High resolution multimodal clinical ophthalmic imaging system. Opt Express

Physical Sciences Inc., 20 New England Business Center, Andover MA 01810, USA.
Optics Express (Impact Factor: 3.49). 05/2010; 18(11):11607-21. DOI: 10.1364/OE.18.011607
Source: PubMed


We developed a multimodal adaptive optics (AO) retinal imager which is the first to combine high performance AO-corrected scanning laser ophthalmoscopy (SLO) and swept source Fourier domain optical coherence tomography (SSOCT) imaging modes in a single compact clinical prototype platform. Such systems are becoming ever more essential to vision research and are expected to prove their clinical value for diagnosis of retinal diseases, including glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD), and retinitis pigmentosa. The SSOCT channel operates at a wavelength of 1 microm for increased penetration and visualization of the choriocapillaris and choroid, sites of major disease activity for DR and wet AMD. This AO system is designed for use in clinical populations; a dual deformable mirror (DM) configuration allows simultaneous low- and high-order aberration correction over a large range of refractions and ocular media quality. The system also includes a wide field (33 deg.) line scanning ophthalmoscope (LSO) for initial screening, target identification, and global orientation, an integrated retinal tracker (RT) to stabilize the SLO, OCT, and LSO imaging fields in the presence of lateral eye motion, and a high-resolution LCD-based fixation target for presentation of visual cues. The system was tested in human subjects without retinal disease for performance optimization and validation. We were able to resolve and quantify cone photoreceptors across the macula to within approximately 0.5 deg (approximately 100-150 microm) of the fovea, image and delineate ten retinal layers, and penetrate to resolve features deep into the choroid. The prototype presented here is the first of a new class of powerful flexible imaging platforms that will provide clinicians and researchers with high-resolution, high performance adaptive optics imaging to help guide therapies, develop new drugs, and improve patient outcomes.

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    • "These reports were followed by a large number of developments that targeted improvements in AO-OCT system design and performance , and included an expanded list of laboratories pursuing AO-OCT [34]–[43]. Today, Fd-OCT is employed in almost all AO-OCT systems, with spectral-domain OCT (Sd-OCT) the principal design configuration and swept source OCT (SS-OCT) gaining increased interest due to higher imaging speeds and flatter sensitivity roll-off with depth [44]. To date, AO-based instruments for in vivo human retinal imaging other than AO-OCT have been most successful in imaging the photoreceptor mosaic, including recent reports of foveal cone [45], [46] and rod photoreceptors [47]–[49]. "
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    ABSTRACT: Recent progress in retinal image acquisition techniques, including optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO), combined with improved performance of adaptive optics (AO) instrumentation, has resulted in improvement in the quality of in vivo images of cellular structures in the human retina. Here, we present a short review of progress on developing AO-OCT instruments. Despite significant progress in imaging speed and resolution, eye movements present during acquisition of a retinal image with OCT introduce motion artifacts into the image, complicating analysis and registration. This effect is especially pronounced in high-resolution datasets acquired with AO-OCT instruments. Several retinal tracking systems have been introduced to correct retinal motion during data acquisition. We present a method for correcting motion artifacts in AO-OCT volume data after acquisition using simultaneously captured adaptive optics-scanning laser ophthalmoscope (AO-SLO) images. We extract transverse eye motion data from the AO-SLO images, assign a motion adjustment vector to each AO-OCT $A$-scan, and re-sample from the scattered data back onto a regular grid. The corrected volume data improve the accuracy of quantitative analyses of microscopic structures.
    Full-text · Article · Mar 2014 · IEEE Journal of Selected Topics in Quantum Electronics
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    • "To generate quantitative metrics of the photoreceptor mosaic, identification of individual photoreceptors is often a required step. Since manual identification is extremely time-consuming, many groups have utilized some form of automation when studying the photoreceptor mosaic [9,12,14,17,18,27]. Cone identification algorithms have also been developed and validated for accuracy [39–43]; the Garrioch et al. 2012 algorithm [44], for example, is a modified version of the Li & Roorda 2007 algorithm [39] and was thoroughly validated for repeatability on a large cone mosaic data set. "
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    ABSTRACT: Geometrical analysis of the photoreceptor mosaic can reveal subclinical ocular pathologies. In this paper, we describe a fully automatic algorithm to identify and segment photoreceptors in adaptive optics ophthalmoscope images of the photoreceptor mosaic. This method is an extension of our previously described closed contour segmentation framework based on graph theory and dynamic programming (GTDP). We validated the performance of the proposed algorithm by comparing it to the state-of-the-art technique on a large data set consisting of over 200,000 cones and posted the results online. We found that the GTDP method achieved a higher detection rate, decreasing the cone miss rate by over a factor of five.
    Full-text · Article · Jun 2013 · Biomedical Optics Express
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    • "However, these segmentation algorithms were often developed for specific ophthalmic applications, rather than as a general framework applicable to both layer and closed-contour features. Furthermore, in some cases such as with photoreceptor or corneal cell identification, cell counts or densities were achieved but the actual structure size and shape were not necessarily determined [27,29,31–35,37]. "
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    ABSTRACT: This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.
    Full-text · Article · May 2012 · Biomedical Optics Express
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