Influence of sampling window size and orientation on parafoveal cone packing density

Quantitative Analysis of the Vasculature and Cone Photoreceptors in Subjects With Diabetes Without Diabetic Retinopathy

PURPOSE

To characterize any differences in the vasculature and cone photoreceptor packing geometry (CPG) between subjects with diabetes without/no diabetic retinopathy (NDR) and healthy controls.

METHODS

Eight NDR and five controls were enrolled. Optical coherence tomography angiography (OCTA) taken at the macula was used to measure vessel density, vessel length density, and vessel density index (VDI) in three vascular plexuses, namely, the superficial vascular plexus, intermediate capillary plexus, and deep capillary plexus (DCP). The choriocapillaris (CC) flow deficit (FD) was also measured. OCTA images were binarized and processed to extrapolate the parafovea and parafoveal quadrants and the OCTA indices mentioned above. The CC was processed with six different radii to quantify FD. Adaptive optics - scanning laser ophthalmoscopy images were acquired and processed to extract CPG indices, i.e., cone density (CD), cone-to-cone spacing (CS), linear dispersion index, heterogeneity packing index and percent of cells with six neighbors at 3.6° in the temporal retina.

RESULTS

In all eyes, statistically significant differences were found (i) in parafoveal FD across the six radii (p < 0.001) and (ii) in the correlation between the parafoveal temporal quadrant (PTQ) DCP VDI and CS (r = 0.606, p = 0.048). No other significant correlations were found. For OCTA or CPG indices, no significant differences were found between the cohorts in the parafovea or parafoveal quadrants.

CONCLUSIONS

CS is the most sensitive CPG index for detecting alterations in the cone mosaic. The DCP and the cone photoreceptors are significantly correlated, indicating that alterations in the DCP can affect the cones. Future work elucidating the vascular alterations and neurodegeneration present in diabetic eyes should focus on the DCP and multiple CPG indices, not solely CD. Moreover, such alterations are highly localized, hence using larger regions e.g. parafovea versus smaller areas, such as the PTQ, will potentially mask significant correlations.

The effect of sampling window size on topographical maps of foveal cone density

Purpose

To characterize the effect of sampling window size on maps of foveal cone density derived from adaptive optics scanning light ophthalmoscope (AOSLO) images of the cone mosaic.

Methods

Forty-four AOSLO-derived montages of the foveal cone mosaic (300 x 300µm) were used for this study (from 44 individuals with normal vision). Cone photoreceptor coordinates were semi-automatically identified by one experienced grader. From these coordinates, cone density matrices across each foveal montage were derived using 10 different sampling window sizes containing 5, 10, 15, 20, 40, 60, 80, 100, 150, or 200 cones. For all 440 density matrices, we extracted the location and value of peak cone density (PCD), the cone density centroid (CDC) location, and cone density at the CDC.

Results

Across all window sizes, PCD values were larger than those extracted at the CDC location, though the difference between these density values decreased as the sampling window size increased (p<0.0001). Overall, both PCD (r=-0.8099, p=0.0045) and density at the CDC (r=-0.7596, p=0.0108) decreased with increasing sampling window size. This reduction was more pronounced for PCD, with a 27.8% lower PCD value on average when using the 200-cone versus the 5-cone window (compared to only a 3.5% reduction for density at the CDC between these same window sizes). While the PCD and CDC locations did not occur at the same location within a given montage, there was no significant relationship between this PCD-CDC offset and sampling window size (p=0.8919). The CDC location was less variable across sampling windows, with an average per-participant 95% confidence ellipse area across the 10 window sizes of 47.56µm² (compared to 844.10µm² for the PCD location, p<0.0001).

Conclusion

CDC metrics appear more stable across varying sampling window sizes than PCD metrics. Understanding how density values change according to the method used to sample the cone mosaic may facilitate comparing cone density data across different studies.

