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Adaptive optics OCT cross-sectional and en face images extracted from the photoreceptor-RPE complex in a 25–year-old subject (S1) at 78 temporal retina. (a) Averaged B-scan and corresponding A-scan profile of a registered AO-OCT volume reveal distinct reflectance bands within the complex labeled IS/OS, COST, ROST, and RPE. En face projection views of (b) single and (c) averaged registered frames are shown for IS/OS þ COST, ROST, and RPE layers. For brevity, IS/OS and COST are shown combined due to their similar appearance . 34,38 Averaged, registered frames reveal darkened cone projections that are surrounded by a halo of elevated reflectance at ROST. A similar pattern occurs at RPE, but of coarser grain and no apparent relation to the cone mosaic. (d) Magnified views of the yellow highlighted subregion in (c) provide better visualization of cellular detail. For comparison, copies of the magnified images also are shown with cell locations marked. (e) The same magnified cone view is shown with all three marks superimposed. In the Supplementary Video S1, frames #14, #24, #30, and #35 pertain to IS/OS, COST, ROST, and RPE, respectively. As expected at 78 retinal eccentricity, the cone reflection at IS/OS (#14) appears multimodal and at COST (#24) single mode. 34 Scale bar: 50 lm.
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Purpose:
Dysfunction of the retinal pigment epithelium (RPE) underlies numerous retinal pathologies, but biomarkers sensitive to RPE change at the cellular level are limited. In this study, we used adaptive optics optical coherence tomography (AO-OCT) in conjunction with organelle motility as a novel contrast mechanism to visualize RPE cells and c...
Contexts in source publication
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... the motion-corrected cone (IS/OS þ COST), ROST, and RPE images were averaged across the selected volumes to generate averaged, registered en face images for cone, ROST, and RPE, an example of which is given in Figure 1. For reference purposes, the depth at which the en face images were extracted was measured relative to the IS/OS layer (zero location). ...
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... patches of retina were successfully imaged and registered in all six subjects and two retinal eccentricities. Representative single and averaged, registered images acquired at 78 retinal eccentricity in one subject are shown in Figure 1 (Supplementary Video S1 shows en face flythrough of the averaged, registered volume). The averaged B-scan and averaged The same magnified cone view is shown with all three marks superimposed. ...
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... profile in Figure 1a reveal distinct reflectance bands within the photoreceptor-RPE complex. These are labeled IS/ OS, COST, ROST, and RPE with their corresponding single-frame en face projections shown in Figure 1b. ...
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... profile in Figure 1a reveal distinct reflectance bands within the photoreceptor-RPE complex. These are labeled IS/ OS, COST, ROST, and RPE with their corresponding single-frame en face projections shown in Figure 1b. As expected, the single en face frame (Fig. 1b, top) reveals a regular pattern of bright punctate reflections, each originating from an individual cone cell and consistent with that reported previously with AO- OCT. ...
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... profile in Figure 1a reveal distinct reflectance bands within the photoreceptor-RPE complex. These are labeled IS/ OS, COST, ROST, and RPE with their corresponding single-frame en face projections shown in Figure 1b. As expected, the single en face frame (Fig. 1b, top) reveals a regular pattern of bright punctate reflections, each originating from an individual cone cell and consistent with that reported previously with AO- OCT. 21,31,32,34,[36][37][38] Unlike the cone reflection, a regular pattern is not evident in single-frame images of the ROST (Fig. 1b, middle) and RPE layers (Fig. 1b, bottom). ...
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... Figure 1b. As expected, the single en face frame (Fig. 1b, top) reveals a regular pattern of bright punctate reflections, each originating from an individual cone cell and consistent with that reported previously with AO- OCT. 21,31,32,34,[36][37][38] Unlike the cone reflection, a regular pattern is not evident in single-frame images of the ROST (Fig. 1b, middle) and RPE layers (Fig. 1b, bottom). However, a pattern emerges when registered images of the same patch are averaged. This is illustrated in Figure 1c (middle and bottom) for the averaging of 26 frames. Rod outer segment tips and RPE layers reveal a regular pattern, but with reflectance inverted from that of cones, that is, ...
