h4>This study included 105 eyes of 105 normal tension glaucoma (NTG) patients (age, 59.8 ± 13.2). Exclusion criteria were best-corrected visual acuity < 0.5, axial length > 28 mm, non- ...[more]

PMID: 29077716

Similar Datasets

Project description:PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

Project description:To characterize en face features of the retinal pigment epithelium (RPE) and choroid in eyes with chronic central serous chorioretinopathy (CSCR) using a high-speed, enhanced-depth swept-source optical coherence tomography (SS-OCT) prototype.Consecutive patients with chronic CSCR were prospectively examined with SS-OCT.Fifteen eyes of 13 patients.Three-dimensional 6×6 mm macular cube raster scans were obtained with SS-OCT operating at 1050 nm wavelength and 100000 A-lines/sec with 6 ?m axial resolution. Segmentation of the RPE generated a reference surface; en face SS-OCT images of the RPE and choroid were extracted at varying depths every 3.5 ?m (1 pixel). Abnormal features were characterized by systematic analysis of multimodal fundus imaging, including color photographs, fundus autofluorescence, fluorescein angiography, and indocyanine-green angiography (ICGA).En face SS-OCT morphology of the RPE and individual choroidal layers.En face SS-OCT imaging at the RPE level revealed absence of signal corresponding to RPE detachment or RPE loss in 15 of 15 (100%) eyes. En face SS-OCT imaging at the choriocapillaris level showed focally enlarged vessels in 8 of 15 eyes (53%). At the level of Sattler's layer, en face SS-OCT documented focal choroidal dilation in 8 of 15 eyes (53%) and diffuse choroidal dilation in 7 of 15 eyes (47%). At the level of Haller's layer, these same features were observed in 3 of 15 eyes (20%) and 12 of 15 eyes (80%), respectively. In all affected eyes, these choroidal vascular abnormalities were seen just below areas of RPE abnormalities. In 2 eyes with secondary choroidal neovascularization (CNV), distinct en face SS-OCT features corresponded to the neovascular lesions.High-speed, enhanced-depth SS-OCT at 1050 nm wavelength enables the visualization of pathologic features of the RPE and choroid in eyes with chronic CSCR not usually appreciated with standard spectral domain (SD) OCT. En face SS-OCT imaging seems to be a useful tool in the identification of CNV without the use of angiography. This in vivo documentation of the RPE and choroidal vasculature at variable depths may help elucidate the pathophysiology of disease and can contribute to the diagnosis and management of chronic CSCR.

Project description:To examine and quantify choriocapillaris lesions in active and quiescent serpiginous choroiditis (SC) using swept-source optical coherence tomography angiography (SS-OCTA) and en-face image analysis.Prospective observational case series.Patients with a clinical diagnosis of SC.A SS-OCTA prototype was used to image active and quiescent serpiginous lesions longitudinally before and after anti-inflammatory treatment. En-face slabs of choriocapillaris flow (CC-slab) or outer nuclear layer structure (ONL-slab) were generated from OCTA and OCT data, respectively.Qualitative and quantitative analyses on lesion boundary and area using a semi-automated MATLAB algorithm. Lesions were also compared to traditional multimodal imaging.Six eyes of three patients were imaged. Choroidal lesions were identified and analyzed in four of six eyes. Lesions with well-defined boundaries were identified in the CC-slab in areas of both active and inactive choroiditis. CC-slab lesion size and shape showed good correlation with lesions identified on indocyanine green angiography. CC-slab lesion area increased with disease activity and decreased with corticosteroid treatment. During active disease, the CC-slab lesion area was larger than both the ONL-slab and fundus autofluorescence lesion areas. Active CC-slab lesions not associated with corresponding abnormal autofluorescence resolved without clinical scarring after treatment. In inactive scars, the areas of retinal and choriocapillaris lesions were similar and did not change over time.En-face analysis of SS-OCTA choriocapillaris flow voids provide a non-invasive method for the detection of lesions in patients with SC. The presence of lesions in the choriocapillaris in the absence of retinal pigment epithelium and outer retinal abnormalities supports the hypothesis that choriocapillaris is the primary site of pathology in SC, and may be a sensitive early sign of disease activity. We propose a simple grading system of SC lesions based on SS-OCTA and fundus autofluorescence findings. SS-OCTA is a promising non-invasive method for monitoring patients with SC.

Project description:To demonstrate papillary imaging of eyes with optic disc pits (ODP) or optic disc pit associated maculopathy (ODP-M) with ultrahigh-speed swept-source optical coherence tomography (SS-OCT) at 1.68 million A-scans/s. To generate 3D-renderings of the papillary area with 3D volume-reconstructions of the ODP and highly resolved en face images from a single densely-sampled megahertz-OCT (MHz-OCT) dataset for investigation of ODP-characteristics.A 1.68 MHz-prototype SS-MHz-OCT system at 1050 nm based on a Fourier-domain mode-locked laser was employed to acquire high-definition, 3D datasets with a dense sampling of 1600?×?1600 A-scans over a 45° field of view. Six eyes with ODPs, and two further eyes with glaucomatous alteration or without ocular pathology are presented. 3D-rendering of the deep papillary structures, virtual 3D-reconstructions of the ODPs and depth resolved isotropic en face images were generated using semiautomatic segmentation.3D-rendering and en face imaging of the optic disc, ODPs and ODP associated pathologies showed a broad spectrum regarding ODP characteristics. Between individuals the shape of the ODP and the appending pathologies varied considerably. MHz-OCT en face imaging generates distinct top-view images of ODPs and ODP-M. MHz-OCT generates high resolution images of retinal pathologies associated with ODP-M and allows visualizing ODPs with depths of up to 2.7 mm.Different patterns of ODPs can be visualized in patients for the first time using 3D-reconstructions and co-registered high-definition en face images extracted from a single densely sampled 1050 nm megahertz-OCT (MHz-OCT) dataset. As the immediate vicinity to the SAS and the site of intrapapillary proliferation is located at the bottom of the ODP it is crucial to image the complete structure and the whole depth of ODPs. Especially in very deep pits, where non-swept-source OCT fails to reach the bottom, conventional swept-source devices and the MHz-OCT alike are feasible and beneficial methods to examine deep details of optic disc pathologies, while the MHz-OCT bears the advantage of an essentially swifter imaging process.

Dataset Information

Evaluation of retinal nerve fiber layer defect using wide-field en-face swept-source OCT images by applying the inner limiting membrane flattening.

Purpose

Methods

Results

Conclusions

Publications

Evaluation of retinal nerve fiber layer defect using wide-field en-face swept-source OCT images by applying the inner limiting membrane flattening.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure