Sub-Airy Confocal Adaptive Optics Scanning Ophthalmoscopy

Peak Cone Density Predicted from Outer Segment Length Measured on Optical Coherence Tomography

PURPOSE

To compare peak cone density predicted from outer segment length measured on optical coherence tomography with direct measures of peak cone density from adaptive optics scanning light ophthalmoscopy.

METHODS

Data from 42 healthy participants with direct peak cone density measures and optical coherence tomography line scans available were used in this study. Longitudinal reflectivity profiles were analyzed using two methods of identifying the boundaries of the ellipsoid and interdigitation zones to estimate maximum outer segment length: peak-to-peak and the slope method. These maximum outer segment length values were then used to predict peak cone density using a previously described geometrical model. A comparison between predicted and direct peak cone density measures was then performed.

RESULTS

The mean bias between observers for estimating maximum outer segment length across methods was less than 2 µm. Cone density predicted from the peak-to-peak method against direct cone density measures showed a mean bias of 6,812 cones/mm2 with 50% of participants displaying a 10% difference or less between predicted and direct cone density values. Cone density derived from the slope method showed a mean bias of -17,929 cones/mm2 relative to direct cone density measures, with only 41% of participants demonstrating less than a 10% difference between direct and predicted cone density values.

CONCLUSION

Predicted foveal cone density derived from peak-to-peak outer segment length measurements using commercial optical coherence tomography show modest agreement with direct measures of peak cone density from adaptive optics scanning light ophthalmoscopy. The methods used here are imperfect predictors of cone density, however, further exploration of this relationship could reveal a clinically relevant marker of cone structure.

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.

Assessing Interocular Symmetry of the Foveal Cone Mosaic

Purpose

To test the hypothesis that foveal cone topography is symmetrical between contralateral eyes.

Methods

We used adaptive optics scanning light ophthalmoscopy to acquire images of the foveal cone mosaic in each eye of 58 subjects with normal vision (35 female, 23 male). Cones were semiautomatically identified over a 300 × 300-µm foveal area. From these cone coordinates, maps of cone density were derived, and we extracted estimates of peak cone density from each map. Mosaic regularity was assessed using Voronoi cell area regularity (VCAR). Average roundness and average area of the 70%, 75%, 80%, 85%, and 90% of peak density isodensity contours were evaluated.

Results

The average peak cone density for right eyes was 180,286 cones/mm² (n = 49) and for left eyes was 182,397 cones/mm² (n = 45), with a mean absolute difference of 6363 cones/mm² (n = 43). Peak density, cone spacing, VCAR, and average area within the isodensity contours of fellow eyes were not significantly different (P = 0.60, P = 0.83, P = 0.30, and P = 0.39, respectively). However, the average roundness of the isodensity contours was 2% more circular in the right eyes than in the left eyes (P = 0.02).

Conclusions

There is interocular symmetry of peak foveal cone density, mosaic regularity, and area encompassing the most densely packed cells in subjects with normal vision. The origin and significance of the observed interocular difference in average roundness of the isodensity contours are unclear.

In-vivo sub-diffraction adaptive optics imaging of photoreceptors in the human eye with annular pupil illumination and sub-Airy detection

Adaptive optics scanning light ophthalmoscopy (AOSLO) allows non-invasive visualization of the living human eye at the microscopic scale; but even with correction of the ocular wavefront aberrations over a large pupil, the smallest cells in the photoreceptor mosaic cannot always be resolved. Here, we synergistically combine annular pupil illumination with sub-Airy disk confocal detection to demonstrate a 33% improvement in transverse resolution (from 2.36 to 1.58 μm) and a 13% axial resolution enhancement (from 37 to 32 μm), an important step towards the study of the complete photoreceptor mosaic in heath and disease. Interestingly, annular pupil illumination also enhanced the visualization of the photoreceptor mosaic in non-confocal detection schemes such as split detection AOSLO, providing a strategy for enhanced multimodal imaging of the cone and rod photoreceptor mosaic.

Template free eye motion correction for scanning systems

Scanning imaging systems are susceptible to image warping in the presence of target motion occurring within the time required to acquire an individual image frame. In this Letter, we introduce the use of a dual raster scanning approach to correct for motion distortion without the need for prior knowledge of the undistorted image. In the dual scanning approach, the target is imaged simultaneously with two imaging beams from the same imaging system. The two imaging beams share a common pupil but have a spatial shift between the beams on the imaging plane. The spatial shift can be used to measure high speed events, because it measures an identical region at two different times within the time required for acquisition of a single frame. In addition, it provides accurate spatial information, since two different regions on the target are imaged simultaneously, providing an undistorted estimate of the spatial relation between regions. These spatial and temporal relations accurately measure target motion. Data from adaptive optics scanning laser ophthalmoscope (AOSLO) imaging of the human retina are used to demonstrate this technique. We apply the technique to correct the shearing of retinal images produced by eye motion. Three control subjects were measured while imaging different retinal layers and retinal locations to qualify the effectiveness of the algorithm. Since the time shift between channels is readily adjustable, this method can be tuned to match different imaging situations. The major requirement is the need to separate the two images; in our case, we used different near infrared spectral regions and dichroic filters.