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... en face frame (Fig. 1b, top) reveals a regular pattern of bright punctate reflections, each originating from an individual cone cell and consistent with that reported previously with AO- OCT. 21,31,32,34,[36][37][38] Unlike the cone reflection, a regular pattern is not evident in single-frame images of the ROST (Fig. 1b, middle) and RPE layers (Fig. 1b, bottom). However, a pattern emerges when registered images of the same patch are averaged. This is illustrated in Figure 1c (middle and bottom) for the averaging of 26 frames. Rod outer segment tips and RPE layers reveal a regular pattern, but with reflectance inverted from that of cones, that is, darkened punctate reflections in a bright ...
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... a pattern emerges when registered images of the same patch are averaged. This is illustrated in Figure 1c (middle and bottom) for the averaging of 26 frames. Rod outer segment tips and RPE layers reveal a regular pattern, but with reflectance inverted from that of cones, that is, darkened punctate reflections in a bright surround. ...
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... on closer inspection, the two mosaic patterns are spatially distinct, in terms of the location and spacing of the darkened punctate reflections. These differences, as well as those with the overlying cone mosaic pattern, are evident in Figures 1d and 1e. To aid the comparison, the bright spots in the cone projection and dark spots in the ROST and RPE layers (see Fig. 1d) are manually marked. ...
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... the two mosaic patterns are spatially distinct, in terms of the location and spacing of the darkened punctate reflections. These differences, as well as those with the overlying cone mosaic pattern, are evident in Figures 1d and 1e. To aid the comparison, the bright spots in the cone projection and dark spots in the ROST and RPE layers (see Fig. 1d) are manually marked. Figure 1e demonstrates the one-to-one correspondence between cone (cyan cross) and darkened spot (red dot) in the ROST, with a psuedo-shadow typically forming underneath each cone. In contrast no correspondence is apparent between cones (or equivalently their psuedo-shadow locations in ROST) and RPE (purple ...
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... aid the comparison, the bright spots in the cone projection and dark spots in the ROST and RPE layers (see Fig. 1d) are manually marked. Figure 1e demonstrates the one-to-one correspondence between cone (cyan cross) and darkened spot (red dot) in the ROST, with a psuedo-shadow typically forming underneath each cone. In contrast no correspondence is apparent between cones (or equivalently their psuedo-shadow locations in ROST) and RPE (purple Voronoi map). ...
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... contrast no correspondence is apparent between cones (or equivalently their psuedo-shadow locations in ROST) and RPE (purple Voronoi map). In fact, for the magnified view shown, there are approximately two times more cones (bright spots) than dark spots in RPE. Figure 2 shows power spectra of the en face images in Figure 1 (cone projection, ROST, and RPE layers). Rings of concentrated energy are evident in the three power spectra, substantiating the observation of regular mosaics in the en face images of Figure 1c. ...
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... fact, for the magnified view shown, there are approximately two times more cones (bright spots) than dark spots in RPE. Figure 2 shows power spectra of the en face images in Figure 1 (cone projection, ROST, and RPE layers). Rings of concentrated energy are evident in the three power spectra, substantiating the observation of regular mosaics in the en face images of Figure 1c. However the rings of ROST and RPE locate at different frequencies in the power spectra, supporting the observation of different spatial cell arrangements in the two layers. ...
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... of our Voronoi analysis are summarized in Figures 8 to 10, which show RPE number of nearest neighbors, Voronoi side length, cell area, cell density, and cone-to-RPE ratio for the six subjects and two retinal eccentricities imaged. Analysis is based on a total of 2997 RPE cells. ...
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... AO-OCT study revealed that the RPE band observed in conventional OCT is actually composed of two distinct, but faint bands. Both bands were visible regardless of age and separated in depth by approximately 10 lm (see Fig. 1a). We labelled these as ROST and RPE. Other AO-OCT and OCT studies also have reported two subbands in the RPE layer 21,37,39 and appear to correspond to ROST and RPE in this study. However, unlike these other studies, we observed the double band in every subject (6 subjects) and retinal location (38 and 78 in the six subjects and 28 to ...