Extracting spacing-derived estimates of rod density in healthy retinae

Quantification of the rod photoreceptor mosaic using adaptive optics scanning light ophthalmoscopy (AOSLO) remains challenging. Here we demonstrate a method for deriving estimates of rod density and rod:cone ratio based on measures of rod spacing, cone numerosity, and cone inner segment area. Twenty-two AOSLO images with complete rod visualization were used to validate this spacing-derived method for estimating density. The method was then used to estimate rod metrics in an additional 105 images without complete rod visualization. The spacing-derived rod mosaic metrics were comparable to published data from histology. This method could be leveraged to develop large normative databases of rod mosaic metrics, though limitations persist with intergrader variability in assessing cone area and numerosity.

Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy

The foveal cone mosaic can be directly visualized using adaptive optics scanning light ophthalmoscopy (AOSLO). Previous studies in individuals with normal vision report wide variability in the topography of the foveal cone mosaic, especially the value of peak cone density (PCD). While these studies often involve a human grader, there have been no studies examining intergrader reproducibility of foveal cone mosaic metrics. Here we re-analyzed published AOSLO foveal cone images from 44 individuals to assess the relationship between the cone density centroid (CDC) location and the location of PCD. Across 5 graders with variable experience, we found a measurement error of 11.7% in PCD estimates and higher intergrader reproducibility of CDC location compared to PCD location (p < 0.0001). These estimates of measurement error can be used in future studies of the foveal cone mosaic, and our results support use of the CDC location as a more reproducible anchor for cross-modality analyses.

Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy

Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality.

High-resolution imaging of photoreceptors in healthy human eyes using an adaptive optics retinal camera

PURPOSE

To determine the effects of age on perifoveal cone density in healthy subjects using adaptive optics.

METHODS

Healthy subjects of various ages were imaged using an adaptive optics retinal camera (RTX-1^® Imagine Eyes, Orsay, France). All patients underwent a comprehensive ophthalmologic examination and retinal imaging using spectral-domain optical coherence tomography (Spectralis^®, Heidelberg Engineering, Heidelberg, Germany). Cone density together with cone spacing and cone mosaic packing were measured in the nasal and temporal area 450 µm from the fovea. A multivariate analysis was performed to determine which of the following parameters were related to a decrease in cone density: age, axial length, central macular thickness, and retrofoveal choroidal thickness.

RESULTS

One hundred and sixty-seven eyes of 101 subjects aged 6-78 years were studied. Perifoveal cone density significantly decreased with age (R² = 0.17, p<0.01). Inversely, cone spacing increased with age (R²=0.18, p<0.01). There was no change in the cone packing mosaic (p>0.05). The mean coefficient of variation between fellow eyes was 3.9%. Age and axial length were related to a cone density decrease, while choroidal and retinal thicknesses did not affect cone metrics in healthy subjects.

CONCLUSIONS

A moderate perifoveal cone loss occurs with age. The precise consequences of these findings on visual function should be investigated. In addition to a better understanding of normal retinal anatomy, these results could act as a comparative database for further studies on normal and diseased retinas.

The Reliability of Cone Density Measurements in the Presence of Rods

Purpose

Recent advances in adaptive optics scanning light ophthalmoscopy (AOSLO) have enabled visualization of cone inner segments through nonconfocal split-detection, in addition to rod and cone outer segments revealed by confocal reflectance. Here, we examined the interobserver reliability of cone density measurements in both AOSLO imaging modalities.

Methods

Five normal subjects (nine eyes) were imaged along the horizontal and vertical meridians using a custom AOSLO with confocal and nonconfocal split-detection modalities. The resulting images were montaged using a previously described semiautomatic algorithm. Regions of interest (ROIs) were selected from the confocal montage at 190 μm, and from split-detection and confocal montages at 900 and 1800 μm from the fovea. Four observers (three experts, one naïve) manually identified cone locations in each ROI, and these locations were used to calculate bound densities. Intraclass correlation coefficients and Dice's coefficients were calculated to assess interobserver agreement.