Super-resolution ophthalmoscopy: Virtually structured detection for resolution improvement in retinal imaging

Quantitative retinal imaging is essential for advanced study and clinical management of eye diseases. However, spatial resolution of retinal imaging has been limited due to available numerical aperture and optical aberration of the ocular optics. Structured illumination microscopy has been established to break the diffraction-limit resolution in conventional light microscopy. However, practical implementation of structured illumination microscopy for in vivo ophthalmoscopy of the retina is challenging due to inevitable eye movements that can produce phase artifacts. Recently, we have demonstrated the feasibility of using virtually structured detection as one alternative to structured illumination microscopy for super-resolution imaging. By providing the flexibility of digital compensation of eye movements, the virtually structured detection provides a feasible, phase-artifact-free strategy to achieve super-resolution ophthalmoscopy. In this article, we summarize the technical rationale of virtually structured detection, and its implementations for super-resolution imaging of freshly isolated retinas, intact animals, and awake human subjects.

Comparison of confocal and non-confocal split-detection cone photoreceptor imaging

Quadrant reflectance confocal and non-confocal scanning light ophthalmoscope images of the photoreceptor mosaic were recorded in a subject with congenital achromatopsia (ACHM) and a normal control. These images, captured with various circular and annular apertures, were used to calculate split-detection images, revealing two cone photoreceptor contrast mechanisms. The first contrast mechanism, maximal in the non-confocal 5.5-10 Airy disk diameter annular region, is unrelated to the cone reflectivity in confocal or flood illumination imaging. The second mechanism, maximal for confocal split-detection, is related to the cone reflectivity in confocal or flood illumination imaging that originates from the ellipsoid zone and/or inner-outer segment junction. Seeking to maximize image contrast, split-detection images were generated using various quadrant detector combinations, with opposite (diagonal) quadrant detectors producing the highest contrast. Split-detection generated with the addition of adjacent quadrant detector pairs, shows lower contrast, while azimuthal split-detection images, calculated from adjacent quadrant detectors, showed the lowest contrast. Finally, the integration of image pairs with orthogonal split directions was used to produce images in which the photoreceptor contrast does not change with direction.

Adaptive optics for high-resolution imaging

Adaptive optics (AO) is a technique that corrects for optical aberrations. It was originally proposed to correct for the blurring effect of atmospheric turbulence on images in ground-based telescopes and was instrumental in the work that resulted in the Nobel prize-winning discovery of a supermassive compact object at the centre of our galaxy. When AO is used to correct for the eye's imperfect optics, retinal changes at the cellular level can be detected, allowing us to study the operation of the visual system and to assess ocular health in the microscopic domain. By correcting for sample-induced blur in microscopy, AO has pushed the boundaries of imaging in thick tissue specimens, such as when observing neuronal processes in the brain. In this primer, we focus on the application of AO for high-resolution imaging in astronomy, vision science and microscopy. We begin with an overview of the general principles of AO and its main components, which include methods to measure the aberrations, devices for aberration correction, and how these components are linked in operation. We present results and applications from each field along with reproducibility considerations and limitations. Finally, we discuss future directions.

Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO)

Adaptive optics scanning light ophthalmoscopy (AOSLO) allows visualization of the living human retina with exquisite single-cell resolution. This technology has improved our understanding of normal retinal structure and revealed pathophysiological details of a number of retinal diseases. Despite the remarkable capabilities of AOSLO, it has not seen the widespread commercial adoption and mainstream clinical success of other modalities developed in a similar time frame. Nevertheless, continued advancements in AOSLO hardware and software have expanded use to a broader range of patients. Current devices enable imaging of a number of different retinal cell types, with recent improvements in stimulus and detection schemes enabling monitoring of retinal function, microscopic structural changes, and even subcellular activity. This has positioned AOSLO for use in clinical trials, primarily as exploratory outcome measures or biomarkers that can be used to monitor disease progression or therapeutic response. AOSLO metrics could facilitate patient selection for such trials, to refine inclusion criteria or to guide the choice of therapy, depending on the presence, absence, or functional viability of specific cell types. Here we explore the potential of AOSLO retinal imaging by reviewing clinical applications as well as some of the pitfalls and barriers to more widespread clinical adoption.

