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.
Adaptive optics (AO) enables retinal imaging at cellular resolution. Today, most ophthalmic AO systems have closed-loop bandwidths of ≤2 Hz, insufficient for many conditions encountered in the clinic. Here, we develop an ultrafast AO with a bandwidth of 32.6 Hz and evaluate its use with optical coherence tomography. After AO activation, the RMS wavefront aberration from an un-cyclopleged human eye dropped below diffraction limit within 5 ms, 40× faster than the fastest ophthalmic AO system reported in the literature. Because the system converges so quickly, we can use the data immediately after a blink or when imaging locations are changed, even in eyes wearing contact lenses.
Retinitis Pigmentosa (RP), the most common group of inherited retinal degenerative diseases, is characterized by progressive loss of peripheral vision that surrounds an island of healthy central vision and a transition zone of reduced vision. The most debilitating phase of the disease is cone photoreceptor death whose biological mechanisms remain unknown. Traditional clinical methods such as perimetry and electroretinography are gold standards for diagnosing and monitoring RP and indirectly assessing cone function. Both methods, however, lack the spatial resolution and sensitivity to assess disease progression at the level of individual photoreceptor cells, where it begins. To address this need, we developed an imaging method based on phase-sensitive adaptive optics optical coherence tomography (PS-AO-OCT) that characterizes cone dysfunction in RP subjects by stimulating cone cells with flashes of light and measuring their resulting nanometer-scale changes in optical path length. We introduce new biomarkers to quantify cone dysfunction. We find cone function decreases with increasing RP severity and even in the healthy central area where cone structure appears normal, cones respond differently than cones in the healthy controls.
The ganglion cell (GC) is the primary cell type damaged by diseases of the optic nerve such as glaucoma. Assessment of individual glaucoma risk is limited by our inability to accurately measure GC degeneration and loss. Recently, adaptive optics optical coherence tomography (AO-OCT) has enabled visualization and quantification of individual GC layer (GCL) somas in normal, healthy subjects. Quantifying GC loss in glaucoma, however, requires longitudinal assessment of these cells, which is confounded by normal age-related loss of these same cells. The ability to distinguish between these two causes of cell death is therefore paramount for early detection of glaucoma. In this study, we assess the ability of our AO-OCT method to track individual GCL somas over a period of one year and of our post processing methods to reliably measure soma loss rates. In four normal subjects with no history of ocular disease, we measured a soma loss rate of 0.15±0.04 %/yr (average±SD). As expected, this rate is more consistent with loss due to normal aging (~0.5%/yr) than to glaucomatous progression (~4.6%/yr). Aside from these rare isolated losses, the GCL soma mosaic was highly stable over the one year interval examined. Our measurements of peak GCL soma density did not differ significantly from histology reported in the literature.
Human color vision is achieved by mixing neural signals from cone photoreceptors sensitive to long- (L), medium- (M), and short- (S) wavelength light. The spatial arrangement and proportion of these spectral types in the retina set fundamental limits on color perception, and abnormal or missing types lead to color vision deficiencies. In vivo mapping of the trichromatic cone mosaic provides the most direct and quantitative means to assess the role photoreceptors play in color vision, but current methods of in vivo imaging have important limitations that preclude their widespread use. In this study, we present a new method for classifying cones based on their unique phase response to flashes of quasi-monochromatic light. Our use of phase provides unprecedented efficiency (30 min of subject time/retinal location) and accuracy (<0.02% of uncertainty), thus making in vivo cone classification practical in a wide range of color vision applications. We used adaptive optics optical coherence tomography to resolve cone cells in 3D and customized post-processing algorithms to extract the phase signal of individual cones. We successfully characterized light-induced changes to the phase signature of cones under different illuminant spectra, established the relationship between this phase change and the three cone spectral types, and used this relationship to classify and map cones in two color normal subjects.
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