Handheld Adaptive Optics Scanning Laser Ophthalmoscope

Compact lens-based dual-channel adaptive optics scanning laser ophthalmoscopy for in-vivo three-dimensional retinal imaging in mice

Adaptive optics (AO) has been instrumental in ophthalmic imaging, by correcting wavefront aberrations in ocular optics and achieving diffraction-limited resolution. Current state-of-the-art AO retinal imaging systems use mirror-based optics to avoid surface reflection and chromatic aberrations, requiring a large system footprint with long focal length spherical mirrors. Here we report a compact refractive lens-based AO scanning laser ophthalmoscopy (SLO) system with simultaneous dual-channel fluorescence imaging capacity in mouse retina. The optical layout fits on a 2'x2' optical breadboard and the whole system is constructed on a mobile 3'x4' optical table. We show that the 3D image resolutions are significantly improved with AO correction, particularly in the z-axis (2x improvement compared to without AO, approaching diffraction-limited resolution). The optical design enables survey of a relatively large retinal area, up to 20º field of view, as well as high magnification AO imaging. Simultaneous imaging with 488nm and 561nm laser lines was evaluated using dual-channel AOSLO in CX3CR1-GFP transgenic mice expressing EGFP in microglia, undergoing rhodamine angiography. We performed dynamic high-resolution 3D imaging of microglial morphology every 5 mins for one hour and longitudinally over 3 weeks, demonstrating microglial activation and translocation over short and long time periods in an optic nerve crush model. This lens-based compact AOSLO offers a versatile and compact design for retinal fluorescence imaging in mice.

Identifying retinal pigment epithelium cells in adaptive optics-optical coherence tomography images with partial annotations and superhuman accuracy

Retinal pigment epithelium (RPE) cells are essential for normal retinal function. Morphological defects in these cells are associated with a number of retinal neurodegenerative diseases. Owing to the cellular resolution and depth-sectioning capabilities, individual RPE cells can be visualized in vivo with adaptive optics-optical coherence tomography (AO-OCT). Rapid, cost-efficient, and objective quantification of the RPE mosaic's structural properties necessitates the development of an automated cell segmentation algorithm. This paper presents a deep learning-based method with partial annotation training for detecting RPE cells in AO-OCT images with accuracy better than human performance. We have made the code, imaging datasets, and the manual expert labels available online.

In vivo imaging of human retinal ganglion cells using optical coherence tomography without adaptive optics

Retinal ganglion cells play an important role in human vision, and their degeneration results in glaucoma and other neurodegenerative diseases. Imaging these cells in the living human retina can greatly improve the diagnosis and treatment of glaucoma. However, owing to their translucent soma and tight packing arrangement within the ganglion cell layer (GCL), successful imaging has only been achieved with sophisticated research-grade adaptive optics (AO) systems. For the first time we demonstrate that GCL somas can be resolved and cell morphology can be quantified using non-AO optical coherence tomography (OCT) devices with optimal parameter configuration and post-processing.

Optimization of handheld spectrally encoded coherence tomography and reflectometry for point-of-care ophthalmic diagnostic imaging

Significance

Handheld optical coherence tomography (HH-OCT) systems enable point-of-care ophthalmic imaging in bedridden, uncooperative, and pediatric patients. Handheld spectrally encoded coherence tomography and reflectometry (HH-SECTR) combines OCT and spectrally encoded reflectometry (SER) to address critical clinical challenges in HH-OCT imaging with real-time en face retinal aiming for OCT volume alignment and volumetric correction of motion artifacts that occur during HH-OCT imaging.

Aim

We aim to enable robust clinical translation of HH-SECTR and improve clinical ergonomics during point-of-care OCT imaging for ophthalmic diagnostics.

Approach

HH-SECTR is redesigned with (1) optimized SER optical imaging for en face retinal aiming and retinal tracking for motion correction, (2) a modular aluminum form factor for sustained alignment and probe stability for longitudinal clinical studies, and (3) one-handed photographer-ergonomic motorized focus adjustment.

Results

We demonstrate an HH-SECTR imaging probe with micron-scale optical-optomechanical stability and use it for in vivo human retinal imaging and volumetric motion correction.

Conclusions

This research will benefit the clinical translation of HH-SECTR for point-of-care ophthalmic diagnostics.

Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes

Objective quantification of photoreceptor cell morphology, such as cell diameter and outer segment length, is crucial for early, accurate, and sensitive diagnosis and prognosis of retinal neurodegenerative diseases. Adaptive optics optical coherence tomography (AO-OCT) provides three-dimensional (3-D) visualization of photoreceptor cells in the living human eye. The current gold standard for extracting cell morphology from AO-OCT images involves the tedious process of 2-D manual marking. To automate this process and extend to 3-D analysis of the volumetric data, we propose a comprehensive deep learning framework to segment individual cone cells in AO-OCT scans. Our automated method achieved human-level performance in assessing cone photoreceptors of healthy and diseased participants captured with three different AO-OCT systems representing two different types of point scanning OCT: spectral domain and swept source.