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... should be noted that in the younger eyes an additional more posterior band also is apparent, thus, making the double band actually a triple band, as for example Figure 1a, which shows a faint band immediately below that labeled RPE. In older subjects, this additional band is not evident, for example in Figure 5. ...
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... best characterization of the depth profile of the dual band is with the AO-OCT measurements in Figures 5 and 11, obtained on Subject S5 from 0.68 to 108 retinal eccentricity. These measurements reveal not only the variation in the ROST peak with retinal eccentricity, but also the peak's axial separation from other prominent reflections in the photore- ceptor-RPE complex, namely IS/OS, COST, and RPE. ...
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... measurements reveal not only the variation in the ROST peak with retinal eccentricity, but also the peak's axial separation from other prominent reflections in the photore- ceptor-RPE complex, namely IS/OS, COST, and RPE. As shown in Figure 11, the ROST-to-IS/OS separation (defined as the rod OS length) is relatively constant across retinal eccentricities, on average 34.81 6 0.99 lm, which is consistent with the histologic value of 32 lm summarized by Spaide and Curcio, 30 but shorter than the 40 to 45 lm reported near the optic disc using ultrahigh-resolution OCT. 39 In contrast the COST-to-IS/OS separation decreases from 29.07 to 20.07 lm over the same retinal eccentricity range, a 31% reduction and consistent with histology. ...
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... owing in part to its high refractive index (n ~ 1.7). In addition, growing evidence points to melanin as the source of the strong OCT signal in the RPE layer. 13,45 This makes melanin the likely source of the elevated surround in our images. Consistent with this expectation is the higher SNR we measured for RPE cells at 38 compared to at 78 (see Fig. 12 and discussion in next section). Because of the inverse concentration of melanin and lipofuscin with retinal eccentricity, 28 we would expect the opposite, that is, higher contrast at 78, had lipofuscin been the key scatterer. A further test would have been to analyze reflectance of the cytoplasm as a function of depth in the RPE cell as melanin and lipofuscin also ...
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... do this, we systematically computed SNR for the RPE mosaic as a function of number of images averaged across subjects and retinal eccentricities. Figure 12 plots the results of the SNR analysis along with representative RPE images of different averaging. As evident in the plot, SNR improves proportional to the square root of images averaged, expected if the noise patterns are indepen- dent. ...
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... determine the extent to which these differences manifest themselves for density measurements of the RPE mosaic, we tested their correlation on the same images. Results are shown in Figure 13 and reveal an R 2 of 0.76. Average absolute difference between measure- ment points in the Figure is 9.4% of the average cell density, corresponding to a difference in row-to-row cell spacing of 0.64 lm assuming triangular packing. ...
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... are made here. To facilitate, we chose to use density measurements from the Voronoi analysis instead of power spectra as the former better aligns to the methods used in the literature, some of which were based on Voronoi. From our Voronoi analysis, average RPE cell density for the six subjects at 38 and 78 temporal retinal eccentricity (Fig. 10) were 4975 6 651 cells/mm 2 and 4780 6 354 cells/mm 2 , respectively. Two in vivo imaging studies used autofluorescence AO-SLO 15 and dark-field AO-SLO 19 to image RPE cells in normal, relatively young subjects. For autofluores- cence AO-SLO, three subjects (25-30 years) were imaged along the superior vertical meridian starting at 58, ...
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... also tested for an age dependence across the age range examined: 25 to 61 years. The regression line in Figure 10 shows a decrease in density with age, but this was not statistically significant (P ¼ 0.30). No statistical significance also was found for the corresponding power spectra measurements, but the P value was lower (P ¼ 0.11) and the 95% confidence interval for the regression slope was À56.3 to 7.09 cells/mm 2 /y. ...
