53 research outputs found

    Improvement of the clinical utility of optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) measurement by establishing data comparability across the OCT technology generations and models

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    Glaucoma is the second leading cause of blindness worldwide, which induces irreversible structural damage (retinal ganglion cell loss and retinal nerve fiber layer (RNFL) thinning) on the retina. Optical coherence tomography (OCT) provides RNFL thickness measurements, which have become an essential clinical measure for objective glaucoma assessment. RNFL thickness is measured on a cross-sectional retinal image sampled along a 3.4mm circle centered around the optic nerve head (ONH). With the conventional time-domain OCT (TD-OCT), its operator dependent scan registration is responsible for the majority of measurement variability. Recently, spectral domain OCT (SD-OCT) technology has been introduced. SD-OCT provides faster scanning (up to 100x) and finer axial resolution (up to 2x) compared to TD-OCT, allowing three-dimensional (3D) volume sampling. 3D SD-OCT data can be visualized as an en face image of the retina. This allows us to create a virtual OCT image along any sampling line (curved or straight), which permits virtually perfect scan registration. The objective of this study is to improve the clinical utility of OCT RNFL measurement by establishing data comparability across the multiple OCT generations and models. First, we developed an algorithm to match the TD-OCT scan location within the corresponding 3D SD-OCT volume. Scan location matching (SLM) enables computation of the calibration equation between TD-OCT and SD-OCT for direct comparison of measurements, bridging the old technology with new ones. Second, the performance of the SLM method was measured using various SD-OCT devices with different spatial sampling methods. By making TD-OCT measurements at one time point comparable to the most recent SD-OCT measurement using SLM, glaucoma progression can be assessed on one to one basis. However, due to the variable TD-OCT scan registration over multiple visits, one can still not analyze the trend of glaucoma progression because RNFL thickness measured at different locations is not directly comparable even after calibration. Therefore, we developed a mathematical model of the retinal nerve fiber bundle distribution pattern to normalize the off-centered TD-OCT RNFL thickness to a virtually centered one. The outcome of this study would facilitate more accurate and reliable glaucoma disease/progression detection in cross-sectional as well as longitudinal clinical settings

    Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region.

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    Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra's algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496x644x51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel ( approximately 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data

    Artifacts in Optical Coherence Tomography Angiography

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    We performed a comprehensive search of the published literature in PubMed and Google Scholar to identify types, prevalence, etiology, clinical impact, and current methods for correction of various artifacts in optical coherence tomography angiography (OCTA) images. We found that the prevalence of OCTA image artifacts is fairly high. Artifacts associated with eye motion, misidentification of retinal layers, projections, and low optical coherence tomography signal are the most prevalent types. Artifacts in OCTA images are the major limitations of this diagnostic modality in clinical practice and identification of these artifacts and measures to mitigate them are essential for correct diagnosis and follow-up of patients

    Optic nerve head three-dimensional shape analysis

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    We present a method for optic nerve head (ONH) 3-D shape analysis from retinal optical coherence tomography (OCT). The possibility to noninvasively acquire in vivo high-resolution 3-D volumes of the ONH using spectral domain OCT drives the need to develop tools that quantify the shape of this structure and extract information for clinical applications. The presented method automatically generates a 3-D ONH model and then allows the computation of several 3-D parameters describing the ONH. The method starts with a high-resolution OCT volume scan as input. From this scan, the model-defining inner limiting membrane (ILM) as inner surface and the retinal pigment epithelium as outer surface are segmented, and the Bruch's membrane opening (BMO) as the model origin is detected. Based on the generated ONH model by triangulated 3-D surface reconstruction, different parameters (areas, volumes, annular surface ring, minimum distances) of different ONH regions can then be computed. Additionally, the bending energy (roughness) in the BMO region on the ILM surface and 3-D BMO-MRW surface area are computed. We show that our method is reliable and robust across a large variety of ONH topologies (specific to this structure) and present a first clinical application

    Enhanced visualization of the retinal vasculature using depth information in OCT

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    This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11517-017-1660-8[Abstract]: Retinal vessel tree extraction is a crucial step for analyzing the microcirculation, a frequently needed process in the study of relevant diseases. To date, this has normally been done by using 2D image capture paradigms, offering a restricted visualization of the real layout of the retinal vasculature. In this work, we propose a new approach that automatically segments and reconstructs the 3D retinal vessel tree by combining near-infrared reflectance retinography information with Optical Coherence Tomography (OCT) sections. Our proposal identifies the vessels, estimates their calibers, and obtains the depth at all the positions of the entire vessel tree, thereby enabling the reconstruction of the 3D layout of the complete arteriovenous tree for subsequent analysis. The method was tested using 991 OCT images combined with their corresponding near-infrared reflectance retinography. The different stages of the methodology were validated using the opinion of an expert as a reference. The tests offered accurate results, showing coherent reconstructions of the 3D vasculature that can be analyzed in the diagnosis of relevant diseases affecting the retinal microcirculation, such as hypertension or diabetes, among others.This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04

    An in vivo investigation of choroidal vasculature in age-related macular degeneration

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    Age-related macular degeneration (AMD) is the leading cause of visual impairment in the developed world. Whilst the pathogenesis is complex and not fully understood, changes to the choroidal vasculature in AMD have been demonstrated using histology. Advances in imaging technology, particularly long-wavelength optical coherence tomography (OCT), allow in vivo visualisation and investigation of this structure. The aim of this work is to determine whether changes to the choroidal vasculature are detectable in AMD using in vivo imaging. This was achieved through the evaluation of parameters for quantifying the structure, and the application of a machine learning approach to automated disease severity classification, based on choroidal appearance. Participants with early AMD (n=25), neovascular AMD (nAMD; n=25), and healthy controls (n=25) underwent imaging with a non-commercial long-wavelength (λc=1040 nm) OCT device. Subfoveal choroidal thickness, choroidal area, and luminal area were significantly lower in the nAMD group than the healthy and early AMD groups, whilst vessel ratio was significantly greater (P<0.05 in all cases). There was no significant difference in visible vessel diameter, choroidal vascularity index, luminal area ratio, or luminal perimeter ratio between the groups. No significant differences were found between the healthy and early AMD groups for any of the eight vascular parameters assessed. Classification of the disease groups based on choroidal OCT images was demonstrated using machine learning techniques. Textural features within the images were extracted using Gabor filters, and K-nearest neighbour, support vector machine, and random forest classifiers were assessed for this classification task. Textural changes were most pronounced in late-stage disease, although attribution to pathology or pharmacological intervention (anti-VEGF treatment) was not possible. Changes were also discernible in the early AMD group, suggesting sensitivity of this approach to detecting vascular involvement in early disease. In conclusion, structural changes to the choroidal vasculature in AMD are detectable in vivo using OCT imaging, demonstrated with both manual and automated analysis techniques. Whilst changes were most prominent in late-stage disease, subtle structural changes in early AMD were identified with texture analysis, warranting further investigation to improve our understanding of choroidal involvement in the pathogenesis of early AMD
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