We present cone spectral sensitivity and photopigment density measures in the living human eye using adaptive optics optical coherence tomography (AO-OCT) and initial results in rods. AO-OCT optoretinograms after visible light stimulation of variable intensities were acquired. Cones were classified and the mean post-stimulus response to incident retinal energy and wavelength was fit to a power law and related to spectral sensitivity and photopigment density. Individual cone sensitivities showed excellent agreement with ex vivo macaque suction electrophysiology measurements (Baylor 1987). Photopigment density variation and increasing photopigment towards the fovea were consistent with the literature. Rod mean µΔOPL responses trended in the direction of expected rod sensitivity.
Adaptive optics (AO) ophthalmoscopes enable retinal imaging at cellular resolution. The small field of view (FOV) and high magnification of these instruments make inclusion of a fixation channel critical for controlling the patch of retina that is stimulated with light and imaged. Here, we develop a more powerful fixation channel that is integrated with an improved stimulus channel in the Indiana AO optical coherence tomography (AO-OCT) system. It uses all stock components except one 3D printed optical mount and some machined adaptor plates. We balanced the trade-offs between subject working distance, steering field of view, dioptric correction range, and stimulus light efficiency and achieved better performance in all areas compared to our previous channel. We report on the overarching objectives of the integrated fixation and stimulus channel, its design and its validation as illustrated by several AO-OCT imaging examples. While intended for our AO-OCT system, the design, components, and performance trade-offs are general enough to be applicable to many other AO ophthalmoscopes in the field.
Cone photoreceptors are central to vision and die in many retinal degenerative diseases. High-resolution retinal imaging methods–notably adaptive optics optical coherence tomography (AO-OCT)–use these cells’ reflectance profiles to characterize their morphologic and functional properties in the living human eye to assess their health. While some cone cells reveal reflections that correspond to identifiable features such as the inner segment/outer segment junction (IS/OS) and cone outer segment tip (COST), other cells can generate additional unexplained reflections that complicate our ability to characterize their reflectance profile. Here, we present a new quantitative method to properly identify cone reflections in AO-OCT images that correspond to their features. We use this method to estimate the prevalence of any additional cone reflections in healthy eyes and eyes with retinitis pigmentosa (RP) and to identify the true COST reflection. Using our method as a ground truth, we find that the conventional method (which identifies COST as the brightest reflection between IS/OS and retinal pigment epithelium) misidentified COST in 6.1±1.5% of cones in healthy controls. In the transition zones of RP, this rate can increase to 18.8%. In these cones, our method’s estimate of cone outer segment length and optoretinogram response differed by 36.8 ± 12.8% and 20.7 ± 17.6%, respectively, in healthy controls, and by 79.5 ± 21.2% and 34.9 ± 24.8% in the transition zone of RP.
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Adaptive optics (AO) measures and corrects ocular wavefront aberrations, enabling cellular-resolution retinal imaging and stimulation, and enhanced visual performance. AO is a dynamic control system that must track and correct temporal changes in ocular aberrations in real time. This necessitates a wavefront sensor whose integration time and readout time are sufficiently short to minimize the latency of the feedback system and hence maximize AO performance. Most current ophthalmic AO systems use long wavefront sensor integration times on the order of 10−60 ms, resulting in long latencies, low AO loop rates (typically no greater than 10 Hz with a discontinuous-exposure scheme), and small AO closed-loop bandwidths (less than 1.5 Hz). Here, by using an integration time (0.126 ms) that is 100−500× shorter and a readout speed of the wavefront sensor that is 3−100× higher, we reduce the AO latency and increase the AO bandwidth by ~30× to 37.5 Hz. Although our wavefront sensor detects 100−500× fewer photons, our noise analysis shows that this limited number of photons is still sufficient for diffraction-limited wavefront measurements and hence our wavefront sensing is photon-efficient. We demonstrate that the resulting ultrafast AO running at 233 Hz significantly improves aberration correction and retinal image quality over conventional AO in a clinically-relevant scenario.
The high resolution of adaptive optics optical coherence tomography (AO-OCT) allows 3-dimensional imaging of individual cone photoreceptors in vivo. Histology has revealed that short-wavelength-sensitive (S) cones have distinct structural features compared with medium-wavelength-sensitive (M) and long-wavelength-sensitive (L) cones. Quantifying these structural features in images of living human retinas may provide a simpler and quicker method for identifying S cones than by imaging cone function (e.g., optoretinography). Here, we present a quantitative method for using AO-OCT measurements of cone structure in a support vector machine (SVM) classifier to identify individual S cones. For every cone cell, we measured six key structural parameters: inner segment length (ISL), outer segment length (OSL), inner segment / outer segment conjunction (IS/OS) diameter, cone outer-segment tip (COST) diameter, IS/OS reflectance, and COST reflectance. ISL and OSL were determined from depth differences between reflections of the external limiting membrane (ELM) and IS/OS, and IS/OS and COST, respectively. Each reflection’s depth was measured with sub-pixel accuracy using Gaussian interpolation; its diameter was measured using the gradient information from the en face projection at that depth. Among 6,398 analyzed cones in six subjects, we found S cones had significantly longer ISLs, shorter OSLs, and wider IS/OS diameters than did cones of other types. We used these structural differences in our SVM model to classify cone spectral types and compared results with cone optoretinography. In five of the six subjects, S cones were identified with F1 scores ranging from 0.78 to 0.93.
Numerous retinal pathologies affect cone photoreceptor photopigment density, making it a potentially attractive functional biomarker for detecting and tracking disease progression. Conventional methods to measure photopigment density include psychophysical color matching, microspectrophotometry, and retinal densitometry, but these are either subjective, measure the aggregate response/change of thousands of cones, or are performed ex vivo. Recently, we have developed a method to measure spectral sensitivities of individual human cone photoreceptors objectively, non-invasively, and in vivo with adaptive optics optical coherence tomography (AO-OCT). In preliminary results we have observed variability in the spectral sensitivities of individual cones of the same type (S, M or L) that we hypothesize attributes to inter-cone variations in photopigment density. If correct, this may be of significant clinical interest. Here, we test this hypothesis by (1) deriving an expression for the relative photopigment densities of individual cone photoreceptors based on a theoretical model of the cone absorption process and (2) using this expression to estimate photopigment density from our AO-OCT measurements of spectral sensitivity. Our mean spectral sensitivity measurements align well to Stockman & Sharpe’s well-recognized cone fundamentals with a total least-squared error of 0.12 and confidence intervals (CI) <0.36, <0.025 and <0.017 for S, M, and L cones, respectively. The substantive variability in individual cone spectral sensitivities once related to photopigment density exhibits a distribution of standard deviation=0.177 for a group of 703 cones. This indicates a two-fold difference in light sensitivity between the least sensitive cone (least amount of photopigment) and the most sensitive cone (largest amount of photopigment) for 95% of the cones measured. Furthermore, we found relative photopigment density decreased with increasing retinal eccentricity from nasal to temporal retina at 3.8° eccentricity with a slope of -0.24/° (p < .001). Both density distribution and eccentricity dependence are consistent with the literature.
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