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... AO-OCT method captures volume images of the retina and, thus, other layers can be extracted from the same volume, enabling a direct spatial comparison on a cell by cell basis. Figure 6 illustrates this for the spatial arrangement of cone photoreceptors relative to the underlying RPE cells of one subject and Figure 10 quantifies the ratio of these two cell types for all six subjects and two retinal eccentricities. Unlike RPE cell density, which showed no significant difference between 38 and 78, cone-to-RPE ratio did. ...
Similar publications
PURPOSE. Dysfunction of the retinal pigment epithelium (RPE) underlies numerous retinal pathologies, but biomarkers sensitive to RPE change at the cellular level are limited. In this study, we used adaptive optics optical coherence tomography (AO-OCT) in conjunction with organelle motility as a novel contrast mechanism to visualize RPE cells and ch...
Citations
... This high contrast, complex intensity distribution of speckle noise can mask cells and limit the visibility of cellular structures. In particular, the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function 11 have low intrinsic contrast compared to speckle noise and therefore are challenging to image directly 12 . To overcome the low intrinsic contrast, a large number of AO-OCT volumes need to be averaged (e.g., 120 volumes) in order to visualize the cells 13 . ...
... Scaling up the averaging approach from 4 to 63 locations would have required nearly 6 h to acquire the same amount of RPE data (note that this does not include any data processing time), which is not readily achievable in clinical practice. This fundamental limitation explains why AO-OCT RPE imaging is currently performed only on a small number of retinal locations 12,13 . ...
... We demonstrated that P-GAN can effectively recover the cellular structure from speckle-obscured AO-OCT images of the RPE. The key feature of our approach is that cellular contrast can be improved using only a single speckled acquisition, completely bypassing the need for sequential volume averaging currently being used for AO-OCT RPE imaging 12,13 . This is an important step towards more routine clinical application of AO-OCT imaging for probing the health of the retinal tissue at the cellular level, especially for the task of morphometric measurements of cell structure across different retinal locations (Figs. 4, 5 and Supplementary Fig. 5). ...
Background
In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput.
Methods
Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study.
Results
We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges.
Conclusions
The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.
... The most commonly used modality to evaluate the RPE state in the clinic is short and near-infrared wavelength autofluorescence scanning laser ophthalmoscope (SLO), which, through excitation of endogenous fluorophores [5] allows the user to observe damage to the RPE [6]. To observe fine cellular changes, various teams applied Adaptive Optics (AO) technology to these modalities, first leading to cellular resolution of autofluorescence images of the RPE in the SLO camera [7][8][9][10][11][12][13] and later on revealing individual cells in OCT cross-sections [14][15][16]. Autofluorescence imaging with the AOSLO requires longer exposure times (from 30 s to 90 s) to provide high-resolution images of the RPE. Autofluorescence imaging in histology samples has enabled the study of the different types of fluorophores that are excited in these cells [17]. ...
... In addition, the larger size of the RPE cells in perifoveal locations compared to fovea [32] facilitates the resolution of individual cells at further eccentricities in both modalities. One concern raised in previous work developing RPE imaging was the possibility that there might be some signal from the rods at certain eccentricities which could be mistaken for RPE cells when they were forming a ring of single cells around the cones [15]. Our region of interest of 8°T-10°T corresponding to 2.5-3.1 mm is beyond eccentricities around 1.35 mm displaying these types of rings and rods [35,36], suggesting the observed cells are indeed RPE cells. ...
The Retinal Pigment Epithelium (RPE) plays a prominent role in diseases such as age-related macular degeneration, but imaging individual RPE cells is challenging due to their high absorption and low autofluorescence emission. The RPE lies beneath the highly reflective photoreceptor layer (PR) and contains absorptive pigments, preventing direct backscattered light detection when the PR layer is intact. Here, we used near-infrared autofluorescence adaptive optics scanning laser ophthalmoscopy (NIRAF AOSLO) and transscleral flood imaging (TFI) in the same healthy eyes to cross-validate these approaches. Both methods revealed a consistent RPE mosaic pattern and appeared to reflect a distribution of fluorophores consistent with findings from histological studies. Interestingly, even in apparently healthy RPE, we observed dynamic changes over months, suggesting ongoing cellular activity or alterations in fluorophore distribution. These findings emphasize the value of NIRAF AOSLO and TFI in understanding RPE morphology and dynamics.
