The rod photoreceptors are implicated in a number of devastating retinal diseases. However, routine imaging of these cells has remained elusive, even with the advent of adaptive optics imaging. Here, we present the first in vivo images of the contiguous rod photoreceptor mosaic in nine healthy human subjects. The images were collected with three different confocal adaptive optics scanning ophthalmoscopes at two different institutions, using 680 and 775 nm superluminescent diodes for illumination. Estimates of photoreceptor density and rod:cone ratios in the 5°–15° retinal eccentricity range are consistent with histological findings, confirming our ability to resolve the rod mosaic by averaging multiple registered images, without the need for additional image processing. In one subject, we were able to identify the emergence of the first rods at approximately 190 μm from the foveal center, in agreement with previous histological studies. The rod and cone photoreceptor mosaics appear in focus at different retinal depths, with the rod mosaic best focus (i.e., brightest and sharpest) being at least 10 μm shallower than the cones at retinal eccentricities larger than 8°. This study represents an important step in bringing high-resolution imaging to bear on the study of rod disorders.

Purpose To assess the repeatability and measurement error associated with cone density and nearest neighbor distance (NND) estimates in images of the parafoveal cone mosaic obtained with an adaptive optics scanning light ophthalmoscope (AOSLO). Methods Twenty-one participants with no known ocular pathology were recruited. Four retinal locations, approximately 0.65° eccentricity from the center of fixation were imaged 10 times in randomized order with an AOSLO. Cone coordinates in each image were identified using an automated algorithm (with or without manual correction), from which cone density and NND were calculated. Owing to naturally occurring fixational instability, the 10 images recorded from a given location did not overlap entirely. We thus analyzed each image set both before and after alignment. Results Automated estimates of cone density on the unaligned image sets showed a coefficient of repeatability of 11,769 cones/mm2 (17.1%). The primary reason for this variability appears to be fixational instability, as aligning the 10 images to include the exact same retinal area, results in an improved repeatability of 4,358 cones/mm2 (6.4%) using completely automated cone identification software. Repeatability improved further by manually identifying cones missed by the automated algorithm, with a coefficient of repeatability of 1,967 cones/mm2 (2.7%). NND showed improved repeatability, and was generally insensitive to the undersampling by the automated algorithm. Conclusions As our data were collected in a young, healthy population, this likely represents a best-case estimate for corresponding measurements in patients with retinal disease. Similar studies need to be carried out on other imaging systems (including those using different imaging modalities, wavefront correction technology, and/or cone identification software), as repeatability would be expected to be highly sensitive to initial image quality and the performance of cone identification algorithms. Separate studies addressing inter-session repeatability and inter-observer reliability are also needed.

Foveal cone specialization and pit morphology vary greatly in albinism. Normal cone packing was observed in the absence of a foveal pit, suggesting a pit is not required for packing to occur. The degree to which retinal anatomy correlates with genotype or visual function remains unclear, and future examination of larger patient groups will provide important insight on this issue.

PurposeTo evaluate how metrics used to describe the cone mosaic change in response to simulated photoreceptor undersampling (i.e., cell loss or misidentification).MethodsUsing an adaptive optics ophthalmoscope, we acquired images of the cone mosaic from the center of fixation to 10° along the temporal, superior, inferior, and nasal meridians in 20 healthy subjects. Regions of interest (n = 1780) were extracted at regular intervals along each meridian. Cone mosaic geometry was assessed using a variety of metrics − density, density recovery profile distance (DRPD), nearest neighbor distance (NND), intercell distance (ICD), farthest neighbor distance (FND), percentage of six-sided Voronoi cells, nearest neighbor regularity (NNR), number of neighbors regularity (NoNR), and Voronoi cell area regularity (VCAR). The “performance” of each metric was evaluated by determining the level of simulated loss necessary to obtain 80% statistical power.ResultsOf the metrics assessed, NND and DRPD were the least sensitive to undersampling, classifying mosaics that lost 50% of their coordinates as indistinguishable from normal. The NoNR was the most sensitive, detecting a significant deviation from normal with only a 10% cell loss.ConclusionsThe robustness of cone spacing metrics makes them unsuitable for reliably detecting small deviations from normal or for tracking small changes in the mosaic over time. In contrast, regularity metrics are more sensitive to diffuse loss and, therefore, better suited for detecting such changes, provided the fraction of misidentified cells is minimal. Combining metrics with a variety of sensitivities may provide a more complete picture of the integrity of the photoreceptor mosaic.

Retinal MAs can be classified in vivo into six different morphologic types, according to the geometry of their two-dimensional (2D) en face view. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging of MAs offers the possibility of studying microvascular change on a histologic scale, which may help our understanding of disease progression and treatment response.

Purpose The outer nuclear layer (ONL) contains photoreceptor nuclei, and its thickness is an important biomarker for retinal degenerations. Accurate ONL thickness measurements are obscured in standard optical coherence tomography (OCT) images because of Henle fiber layer (HFL). Improved differentiation of the ONL and HFL boundary is made possible by using Directional OCT (D-OCT), a method that purposefully varies the pupil entrance position of the OCT beam. Methods Fifty-seven normal eyes were imaged using multiple pupil entry positions with a commercial SDOCT system. Cross-sectional image sets were registered to each other, and segmented at the top of HFL, the border of HFL and the ONL and at the external limiting membrane. Thicknesses of the ONL and HFL were measured and analyzed. Results The true ONL and HFL thicknesses varied substantially by eccentricity and between individuals. The true macular ONL thickness comprised an average of 54.6% of measurements that also included HFL. The ONL and HFL thicknesses at specific retinal eccentricities were poorly correlated. Conclusion Accurate ONL and HFL thickness measurements are made possible by the optical contrast of D-OCT. Distinguishing these individual layers can improve clinical trial endpoints and assessment of disease progression.

Although these results are consistent with predictions from existing models of foveal development, more work is needed to confirm the developmental link between the size of the FAZ and the degree of foveal pit excavation. In addition, more work is needed to understand the relationship between these and other anatomic features of the human foveal region, including peak cone density, rod-free zone diameter, and Henle fiber layer.

The photoreceptor phenotype associated with OPN1LW and OPN1MW mutations is highly variable. These findings have implications for the potential restoration of visual function in subjects with opsin mutations. Our study highlights the importance of high-resolution phenotyping to characterize cellular structure in inherited retinal disease; such information will be critical for selecting patients most likely to respond to therapeutic intervention and for establishing a baseline for evaluating treatment efficacy.