Non-interferometric volumetric imaging in living human retina by confocal oblique scanning laser ophthalmoscopy

Three-dimensional (3D) imaging of the human retina is instrumental in vision science and ophthalmology. While interferometric retinal imaging is well established by optical coherence tomography (OCT), non-interferometric volumetric imaging in the human retina has been challenging up to date. Here, we report confocal oblique scanning laser ophthalmoscopy (CoSLO) to fill that void and harness non-interferometric optical contrast in 3D. CoSLO decouples the illumination and detection by utilizing oblique laser scanning and oblique imaging to achieve ∼4x better axial resolution than conventional SLO. By combining remote focusing, CoSLO permits the acquisition of depth signals in parallel and over a large field of view. Confocal gating is introduced by a linear sensor array to improve the contrast and resolution. For the first time, we reported non-interferometric 3D human retinal imaging with >20° viewing angle, and revealed detailed features in the inner, outer retina, and choroid. CoSLO shows potential to be another useful technique by offering 3D non-interferometric contrasts.

Application of Adaptive Optics in Ophthalmology

The eye, the photoreceptive organ used to perceive the external environment, is of great importance to humans. It has been proven that some diseases in humans are accompanied by fundus changes; therefore, the health status of people may be interpreted from retinal images. However, the human eye is not a perfect refractive system for the existence of ocular aberrations. These aberrations not only affect the ability of human visual discrimination and recognition, but restrict the observation of the fine structures of human eye and reduce the possibility of exploring the mechanisms of eye disease. Adaptive optics (AO) is a technique that corrects optical wavefront aberrations. Once integrated into ophthalmoscopes, AO enables retinal imaging at the cellular level. This paper illustrates the principle of AO in correcting wavefront aberrations in human eyes, and then reviews the applications and advances of AO in ophthalmology, including the adaptive optics fundus camera (AO-FC), the adaptive optics scanning laser ophthalmoscope (AO-SLO), the adaptive optics optical coherence tomography (AO-OCT), and their combined multimodal imaging technologies. The future development trend of AO in ophthalmology is also prospected.

COVID-19 in Ophthalmology. Current Disease Status and Challenges during Clinical Practice

Purpose: The novel coronavirus disease 2019 (COVID-19) has raised a global public health concern. The purpose of this review is to summarize the evidence currently available on COVID-19 for its ocular implications and manifestations from both pathogenetic and clinical standpoints. Methods: For this narrative review, more than 100 relevant scientific articles were considered from various databases (PubMed, Google Scholar, and Science Direct) using keywords such as coronavirus outbreak, COVID-19, ophthalmology, ocular symptoms. Results:Daily healthcare both from patient and physician perspective, as well as on some guidelines regarding prevention and management have dramatically changed over the last few months. Although COVID-19 infection mainly affects the respiratory system as well as the gastrointestinal, cardiovascular, and urinary systems, it may cause a wide spectrum of ocular manifestations. Various challenges have to be faced to minimize exposure for both patients and physicians. Conclusion:The risk of COVID-19 infection should be considered and medical care should be prioritized for urgent cases. Appropriate management for patients with chronic cases that may result in adverse outcomes should not be neglected, while patients that can be monitored remotely should be identified.

Wavefront sensor-less adaptive optics using deep reinforcement learning

Image degradation due to wavefront aberrations can be corrected with adaptive optics (AO). In a typical AO configuration, the aberrations are measured directly using a Shack-Hartmann wavefront sensor and corrected with a deformable mirror in order to attain diffraction limited performance for the main imaging system. Wavefront sensor-less adaptive optics (SAO) uses the image information directly to determine the aberrations and provide guidance for shaping the deformable mirror, often iteratively. In this report, we present a Deep Reinforcement Learning (DRL) approach for SAO correction using a custom-built fluorescence confocal scanning laser microscope. The experimental results demonstrate the improved performance of the DRL approach relative to a Zernike Mode Hill Climbing algorithm for SAO.