... With real-time sensing and the correction of the eye's aberrations, AO-based retinal imaging systems allow us to obtain cellular and subcellular resolution to measure retinal structure and function in vivo. By combining AO with numerous retinal imaging modalities like adaptive optics scanning laser ophthalmoscope (AOSLO) [1,2], adaptive optics optical coherence tomography (AO-OCT) [3][4][5], and adaptive optics flood-illumination cameras [6][7][8][9], it has become possible to obtain quantitative measures of cone photoreceptors [10][11][12][13], nerve fiber bundles [14][15][16], ganglion cells [17][18][19], RPE [20][21][22][23] and the retinal vasculature [24]. ...
We present a fully automatic montage pipeline for adaptive optics SLO retinal images. It contains a flexible module to estimate the translation between pairwise images. The user can change modules to accommodate the alignment of the dataset using the most appropriate alignment technique, provided that it estimates the translation between image pairs and provides a quantitative confidence metric for the match between 0 and 1. We use these pairwise comparisons and associated metrics to construct a graph where nodes represent frames and edges represent the overlap relations. We use a small diameter spanning tree to determine the best pairwise alignment for each image based on the entire set of image relations. The final stage of the pipeline is a blending module that uses dynamic programming to improve the smoothness of the transition between frames. Data sets ranging from 26 to 119 images were obtained from individuals aged 24 to 81 years with a mix of visually normal control eyes and eyes with glaucoma or diabetes. The resulting automatically generated montages were qualitatively and quantitatively compared to results from semi-automated alignment. Data sets were specifically chosen to include both high quality and medium quality data. The results obtained from the automatic method are comparable or better than results obtained by an experienced operator performing semi-automated montaging. For the plug-in pairwise alignment module, we tested a technique that utilizes SIFT + RANSAC, Normalized cross-correlation (NCC) and a combination of the two. This pipeline produces consistent results not only on outer retinal layers, but also on inner retinal layers such as a nerve fiber layer or images of the vascular complexes, even when images are not of excellent quality.
... In adaptive optics OCT, advances toward imaging of additional cell classes, such as retinal ganglion cells and macrophages, have come from higher speed devices with improved image processing and registration that enable the averaging of many volumetric data sets. 8,19,20 In AOSLO, additional cell classes and structures have recently become accessible mostly due to changes in how the light is detected. Nonconfocal AOSLO has enabled imaging of structures that are not visible using traditional confocal imaging. ...
Purpose
Putative microglia were recently detected using adaptive optics ophthalmoscopy in healthy eyes. Here we evaluate the use of nonconfocal adaptive optics scanning light ophthalmoscopy (AOSLO) for quantifying the morphology and motility of presumed microglia and other immune cells in eyes with retinal inflammation from uveitis and healthy eyes.
Design
Observational exploratory study.
Participants
Twelve participants were imaged, including 8 healthy participants and 4 posterior uveitis patients recruited from the clinic of 1 of the authors (M.H.E.).
Methods
The Pittsburgh AOSLO imaging system was used with a custom-designed 7-fiber optical fiber bundle for simultaneous confocal and nonconfocal multioffset detection. The inner retina was imaged at several locations at multiple timepoints in healthy participants and uveitis patients to generate time-lapse images.
Main Outcome Measures
Microglia and macrophages were manually segmented from nonconfocal AOSLO images, and their morphological characteristics quantified (including soma size, diameter, and circularity). Cell soma motion was quantified across time for periods of up to 30 minutes and their speeds were calculated by measuring their displacement over time.
Results
A spectrum of cell morphologies was detected in healthy eyes from circular amoeboid cells to elongated cells with visible processes, resembling activated and ramified microglia, respectively. Average soma diameter was 16.1 ± 0.9 μm. Cell movement was slow in healthy eyes (0.02 μm/sec on average), but macrophage-like cells moved rapidly in some uveitis patients (up to 3 μm/sec). In an eye with infectious uveitis, many macrophage-like cells were detected; during treatment their quantity and motility decreased as vision improved.
