Human vision is considered limited to the visible range (∼400–700 nm), yet studies have shown that near-infrared light can elicit visual perception through a nonlinear process known as two-photon vision. This occurs when two infrared photons are absorbed simultaneously by photopigments in the photoreceptors, generating a response equivalent to that of a single visible photon. While this phenomenon has been investigated for monochromatic stimuli, its potential for color perception remains unexplored. Here, we present the first functional prototype of a two-photon infrared RGB display and demonstrate that polychromatic color perception can be achieved by using infrared light alone. We have demonstrated that color mixing in this spectral range follows additive principles similar to those of visible light, enabling the perception of a wide gamut of hues, including white. These findings open new avenues for leveraging this alternative visual mechanism in practical applications requiring precise color control, such as immersive display technologies.

Full-field optical coherence tomography (FFOCT) has recently regained attention thanks to the development of high-resolution dynamic OCT and cross-talk-free swept source FFOCT. However, the choice of wavelength and axial resolution is often a limiting factor with few existing commercial solutions. Here, we developed a novel method to provide rapid spectral shaping for FFOCT imaging. Combining a supercontinuum laser, a fast controllable acousto-optic tunable filter (AOTF), and a multimode fiber with passive and active mode mixing, we obtained an extremely flexible light source compatible with FFOCT. By tuning the AOTF frequency and integrating the resulting wavelength over one camera exposure time, it becomes possible to build any spectrum of interest in the 575-1000 nm range in time domain FFOCT. Alternatively, the designed source module enables achieving swept source FFOCT at up to 100 kfps at an unprecedented axial resolution of 1.1 μm.

Pulsed near-infrared (NIR) lasers can be perceived as light of approximately half their wavelength due to the process of two-photon (2P) absorption. For high intensities of light, single-photon (1P) absorption can still be perceived beyond 700 nm, so that the overall color perception in this region is the result of a mix of 1P and 2P absorption. In this study, color matching experiments were performed with seven laser wavelengths between 730 and 920 nm to investigate the interaction between 1P and 2P absorption and the range of colors that can be created by changing the laser power and repetition frequency of a ns-pulsed laser. We recorded color matches in a range from pure red for shorter wavelengths across shades of purple up to pure blue colors for the longest wavelengths, showing that nonspectral (purple) colors can be created using only one stimulating wavelength in the NIR. Changes in hue could be observed between wavelengths of 850 nm and 920 nm when laser power or repetition frequency were modified, with the highest color shifts occurring between 880 nm and 900 nm.

We present a novel portable in vivo confocal ophthalmoscope (PICO) designed to enable non-contact imaging of the cornea and anterior segment of the eye. PICO uses a broadband light source and diffraction gratings to acquire two-dimensional confocal images without any beam scanning mechanisms. A dry objective lens with a long working distance was used to image the external eye without making physical contact with the cornea or ocular surface. Lateral resolution of 2.2-2.7 μm and axial resolution of 8.1 μm were achieved with PICO when imaging ex vivo animal corneas, and provided reliable signal levels when images were acquired at high speeds of 100-135 frames/sec. The resulting images enable clear visualization of characteristic cellular morphological details of the cornea, including epithelial cells, corneal nerve fibers, stromal keratocytes, and endothelial cells. These preliminary results from high-speed PICO imaging of animal corneas merit further evaluation of the prototype device for in vivo human corneal imaging.

Drusen are a hallmark biomarker of age-related macular degeneration (AMD), with their size, number, and morphology (type) closely linked to disease severity and progression. Accurate segmentation and classification of drusen from optical coherence tomography (OCT) images are essential for objective AMD assessment and monitoring. In this work, we present a deep learning framework that combines a convolutional neural network for automated drusen segmentation with a dedicated classification module to distinguish four clinically relevant, distinct drusen types based on segmentation output. We evaluated our approach on a comprehensive dataset and achieved a mean Dice score of 0.74 ± 0.21 for voxel-wise segmentation accuracy and a critical success index of 0.69 ± 0.24 for drusen count accuracy. This method demonstrates substantial improvements in the quantitative drusen analysis and offers a promising tool for enhanced AMD diagnosis and tracking of disease progression.