Results

Interobserver agreement was high in cone-only images (confocal 190 μm: 0.85; split-detection 900 μm: 0.91; split-detection 1800 μm: 0.89), moderate in confocal images at 900 μm (0.68), and poor in confocal images at 1800 μm (0.24). Excluding the naïve observer data substantially increased agreement within confocal images (190 μm: 0.99; 900 μm: 0.80; 1800 μm: 0.68).

Conclusions

Interobserver measurements of cone density are more reliable in rod-free retinal images. Moreover, when using manual cell identification, it is essential that observers are trained, particularly for confocal AOSLO images.

Translational Relevance

This study underscores the need for additional reliability studies in eyes containing pathology where identifying cones can be substantially more difficult.

Use of focus measure operators for characterization of flood illumination adaptive optics ophthalmoscopy image quality

Adaptive optics flood illumination ophthalmoscopy (AO-FIO) allows imaging of the cone photoreceptor in the living human retina. However, clinical interpretation of the AO-FIO image remains challenging due to suboptimal quality arising from residual uncorrected wavefront aberrations and rapid eye motion. An objective method of assessing image quality is necessary to determine whether an AO-FIO image is suitable for grading and diagnostic purpose. In this work, we explore the use of focus measure operators as a surrogate measure of AO-FIO image quality. A set of operators are tested on data sets acquired at different focal depths and different retinal locations from healthy volunteers. Our results demonstrate differences in focus measure operator performance in quantifying AO-FIO image quality. Further, we discuss the potential application of the selected focus operators in (i) selection of the best quality AO-FIO image from a series of images collected at the same retinal location and (ii) assessment of longitudinal changes in the diseased retina. Focus function could be incorporated into real-time AO-FIO image processing and provide an initial automated quality assessment during image acquisition or reading center grading.

Visual resolution and cone spacing in the nasal and inferior retina

PURPOSE

To determine the retinal eccentricity at which cones are no longer an observable substitute for ganglion cells on nasal and inferior parafoveal visual acuity.

METHOD

Visual acuities were measured on 12 healthy volunteers, under dynamic adaptive optic aberrations correction (crx1™) in white light, from 0° to 6°, every two degrees, along the nasal and inferior retinal meridians. Cone spacing was measured on images of the retina acquired using an adaptive optic flood illumination retina camera (rtx1™) at the same eccentricity, except at 0°.

RESULTS

Cone spacing increased by around 0.13 min of arc per degree of eccentricity and a difference of 7% between both meridians was observed (higher cone spacing in the inferior retinal meridian). Visual resolution was higher in the nasal retinal meridian (difference of around 28% or 0.15 logMAR at 6°). Cone spacing can predict minimum angle of resolution (MAR) at 2° in both semi retinal meridians. In the inferior retinal meridian, MAR measurements are fairly well predicted by Watson's 50% mathematical model based on the midget retinal ganglion cell density. Along the nasal retinal meridian, the measured MAR lies between Watson's 50% and 100% models.

CONCLUSIONS

At 2° of eccentricity, cone density accurately predicts visual resolution in both the nasal and inferior retina, supporting the idea that only 50% of the foveal midget retinal ganglion cells determine VA. The 50% model can also predict VA in the inferior retinal meridian at 4° and 6° of eccentricity. However, the 50% model underestimated visual acuity in the nasal retinal meridian at 4° and 6° of eccentricity consistent with the partially overlapping ON and OFF midget retinal ganglion cell receptive fields.

Multimodal imaging quality control of epithelia regenerated with cultured human donor corneal limbal epithelial stem cells

Current imaging techniques for the characterization of differentiated corneal limbal stem cells are destructive and cannot be used in eye bank for monitoring the regenerated epithelium in culture. We presented a minimally invasive, multimodal, marker-free imaging method for the investigation of epithelia regenerated with cultured human donor corneal limbal epithelial stem cells. Two-photon fluorescence and harmonic generation signals were collected from specimens in culture and used for evaluating the structure and morphology of epithelia cultured on two different bio-scaffolds; in addition, donor human corneal tissues were used as controls. The method provided reliable information on the organization of cellular and extracellular components of biomaterial substrates and was highly sensitive to determine differences between the density packing arrangement of epithelial cells of different biomaterials without relying on inferences from exogenous labels. The present minimally invasive standardized quality control methodology can be reliably translated to eye banks and used for monitoring harvested corneal limbal stem cells growth and differentiation in bioengineered materials.