Conclusions
In vivo adaptive optics ophthalmoscopy offers promise as a potentially powerful tool for detecting and monitoring inflammation and response to treatment at a cellular level in the living eye.
Financial Disclosure(s)
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
... The graders reported that the identification of the interdigitation zone in the control group with the High-Res OCT device was impeded as it often appeared to split up into two different retinal layers. This observation is in line with findings from adaptive optics OCT [39]: photoreceptor related layers between the ellipsoid zone and the RPE are probably split up into the cone outer segment tip (COST) and the rod outer segment tips (ROST). As shown in Figure 1, splitting cannot be observed at the fovea where rod photoreceptors are absent. ...
Optical coherence tomography (OCT) enables in vivo diagnostics of individual retinal layers in the living human eye. However, improved imaging resolution could aid diagnosis and monitoring of retinal diseases and identify potential new imaging biomarkers. The investigational high-resolution OCT platform (High-Res OCT; 853 nm central wavelength, 3 µm axial-resolution) has an improved axial resolution by shifting the central wavelength and increasing the light source bandwidth compared to a conventional OCT device (880 nm central wavelength, 7 µm axial-resolution). To assess the possible benefit of a higher resolution, we compared the retest reliability of retinal layer annotation from conventional and High-Res OCT, evaluated the use of High-Res OCT in patients with age-related macular degeneration (AMD), and assessed differences of both devices on subjective image quality. Thirty eyes of 30 patients with early/intermediate AMD (iAMD; mean age 75 ± 8 years) and 30 eyes of 30 age-similar subjects without macular changes (62 ± 17 years) underwent identical OCT imaging on both devices. Inter- and intra-reader reliability were analyzed for manual retinal layer annotation using EyeLab. Central OCT B-scans were graded for image quality by two graders and a mean-opinion-score (MOS) was formed and evaluated. Inter- and intra-reader reliability were higher for High-Res OCT (greatest benefit for inter-reader reliability: ganglion cell layer; for intra-reader reliability: retinal nerve fiber layer). High-Res OCT was significantly associated with an improved MOS (MOS 9/8, Z-value = 5.4, p < 0.01) mainly due to improved subjective resolution (9/7, Z-Value 6.2, p < 0.01). The retinal pigment epithelium drusen complex showed a trend towards improved retest reliability in High-Res OCT in iAMD eyes but without statistical significance. Improved axial resolution of the High-Res OCT benefits retest reliability of retinal layer annotation and improves perceived image quality and resolution. Automated image analysis algorithms could also benefit from the increased image resolution.
... Early diagnosis, prognosis, and treatment of retinal neurodegenerative diseases are enhanced with the visualization of retinal cell populations in patients. Adaptive optics (AO) enabled imaging, such as AO scanning laser ophthalmoscopy (AO-SLO) [1][2][3][4][5][6][7][8][9][10] and AO optical coherence tomography (AO-OCT) [11][12][13][14][15][16][17][18][19][20][21], allow enhanced in vivo visualization of human retinal cells, including photoreceptors [22,23]. Photoreceptors degenerate in many of the most common retinal diseases, such as age-related macular degeneration (AMD) [24], and inherited retinal diseases, such as retinitis pigmentosa (RP) [25,26], resulting in reduced vision and, ultimately, blindness. ...
Objective quantification of photoreceptor cell morphology, such as cell diameter and outer segment length, is crucial for early, accurate, and sensitive diagnosis and prognosis of retinal neurodegenerative diseases. Adaptive optics optical coherence tomography (AO-OCT) provides three-dimensional (3-D) visualization of photoreceptor cells in the living human eye. The current gold standard for extracting cell morphology from AO-OCT images involves the tedious process of 2-D manual marking. To automate this process and extend to 3-D analysis of the volumetric data, we propose a comprehensive deep learning framework to segment individual cone cells in AO-OCT scans. Our automated method achieved human-level performance in assessing cone photoreceptors of healthy and diseased participants captured with three different AO-OCT systems representing two different types of point scanning OCT: spectral domain and swept source.