Wide-vergence, multi-spectral adaptive optics scanning laser ophthalmoscope with diffraction-limited illumination and collection

Visualizing and assessing the function of microscopic retinal structures in the human eye is a challenging task that has been greatly facilitated by ophthalmic adaptive optics (AO). Yet, as AO imaging systems advance in functionality by employing multiple spectral channels and larger vergence ranges, achieving optimal resolution and signal-to-noise ratios (SNR) becomes difficult and is often compromised. While current-generation AO retinal imaging systems have demonstrated excellent, near diffraction-limited imaging performance over wide vergence and spectral ranges, a full theoretical and experimental analysis of an AOSLO that includes both the light delivery and collection optics has not been done, and neither has the effects of extending wavefront correction from one wavelength to imaging performance in different spectral channels. Here, we report a methodology and system design for simultaneously achieving diffraction-limited performance in both the illumination and collection paths for a wide-vergence, multi-spectral AO scanning laser ophthalmoscope (SLO) over a 1.2 diopter vergence range while correcting the wavefront in a separate wavelength. To validate the design, an AOSLO was constructed to have three imaging channels spanning different wavelength ranges (543 ± 11 nm, 680 ± 11 nm, and 840 ± 6 nm, respectively) and one near-infrared wavefront sensing channel (940 ± 5 nm). The AOSLO optics and their alignment were determined via simulations in optical and optomechanical design software and then experimentally verified by measuring the AOSLO's illumination and collection point spread functions (PSF) for each channel using a phase retrieval technique. The collection efficiency was then measured for each channel as a function of confocal pinhole size when imaging a model eye achieving near-theoretical performance. Imaging results from healthy human adult volunteers demonstrate the system's ability to resolve the foveal cone mosaic in all three imaging channels despite a wide spectral separation between the wavefront sensing and imaging channels.

Cellular imaging of inherited retinal diseases using adaptive optics

Adaptive optics (AO) is an insightful tool that has been increasingly applied to existing imaging systems for viewing the retina at a cellular level. By correcting for individual optical aberrations, AO offers an improvement in transverse resolution from 10-15 μm to ~2 μm, enabling assessment of individual retinal cell types. One of the settings in which its utility has been recognised is that of the inherited retinal diseases (IRDs), the genetic and clinical heterogeneity of which warrants better cellular characterisation. In this review, we provide a summary of the basic principles of AO, its integration into multiple retinal imaging modalities and its clinical applications, focusing primarily on IRDs. Furthermore, we present a comprehensive summary of AO-based cellular findings in IRDs according to their associated disease-causing genes.

RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images

Quantification of the human rod and cone photoreceptor mosaic in adaptive optics scanning light ophthalmoscope (AOSLO) images is useful for the study of various retinal pathologies. Subjective and time-consuming manual grading has remained the gold standard for evaluating these images, with no well validated automatic methods for detecting individual rods having been developed. We present a novel deep learning based automatic method, called the rod and cone CNN (RAC-CNN), for detecting and classifying rods and cones in multimodal AOSLO images. We test our method on images from healthy subjects as well as subjects with achromatopsia over a range of retinal eccentricities. We show that our method is on par with human grading for detecting rods and cones.

Super-resolution retinal imaging using optically reassigned scanning laser ophthalmoscopy

Super-resolution optical microscopy techniques have enabled the discovery and visualization of numerous phenomena in physics, chemistry and biology1-3. However, the highest resolution super-resolution techniques depend on nonlinear fluorescence phenomena and are thus inaccessible to the myriad applications that require reflective imaging4,5. One promising super-resolution technique is optical reassignment6, which so far has only shown potential for fluorescence imaging at low speeds. Here, we present novel advances in optical reassignment to adapt it for any scanning microscopy, including reflective imaging, and enable an order of magnitude faster image acquisition than previous optical reassignment techniques. We utilized these advances to implement optically reassigned scanning laser ophthalmoscopy, an in vivo super-resolution human retinal imaging device not reliant on confocal gating. Using this instrument, we achieved high-resolution imaging of living human retinal cone photoreceptor cells (determined by minimum foveal eccentricity) without adaptive optics or chemical dilation of the eye7.

Adaptive optics imaging of the human retina

Adaptive Optics (AO) retinal imaging has provided revolutionary tools to scientists and clinicians for studying retinal structure and function in the living eye. From animal models to clinical patients, AO imaging is changing the way scientists are approaching the study of the retina. By providing cellular and subcellular details without the need for histology, it is now possible to perform large scale studies as well as to understand how an individual retina changes over time. Because AO retinal imaging is non-invasive and when performed with near-IR wavelengths both safe and easily tolerated by patients, it holds promise for being incorporated into clinical trials providing cell specific approaches to monitoring diseases and therapeutic interventions. AO is being used to enhance the ability of OCT, fluorescence imaging, and reflectance imaging. By incorporating imaging that is sensitive to differences in the scattering properties of retinal tissue, it is especially sensitive to disease, which can drastically impact retinal tissue properties. This review examines human AO retinal imaging with a concentration on the use of the Adaptive Optics Scanning Laser Ophthalmoscope (AOSLO). It first covers the background and the overall approaches to human AO retinal imaging, and the technology involved, and then concentrates on using AO retinal imaging to study the structure and function of the retina.