Human photoreceptor cone density measured with adaptive optics technology (rtx1 device) in healthy eyes: Standardization of measurements

The anatomic structures of the anterior segment of the eye enable correct reception of stimuli by the retina, which contains receptors that receive light impulses and transmit them to the visual cortex. The aim of this study was to assess the effect of the size of the sampling window in an adaptive optics (AO) flood-illumination retinal camera (rtx1) on cone density measurements in the eyes of healthy individuals and to investigate the differences in cone density and spacing in different quadrants of the retina. Thirty-three subjects with no ophthalmic or systemic disease underwent a detailed ophthalmologic examination. Photographs of retinal fragments 3 degrees from the fovea were taken using the rtx1 AO retinal camera. We used sampling windows with 3 sizes (50 × 50, 100 × 100, and 250 × 250 μm). Cone density, spacing, and shape were determined using AOdetect software. The median (interquartile range) cone density was 19,269 (4964) cones/mm. There were statistically significant differences between measurements taken with the 50/50 and 250/250-m windows. There were no significant differences in the cone spacing results between any of the windows examined, but the measurements differed according to location between the superior and temporal quadrants. The most common cone shape was hexagonal (47.6%) for all window sizes and locations. These findings may help in the development of a normative database for variation in cone density in healthy subjects and to allow the best window to be chosen for obtain the most correct values for eccentricity measurements of 3 degrees. In our study, the optimal sampling window was 100 × 100 μm.

Photoreceptor-Based Biomarkers in AOSLO Retinal Imaging

Improved understanding of the mechanisms underlying inherited retinal degenerations has created the possibility of developing much needed treatments for these relentless, blinding diseases. However, standard clinical indicators of retinal health (such as visual acuity and visual field sensitivity) are insensitive measures of photoreceptor survival. In many retinal degenerations, significant photoreceptor loss must occur before measurable differences in visual function are observed. Thus, there is a recognized need for more sensitive outcome measures to assess therapeutic efficacy as numerous clinical trials are getting underway. Adaptive optics (AO) retinal imaging techniques correct for the monochromatic aberrations of the eye and can be used to provide nearly diffraction-limited images of the retina. Many groups routinely are using AO imaging tools to obtain in vivo images of the rod and cone photoreceptor mosaic, and it now is possible to monitor photoreceptor structure over time with single cell resolution. Highlighting recent work using AO scanning light ophthalmoscopy (AOSLO) across a range of patient populations, we review the development of photoreceptor-based metrics (e.g., density/geometry, reflectivity, and size) as candidate biomarkers. Going forward, there is a need for further development of automated tools and normative databases, with the latter facilitating the comparison of data sets across research groups and devices. Ongoing and future clinical trials for inherited retinal diseases will benefit from the improved resolution and sensitivity that multimodal AO retinal imaging affords to evaluate safety and efficacy of emerging therapies.

Automated measurements of human cone photoreceptor density in healthy and degenerative retina by region-based segmentation

Purpose

The purpose of this study was to develop an algorithm based on region-based segmentation for automated calculations of human cone photoreceptor density of en face images obtained by an adaptive optics scanning laser ophthalmoscope (AOSLO).

Subjects and methods

Cone mosaics of 15 eyes of 15 healthy subjects were photographed by a custom-built AOSLO. The cone density was calculated at 0.5, 1.0, and 1.5 mm temporal from the fovea using a region-based segmentation method (RSM) developed in our laboratory. The cone density was also determined by a manual identification method (MIM) and a conventional spatial filtering method (SFM). The cone densities of three eyes of three patients with retinal degeneration were calculated by the three methods and compared to the results from normal eyes.