Optoretinography (ORG) is a label-free imaging of the light-evoked changes in the retina associated with alterations in the cell shape and refractive indices. Its most precise version, the phase-resolved optical coherence tomography (pOCT), exhibited sensitivity of about 10 nm in vivo, limited by the signal-to-noise ratio and accuracy of the tissue registration. While it is yet insufficient for the detection of single action potentials, which are about 1 nm in amplitude, it enables monitoring slower and larger deformations in other retinal layers. In response to a single flash delivered to the dark-adapted retina, photoreceptor outer segments (OS) exhibit rapid (millisecond-scale) contraction, reaching tens of nm in cones and hundreds of nm in rods. This effect can be explained by changes in the membrane tension due to hyperpolarization of the OS discs–that is, the intradiscal space becoming more negatively charged–during the early receptor potential induced by opsins isomerization. In cones, such contraction is followed by a slower elongation by hundreds of nm during hundreds of ms. The proposed underlying mechanisms include osmotic influx of water, swelling of the cone opsin and disc membranes, and conformational changes in phosphodiesterase (PDE6) during phototransduction. ORG also reveals slow deformations in the subretinal space (SRS) and retinal pigment epithelium (RPE), likely induced by light-evoked ionic and osmotic shifts, as well as in the inner plexiform layer (IPL) and ganglion cell layer (GCL). ORG has a high potential as a non-invasive, label-free, and objective assay of retinal health, co-registered with structural images in the same OCT machine. To realize its promise in basic science and clinical assessment of diseases and therapies, its underlying mechanisms need to be delineated. This review summarizes current understanding of the physiological mechanisms behind the ORG.

Slide-free microscopy methods for histological imaging in thick, minimally processed tissue specimens are rapidly emerging for surgical margin analysis applications. Here, we report an optical imaging system that combines 266 nm excited fluorescence, 266 nm confocal reflectance, and 1050 nm spectral-domain optical coherence tomography. The deep UV confocal reflectance and DAPI stain emission contrasts provide histology-like imaging at the tissue surface, with virtual H&E staining enabled by deep learning. Cross-sectional OCT extends visualization of tissue morphology beneath the specimen surface with near-infrared depth penetration. A custom reflective objective facilitates simultaneous high resolution, aberration-free imaging of UV confocal reflectance and fluorescence emission with a 0.52 numerical aperture, while transmitting a secondary low numerical aperture OCT beam. Imaging of ex vivo specimens is demonstrated in murine kidney and liver, and human breast tissues.

Virtual staining, or in-silico labeling, employs deep learning–based image-to-image translation networks to computationally generate synthetic stained images, either from label-free inputs or by digitally transforming one stain type into another. In most reported studies, virtually stained images have been assessed only using traditional image quality measures such as structural similarity or signal-to-noise ratio. However, in biomedical imaging, images are typically acquired to facilitate an image-based inference, which we refer to as a downstream biological or clinical task. This study systematically investigates the utility of virtual staining for facilitating clinically relevant downstream tasks (like segmentation or classification) with consideration of the capacity of the deep neural networks employed to perform the tasks. Comprehensive empirical evaluations were conducted using biological datasets, assessing task performance by use of label-free, virtually stained, and ground truth fluorescence images. The results demonstrated that the utility of virtual staining is largely dependent on the ability of the segmentation or classification task network to extract meaningful task-relevant information, which is related to the concept of network capacity. Examples are provided in which virtual staining does not improve, or even degrades, segmentation or classification performance when the capacity of the associated task network is sufficiently large. The results demonstrate that task network capacity should be considered when deciding whether to perform virtual staining.

Dynamic optical coherence tomography (dOCT) uses signal fluctuations to contrast different cells and tissues. In this paper, we demonstrate that shortening the time base over which the signal fluctuations are evaluated reduces noise induced by motion while still maintaining a decent image quality. Automatic clustering using the neural-gas algorithm is introduced to optimize the border between the color channels. The performance of the automatic border optimization is demonstrated with 15 different tissue samples by quantitative assessment of motion-induced noise and image quality using the mean squared error (MSE) between images and the image quality parameters peak signal to noise ratio (PSNR) and structural similarity (SSIM).

This erratum corrects the 2.1 section for this paper [\\Biomed. Opt. Express16, 3270 (2025)10.1364/BOE.56420940809962]