High-speed and widefield handheld swept-source OCT angiography with a VCSEL light source

Optical coherence tomography (OCT) and OCT angiography (OCTA) enable noninvasive structural and angiographic imaging of the eye. Portable handheld OCT/OCTA systems are required for imaging patients in the supine position. Examples include infants in the neonatal intensive care unit (NICU) and operating room (OR). The speed of image acquisition plays a pivotal role in acquiring high-quality OCT/OCTA images, particularly with the handheld system, since both the operator hand tremor and subject motion can cause significant motion artifacts. In addition, having a large field of view and the ability of real-time data visualization are critical elements in rapid disease screening, reducing imaging time, and detecting peripheral retinal pathologies. The arrangement of optical components is less flexible in the handheld system due to the limitation of size and weight. In this paper, we introduce a 400-kHz, 55-degree field of view handheld OCT/OCTA system that has overcome many technical challenges as a portable OCT system as well as a high-speed OCTA system. We demonstrate imaging premature infants with retinopathy of prematurity (ROP) in the NICU, a patient with incontinentia pigmenti (IP), and a patient with X-linked retinoschisis (XLRS) in the OR using our handheld OCT system. Our design may have the potential for improving the diagnosis of retinal diseases and help provide a practical guideline for designing a flexible and portable OCT system.

Advances in multimodal imaging in ophthalmology

Multimodality ophthalmic imaging systems aim to enhance the contrast, resolution, and functionality of existing technologies to improve disease diagnostics and therapeutic guidance. These systems include advanced acquisition and post-processing methods using optical coherence tomography (OCT), combined scanning laser ophthalmoscopy and OCT systems, adaptive optics, surgical guidance, and photoacoustic technologies. Here, we provide an overview of these ophthalmic imaging systems and their clinical and basic science applications.

COVID-19 Launches Retinal Telemedicine into the Next Frontier

INTRODUCTION

Telemedicine in ophthalmology, and specifically in retinal diseases, has made significant advancements in recent years. The COVID-19 pandemic has launched telehealth into a new era by creating demand from patients and physicians alike, while breaking down previous insurance, reimbursement, access and educational barriers.

METHODS

This paper reviews mulitple studies demonstrating the use of telemedicine in managing various retinal conditions before and during the COVID-19 pandemic.

CONCLUSION

Moving forward, promising new devices and models of care ensure that tele-retinal care will continue to expand and become a vital part of how we screen, diagnose and monitor retinal diseases.

Multimodal handheld adaptive optics scanning laser ophthalmoscope

Non-confocal adaptive optics scanning laser ophthalmoscopy (AOSLO) has enhanced the study of human retinal photoreceptors by providing complementary information to standard confocal AOSLO images. Previously we developed the first confocal handheld AOSLO (HAOSLO) capable of in vivo cone photoreceptor imaging in supine and non-cooperative patients. Here, we introduce the first multimodal (M-)HAOSLO for confocal and non-confocal split-detection (SD) imaging to allow for more comprehensive patient data collection. Aside from its unprecedented miniature size and weight, M-HAOSLO is also the first system to perform sensorless wavefront-corrected SD imaging of cone photoreceptors.

Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm

The transverse resolution of optical coherence tomography is decreased by aberrations introduced from optical components and the tested samples. In this paper, an automated fast computational aberration correction method based on a stochastic parallel gradient descent (SPGD) algorithm is proposed for aberration-corrected imaging without adopting extra adaptive optics hardware components. A virtual phase filter constructed through combination of Zernike polynomials is adopted to eliminate the wavefront aberration, and their coefficients are stochastically estimated in parallel through the optimization of the image metrics. The feasibility of the proposed method is validated by a simulated resolution target image, in which the introduced aberration wavefront is estimated accurately and with fast convergence. The computation time for the aberration correction of a 512 × 512 pixel image from 7 terms to 12 terms requires little change, from 2.13 s to 2.35 s. The proposed method is then applied for samples with different scattering properties including a particle-based phantom, ex-vivo rabbit adipose tissue, and in-vivo human retina photoreceptors, respectively. Results indicate that diffraction-limited optical performance is recovered, and the maximum intensity increased nearly 3-fold for out-of-focus plane in particle-based tissue phantom. The SPGD algorithm shows great potential for aberration correction and improved run-time performance compared to our previous Resilient backpropagation (Rprop) algorithm when correcting for complex wavefront distortions. The fast computational aberration correction suggests that after further optimization our method can be integrated for future applications in real-time clinical imaging.

Compact and contactless reflectance confocal microscope for neurosurgery

Visual guidance at the cellular level during neurosurgical procedures is essential for complete tumour resection. We present a compact reflectance confocal microscope with a 20 mm working distance that provided <1.2 µm spatial resolution over a 600 µm × 600 µm field of view in the near-infrared region. A physical footprint of 200 mm × 550 mm was achieved using only standard off-the-shelf components. Theoretical performance of the optical design was first evaluated via commercial Zemax software. Then three specimens from rodents: fixed brain, frozen calvaria and live hippocampal slices, were used to experimentally assess system capability and robustness. Results show great potential for the proposed system to be translated into use as a next generation label-free and contactless neurosurgical microscope.