Results

The cone densities in healthy retinas determined by the RSM at 0.5, 1.0, and 1.5 mm temporal from the fovea were 28,436, 21,233, and 13,620 cells/mm², respectively. These densities were in good agreement with a histological study and with in vivo AOSLO studies. The cone densities determined by RSM were different from those determined by MIM with a difference of 5% in healthy eyes. In eyes with retinal degeneration, with the appropriate threshold-level settings or spatial frequency bandwidth, the cone density measured by MIM was significantly closer to that measured by RSM than by SFM.

Conclusion

These results suggest that our method is more stable than conventional methods in cases of non-periodical photoreceptor structures such as the affected retinal area. Our method can be used in the longitudinal follow-up of retinal degenerative diseases and to determine the effect of therapy.

Investigation of Adaptive Optics Imaging Biomarkers for Detecting Pathological Changes of the Cone Mosaic in Patients with Type 1 Diabetes Mellitus

PURPOSE

To investigate a set of adaptive optics (AO) imaging biomarkers for the assessment of changes of the cone mosaic spatial arrangement in patients with type 1 diabetes mellitus (DM1).

METHODS

16 patients with ≥20/20 visual acuity and a diagnosis of DM1 in the past 8 years to 37 years and 20 age-matched healthy volunteers were recruited in this study. Cone density, cone spacing and Voronoi diagrams were calculated on 160x160 μm images of the cone mosaic acquired with an AO flood illumination retinal camera at 1.5 degrees eccentricity from the fovea along all retinal meridians. From the cone spacing measures and Voronoi diagrams, the linear dispersion index (LDi) and the heterogeneity packing index (HPi) were computed respectively. Logistic regression analysis was conducted to discriminate DM1 patients without diabetic retinopathy from controls using the cone metrics as predictors.

RESULTS

Of the 16 DM1 patients, eight had no signs of diabetic retinopathy (noDR) and eight had mild nonproliferative diabetic retinopathy (NPDR) on fundoscopy. On average, cone density, LDi and HPi values were significantly different (P<0.05) between noDR or NPDR eyes and controls, with these differences increasing with duration of diabetes. However, each cone metric alone was not sufficiently sensitive to discriminate entirely between membership of noDR cases and controls. The complementary use of all the three cone metrics in the logistic regression model gained 100% accuracy to identify noDR cases with respect to controls.

CONCLUSION

The present set of AO imaging biomarkers identified reliably abnormalities in the spatial arrangement of the parafoveal cones in DM1 patients, even when no signs of diabetic retinopathy were seen on fundoscopy.

Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images

The ability to noninvasively image the cone photoreceptor mosaic holds significant potential as a diagnostic for retinal disease. Central to the realization of this potential is the development of sensitive metrics for characterizing the organization of the mosaic. Here we evaluated previously-described and newly-developed (Fourier- and Radon-based) methods of measuring cone orientation in simulated and real images of the parafoveal cone mosaic. The proposed algorithms correlated well across both simulated and real mosaics, suggesting that each algorithm provides an accurate description of photoreceptor orientation. Despite high agreement between algorithms, each performed differently in response to image intensity variation and cone coordinate jitter. The integration property of the Fourier transform allowed the Fourier-based method to be resistant to cone coordinate jitter and perform the most robustly of all three algorithms. Conversely, when there is good image quality but unreliable cone identification, the Radon algorithm performed best. Finally, in cases where the cone coordinate reliability was excellent, the method previously described by Pum and colleagues performed best. These descriptors are complementary to conventional descriptive metrics of the cone mosaic, such as cell density and spacing, and have the potential to aid in the detection of photoreceptor pathology.