... (I) Foveal mosaic of enlarged RPE cells visualized using near infra-red autofluorescence in a case of radiation retinopathy that caused loss of photoreceptors at the same location [115]. Panels Alternate methods of providing contrast for visualization of the RPE mosaic have also been employed including non-confocal dark-field imaging [11] (Fig. 6(C)), and AOOCT [123] ( Fig. 6(D)). Non-confocal dark-field imaging reveals the RPE structure through multiply-scattered light [11,16,124]. ...
... The technique however, may be best for revealing the foveal RPE especially when subject pigment is lighter, as in Caucasian subjects [11]. AOOCT imaging of the RPE mosaic is made possible by organelle motility over time changing the noise pattern observed from the RPE layer [123]. Subcellular registration techniques in three dimensions combined with averaging of volumes over time was critical for success and recent AOOCT studies have reduced the averaging required to observe the RPE [125][126][127]. ...
Twenty-five years ago, adaptive optics (AO) was combined with fundus photography, thereby initiating a new era in the field of ophthalmic imaging. Since that time, clinical applications of AO ophthalmoscopy to investigate visual system structure and function in both health and disease abound. To date, AO ophthalmoscopy has enabled visualization of most cell types in the retina, offered insight into retinal and systemic disease pathogenesis, and been integrated into clinical trials. This article reviews clinical applications of AO ophthalmoscopy and addresses remaining challenges for AO ophthalmoscopy to become fully integrated into standard ophthalmic care.
... AO was successively integrated into conventional fundus imaging (i.e., flood illumination-detection ophthalmoscopy) [2], confocal scanning laser ophthalmoscopy (SLO) [3], and optical coherence tomography (OCT) [4] to achieve cellular-level performance [1,[5][6][7][8]. AO has been successfully used to demonstrate resolution of cells and structures throughout the lamellar retina in the living human, first relatively large and bright cells/structures such as wave-guiding cone photoreceptors [2], nerve fiber bundles [9], and retinal vasculature [10]; and then smaller, transparent, or difficult to localize cells/structures including rod photoreceptors [11], the monolayered retinal pigment epithelium (RPE) [12][13][14][15], transparent ganglion cells in the inner retina [16], immune cells [17][18][19], and the dense choriocapillaris [20,21]. Along the way novel AO-SLO detection methods have been implemented for signal enhancement, notably single and multiphoton fluorescence techniques demonstrated in non-human primates, to probe retinal molecular activity [22,23], and non-confocal split detection approaches that use multiply scattered light for enhanced contrast and sensitivity to transparent cells [24], vessel walls [25], and the inner segments of cone photoreceptors [26]. ...
... Cellular details were also resolved with the system focus set to the outer retina at the photoreceptor-RPE complex (Protocol 2). A total of 10 videos (19 volumes/video) were acquired at 3°temporal retina from S2 with 1.5 minute video separation to allow RPE organelle motility for RPE imaging [13,48]. The AO-OCT images reveal: a radial pattern of Henle fibers at the Henle fiber layer (HFL) (Fig. 5(A)); the mottled reflections surrounding cones at the external limiting membrane (ELM) (Fig. 5(B)) resulting in a negative correspondence in reflectance with the cones (Fig. 5(C)-(E)) at IS, IS/OS and COST; high spatial frequency content with smaller features, presumably individual rod photoreceptors, at the rod outer segment tip (ROST) layer (Fig. 5(F)); and the hexagonal arranged retinal pigment epithelial (RPE) cell mosaic at the RPE layer (Fig. 5 G). ...
