Human color vision depends fundamentally on three spectral types of cone photoreceptors, yet methods to objectively measure these types across the whole visible spectrum in the living human eye do not exist. Here we demonstrate a new method based on phase-sensitive adaptive optics optical coherence tomography that offers the sensitivity and resolution to obtain spectral sensitivities with extremely high precision from 450-635 nm. We present the first objective measurements of human cone spectral sensitivity from 450 – 635 nm for all three cone types and demonstrate a path for quantifying spectral sensitivity over the whole visible spectrum.
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.
Adaptive optics optical coherence tomography technology enables non-invasive high-resolution retinal imaging and promises earlier detection of ocular disease. However, the images are corrupted by eye-movement artifacts that must be corrected to permit proper image analysis. We developed a method for efficiently removing eye-movement artifacts of A-lines using a multiple-reference global coordinate system. It corrects 3D translational eye movements, torsional eye movements, and image scaling, minimizing image distortion and substantially improving both regularity of the cone photoreceptor mosaic and clarity of individual cones.
Significance: Adaptive optics optical coherence tomography (AO-OCT) technology enables non-invasive, high-resolution three-dimensional (3D) imaging of the retina and promises earlier detection of ocular disease. However, AO-OCT data are corrupted by eye-movement artifacts that must be removed in post-processing, a process rendered time-consuming by the immense quantity of data.
Aim: To efficiently remove eye-movement artifacts at the level of individual A-lines, including those present in any individual reference volume.
Approach: We developed a registration method that cascades (1) a 3D B-scan registration algorithm with (2) a global A-line registration algorithm for correcting torsional eye movements and image scaling and generating global motion-free coordinates. The first algorithm corrects 3D translational eye movements to a single reference volume, accelerated using parallel computing. The second algorithm combines outputs of multiple runs of the first algorithm using different reference volumes followed by an affine transformation, permitting registration of all images to a global coordinate system at the level of individual A-lines.
Results: The 3D B-scan algorithm estimates and corrects 3D translational motions with high registration accuracy and robustness, even for volumes containing microsaccades. Averaging registered volumes improves our image quality metrics up to 22 dB. Implementation in CUDA™ on a graphics processing unit registers a 512 × 512 × 512 volume in only 10.6 s, 150 times faster than MATLAB™ on a central processing unit. The global A-line algorithm minimizes image distortion, improves regularity of the cone photoreceptor mosaic, and supports enhanced visualization of low-contrast retinal cellular features. Averaging registered volumes improves our image quality up to 9.4 dB. It also permits extending the imaging field of view (∼2.1 × ) and depth of focus (∼5.6 × ) beyond what is attainable with single-reference registration.
Conclusions: We can efficiently correct eye motion in all 3D at the level of individual A-lines using a global coordinate system.
Significance: There are no label-free imaging descriptors related to physiological activity of inner retinal cells in the living human eye. A major reason is that inner retinal neurons are highly transparent and reflect little light, making them extremely difficult to visualize and quantify.
Aim: To measure physiologically-induced optical changes of inner retinal cells despite their challenging optical properties.
Approach: We developed an imaging method based on adaptive optics and optical coherence tomography (AO-OCT) and a suite of postprocessing algorithms, most notably a new temporal correlation method.
Results: We captured the temporal dynamics of entire inner retinal layers, of specific tissue types, and of individual cells across three different timescales from fast (seconds) to extremely slow (one year). Time correlation analysis revealed significant differences in time constant (up to 0.4 s) between the principal layers of the inner retina with the ganglion cell layer (GCL) being the most dynamic. At the cellular level, significant differences were found between individual GCL somas. The mean time constant of the GCL somas (0.69 ± 0.17 s) was ∼ 30 % smaller than that of nerve fiber bundles and inner plexiform layer synapses and processes. Across longer durations, temporal speckle contrast and time-lapse imaging revealed motion of macrophage-like cells (over minutes) and GCL neuron loss and remodeling (over one year).
Conclusions: Physiological activity of inner retinal cells is now measurable in the living human eye.
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.
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.
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.
The inner retina is critical for visual processing, but much remains unknown about its neural circuitry and vulnerability to disease. A major bottleneck has been our inability to observe the structure and function of the cells composing these retinal layers in the living human eye. Here, we present a noninvasive method to observe both structural and functional information. Adaptive optics optical coherence tomography (AO-OCT) is used to resolve the inner retinal cells in all three dimensions and novel post processing algorithms are applied to extract structure and physiology down to the cellular level. AO-OCT captured the 3D mosaic of individual ganglion cell somas, retinal nerve fiber bundles of micron caliber, and microglial cells, all in exquisite detail. Time correlation analysis of the AO-OCT videos revealed notable temporal differences between the principal layers of the inner retina. The GC layer was more dynamic than the nerve fiber and inner plexiform layers. At the cellular level, we applied a customized correlation method to individual GCL somas, and found a mean time constant of activity of 0.57 s and spread of ±0.1 s suggesting a range of physiological dynamics even in the same cell type. Extending our method to slower dynamics (from minutes to one year), time-lapse imaging and temporal speckle contrast revealed appendage and soma motion of resting microglial cells at the retinal surface.