Semi-automated identification of cones in the human retina using circle Hough transform

A large number of human retinal diseases are characterized by a progressive loss of cones, the photoreceptors critical for visual acuity and color perception. Adaptive Optics (AO) imaging presents a potential method to study these cells in vivo. However, AO imaging in ophthalmology is a relatively new phenomenon and quantitative analysis of these images remains difficult and tedious using manual methods. This paper illustrates a novel semi-automated quantitative technique enabling registration of AO images to macular landmarks, cone counting and its radius quantification at specified distances from the foveal center. The new cone counting approach employs the circle Hough transform (cHT) and is compared to automated counting methods, as well as arbitrated manual cone identification. We explore the impact of varying the circle detection parameter on the validity of cHT cone counting and discuss the potential role of using this algorithm in detecting both cones and rods separately.

Assessment of Different Sampling Methods for Measuring and Representing Macular Cone Density Using Flood-Illuminated Adaptive Optics

PURPOSE

To describe a standardized flood-illuminated adaptive optics (AO) imaging protocol suitable for the clinical setting and to assess sampling methods for measuring cone density.

METHODS

Cone density was calculated following three measurement protocols: 50 × 50-μm sampling window values every 0.5° along the horizontal and vertical meridians (fixed-interval method), the mean density of expanding 0.5°-wide arcuate areas in the nasal, temporal, superior, and inferior quadrants (arcuate mean method), and the peak cone density of a 50 × 50-μm sampling window within expanding arcuate areas near the meridian (peak density method). Repeated imaging was performed in nine subjects to determine intersession repeatability of cone density.

RESULTS

Cone density montages could be created for 67 of the 74 subjects. Image quality was determined to be adequate for automated cone counting for 35 (52%) of the 67 subjects. We found that cone density varied with different sampling methods and regions tested. In the nasal and temporal quadrants, peak density most closely resembled histological data, whereas the arcuate mean and fixed-interval methods tended to underestimate the density compared with histological data. However, in the inferior and superior quadrants, arcuate mean and fixed-interval methods most closely matched histological data, whereas the peak density method overestimated cone density compared with histological data. Intersession repeatability testing showed that repeatability was greatest when sampling by arcuate mean and lowest when sampling by fixed interval.

CONCLUSIONS

We show that different methods of sampling can significantly affect cone density measurements. Therefore, care must be taken when interpreting cone density results, even in a normal population.

Multimodal Approach to Monitoring and Investigating Cone Structure and Function in an Inherited Macular Dystrophy

PURPOSE

To examine a female subject, her father, and a brother harboring a missense mutation of the retinitis pigmentosa 1-like 1 (RP1L1) gene, over 2 years of follow-up.

DESIGN

Observational case series.

METHODS

setting: Fondazione G.B. Bietti IRCCS, Rome, Italy.

STUDY POPULATION

RP1L1 family members and controls.

MAIN OUTCOME MEASURES

Images of the cone mosaic acquired with an adaptive optics retinal camera, spectral-domain optical coherence tomography (SD OCT), and full-field and multifocal electroretinography (mfERG).

RESULTS

In the proband, best-corrected visual acuity (≤0.7 logMAR) was stable in both eyes during follow-up, though analysis of adaptive optics images showed decreased cone density in the central 9 degrees from the fovea with respect to controls (P < .05) and cone density loss in the parafoveal area (2 degrees; <12%-16%) during follow-up. Texture analysis of SD OCT images identified abnormalities of the ellipsoid zone in the central 7 degrees, while mfERG response amplitudes were reduced only in the central 5 degrees relative to controls. In the proband's father, who had 0.0 logMAR visual acuity, significant cone loss was found in the central 7 degrees from the fovea (P < .05); abnormal SD OCT and mfERG values with respect to controls were found in corresponding retinal areas. No defects in the cone structure and function were found in the proband's brother, who had 0.0 logMAR visual acuity.

CONCLUSIONS

Occult macular dystrophy was diagnosed based on genetic and multimodal ophthalmic findings. The quantitative assessment of photoreceptor survival or loss, based on analysis of adaptive optics retinal images, was valuable to monitor disease progression at a cellular level.