We describe the design and performance of a multimodal and multifunctional adaptive optics (AO) system that combines scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) for simultaneous retinal imaging at 13.4 Hz. The high-speed AO-OCT channel uses a 3.4 MHz Fourier-domain mode-locked (FDML) swept source. The system achieves exquisite resolution and sensitivity for pan-macular and transretinal visualization of retinal cells and structures while providing a functional assessment of the cone photoreceptors. The ultra-high speed also enables wide-field scans for clinical usability and angiography for vascular visualization. The FDA FDML-AO system is a powerful platform for studying various retinal and neurological diseases for vision science research, retina physiology investigation, and biomarker development.
... Autofluorescence quenching tools such as TrueBlack can be used for both fixed and live imaging human donor RPE (Kaur et al., 2018;La Cunza et al., 2021). Development of new imaging technologies such as three-photon microscopy that can image pigmented RPE or increased resolution of adaptive optics optical coherence tomography (AO-OCT) would allow monitoring mitochondrial dynamics in the living eye (Liu et al., 2016;Shirazi et al., 2020;Bower et al., 2021). ...
Mitochondrial dysfunction is strongly implicated in neurodegenerative diseases including age-related macular degeneration (AMD), which causes irreversible blindness in over 50 million older adults worldwide. A key site of insult in AMD is the retinal pigment epithelium (RPE), a monolayer of postmitotic polarized cells that performs essential functions for photoreceptor health and vision. Recent studies from our group and others have identified several features of mitochondrial dysfunction in AMD including mitochondrial fragmentation and bioenergetic defects. While these studies provide valuable insight at fixed points in time, high-resolution, high-speed live imaging is essential for following mitochondrial injury in real time and identifying disease mechanisms. Here, we demonstrate the advantages of live imaging to investigate RPE mitochondrial dynamics in cell-based and mouse models. We show that mitochondria in the RPE form extensive networks that are destroyed by fixation and discuss important live imaging considerations that can interfere with accurate evaluation of mitochondrial integrity such as RPE differentiation status and acquisition parameters. Our data demonstrate that RPE mitochondria show localized heterogeneities in membrane potential and ATP production that could reflect focal changes in metabolism and oxidative stress. Contacts between the mitochondria and organelles such as the ER and lysosomes mediate calcium flux and mitochondrial fission. Live imaging of mouse RPE flatmounts revealed a striking loss of mitochondrial integrity in albino mouse RPE compared to pigmented mice that could have significant functional consequences for cellular metabolism. Our studies lay a framework to guide experimental design and selection of model systems for evaluating mitochondrial health and function in the RPE.
... We have also shown that AO-ICG can be used to visualize the choriocapillaris 36 . AO-ICG in combination with other modalities, particularly adaptive optics optical coherence tomography (AO-OCT), provides a means to visualize the RPE mosaic [37][38][39] . Taken together, these techniques enable in vivo multimodal assessment of the photoreceptor, RPE, choriocapillaris complex at the cellular level in both affected males and female carriers of choroideremia. ...
... For AO-OCT imaging (performed in 5 female carriers and 3 affected males), a 1.5 degree field of view (300 × 300 pixels) was used. Repeated AO-OCT volumes were collected in order to reveal the RPE [37][38][39] . For photoreceptor imaging, 50 volumes were obtained for each retinal location and for RPE imaging, 125-150 volumes were obtained for each retinal location. ...
Choroideremia is an X-linked, blinding retinal degeneration with progressive loss of photoreceptors, retinal pigment epithelial (RPE) cells, and choriocapillaris. To study the extent to which these layers are disrupted in affected males and female carriers, we performed multimodal adaptive optics imaging to better visualize the in vivo pathogenesis of choroideremia in the living human eye. We demonstrate the presence of subclinical, widespread enlarged RPE cells present in all subjects imaged. In the fovea, the last area to be affected in choroideremia, we found greater disruption to the RPE than to either the photoreceptor or choriocapillaris layers. The unexpected finding of patches of photoreceptors that were fluorescently-labeled, but structurally and functionally normal, suggests that the RPE blood barrier function may be altered in choroideremia. Finally, we introduce a strategy for detecting enlarged cells using conventional ophthalmic imaging instrumentation. These findings establish that there is subclinical polymegathism of RPE cells in choroideremia.