Advances in understanding the molecular basis of the first steps in color vision

Serving as one of our primary environmental inputs, vision is the most sophisticated sensory system in humans. Here, we present recent findings derived from energetics, genetics and physiology that provide a more advanced understanding of color perception in mammals. Energetics of cis-trans isomerization of 11-cis-retinal accounts for color perception in the narrow region of the electromagnetic spectrum and how human eyes can absorb light in the near infrared (IR) range. Structural homology models of visual pigments reveal complex interactions of the protein moieties with the light sensitive chromophore 11-cis-retinal and that certain color blinding mutations impair secondary structural elements of these G protein-coupled receptors (GPCRs). Finally, we identify unsolved critical aspects of color tuning that require future investigation.

Structural and Function Correlation of Cone Packing Utilizing Adaptive Optics and Microperimetry

AIM

To assess the functional aspects of cone mosaic and correlate cone packing with retinal sensitivity utilizing microperimetry in emmetropes at different eccentricities.

METHODS

Twenty-four healthy volunteers underwent microperimetry (MAIA Centervue, Italy) and assessment of photoreceptors using adaptive optics retinal camera, rtx1 (Imagine Eyes, Orsay, France), at 2 and 3 degrees from the foveal centre in 4 quadrants: superior, inferior, temporal, and nasal. Data was analyzed using SPSS version 17 (IBM). Spearman's correlation tests were used to establish correlation between mean cone packing density and retinal sensitivity at different quadrants.

RESULTS

Thirteen females and 11 males (age range 20-40 years) were included. The cone density was found to be significantly different among all quadrants (temporal = 25786.68/mm(2) ± 4367.07/mm(2), superior = 23009.35/mm(2) ± 5415.81/mm(2), nasal = 22838.09/mm(2) ± 4166.22/mm(2), and inferior = 21097.53/mm(2) ± 4235.84/mm(2)). A statistical significance (P < 0.008) was found between orthogonal meridians, that is, temporal, nasal (48624.77/mm(2))> superior, inferior (44106.88/mm(2)). A drop in retinal sensitivity was observed as the eccentricity increased (P < 0.05). It was also found that as cone packing density decreased retinal sensitivity also decreased (P < 0.05) in all quadrants. This was observed at both 2 and 3 degrees.

CONCLUSION

It is of crucial importance to establish normative variations in cone structure-function correlation. This may help in detection of subtle pathology and its early intervention.

Technical factors influencing cone packing density estimates in adaptive optics flood illuminated retinal images

PURPOSE

To investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images.

METHODS

Adaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL). The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr), the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions.

RESULTS

The cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams.

CONCLUSIONS

The sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi diagrams of the cone mosaic.

Reliability of cone counts using an adaptive optics retinal camera

BACKGROUND

To assess the reproducibility and repeatability of cone imaging in healthy human eyes, using the RTx-1 Adaptive Optics Retinal Camera and its proprietary cone-counting software.

DESIGN

Single-centre, prospective study.

PARTICIPANTS

Ten healthy adults.

METHODS

Macular cones were imaged. Intrasession repeatability was assessed by comparing 10 consecutive acquisitions obtained by the same operator from each subject. For the intersession study, each subject was imaged five consecutive days. Interoperator reproducibility was also evaluated by comparing the images obtained from 10 different subjects by two independent operators. Finally, intergrader agreement was evaluated by comparing the cone counts measured by two masked graders.

MAIN OUTCOME MEASURES

Mean cone density (cells/mm(2) ), spacing between cells (μm) and percentage of cones with six neighbours calculated on Voronoi diagrams were measured. Correlation coefficients, intraclass correlation coefficients, and coefficients of variation were calculated.

RESULTS

Correlation coefficient and intraclass correlation coefficient were respectively 0.81 and 0.96 between operators, and 0.97 and 0.98 between the two graders. The intrasession and intersession coefficients of variation were under 7%. The percentage of cells with six neighbours and the spacing between cones varied in the same proportion (coefficients of variation ranged from 1.66 to 10.05%).

CONCLUSIONS

Overall, the test-retest variability of RTx-1 and its software was good in normal human eyes. Further studies in the normal clinical setting are mandatory.