Fully refractive adaptive optics fluorescence microscope using an optofluidic wavefront modulator

Adaptive optics (AO) represents a powerful range of image correction technologies with proven benefits for many life-science microscopy methods. However, the complexity of adding a reflective wavefront modulator and in some cases a wavefront sensor into an already complicated microscope has made AO prohibitive for its widespread adaptation in microscopy systems. We present here the design and performance of a compact fluorescence microscope using a fully refractive optofluidic wavefront modulator, yielding imaging performance on par with that of conventional deformable mirrors, both in correction fidelity and articulation. We combine this device with a modal sensorless wavefront estimation algorithm that uses spatial frequency content of acquired images as a quality metric and thereby demonstrate a completely in-line adaptive optics microscope that can perform aberration correction up to 4^th radial order of Zernike modes. This entirely new concept for adaptive optics microscopy may prove to extend the performance limits and widespread applicability of AO in life-science imaging.

Handheld spectrally encoded coherence tomography and reflectometry for motion-corrected ophthalmic optical coherence tomography and optical coherence tomography angiography

Optical coherence tomography (OCT) is the gold standard for quantitative ophthalmic imaging. The majority of commercial and research systems require patients to fixate and be imaged in a seated upright position, which limits the ability to perform ophthalmic imaging in bedridden or pediatric patients. Handheld OCT devices overcome this limitation, but image quality often suffers due to a lack of real-time aiming and patient eye and photographer motion. We describe a handheld spectrally encoded coherence tomography and reflectometry (SECTR) system that enables simultaneous en face reflectance and cross-sectional OCT imaging. The handheld probe utilizes a custom double-pass scan lens for fully telecentric OCT scanning with a compact optomechanical design and a rapid-prototyped enclosure to reduce the overall system size and weight. We also introduce a variable velocity scan waveform that allows for simultaneous acquisition of densely sampled OCT angiography (OCTA) volumes and widefield reflectance images, which enables high-resolution vascular imaging with precision motion-tracking for volumetric motion correction and multivolumetric mosaicking. Finally, we demonstrate in vivo human retinal OCT and OCT angiography (OCTA) imaging using handheld SECTR on a healthy volunteer. Clinical translation of handheld SECTR will allow for high-speed, motion-corrected widefield OCT and OCTA imaging in bedridden and pediatric patients who may benefit ophthalmic disease diagnosis and monitoring.

Adaptive optics in the mouse eye: wavefront sensing based vs. image-guided aberration correction

Adaptive Optics (AO) is required to achieve diffraction limited resolution in many real-life imaging applications in biology and medicine. AO is essential to guarantee high fidelity visualization of cellular structures for retinal imaging by correcting ocular aberrations. Aberration correction for mouse retinal imaging by direct wavefront measurement has been demonstrated with great success. However, for mouse eyes, the performance of the wavefront sensor (WFS) based AO can be limited by several factors including non-common path errors, wavefront reconstruction errors, and an ill-defined reference plane. Image-based AO can avoid these issues at the cost of algorithmic execution time. Furthermore, image-based approaches can provide improvements to compactness, accessibility, and even the performance of AO systems. Here, we demonstrate the ability of image-based AO to provide comparable aberration correction and image resolution to the conventional Shack-Hartmann WFS-based AO approach. The residual wavefront error of the mouse eye was monitored during a wavefront sensorless optimization to allow comparison with classical AO. This also allowed us to improve the performance of our AO system for small animal retinal imaging.

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.

Automated sensorless single-shot closed-loop adaptive optics microscopy with feedback from computational adaptive optics

Traditional wavefront-sensor-based adaptive optics (AO) techniques face numerous challenges that cause poor performance in scattering samples. Sensorless closed-loop AO techniques overcome these challenges by optimizing an image metric at different states of a deformable mirror (DM). This requires acquisition of a series of images continuously for optimization - an arduous task in dynamic in vivo samples. We present a technique where the different states of the DM are instead simulated using computational adaptive optics (CAO). The optimal wavefront is estimated by performing CAO on an initial volume to minimize an image metric, and then the pattern is translated to the DM. In this paper, we have demonstrated this technique on a spectral-domain optical coherence microscope for three applications: real-time depth-wise aberration correction, single-shot volumetric aberration correction, and extension of depth-of-focus. Our technique overcomes the disadvantages of sensor-based AO, reduces the number of image acquisitions compared to traditional sensorless AO, and retains the advantages of both computational and hardware-based AO.

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.

IsoSense: frequency enhanced sensorless adaptive optics through structured illumination

We present IsoSense, a wavefront sensing method that mitigates sample dependency in image-based sensorless adaptive optics applications in microscopy. Our method employs structured illumination to create additional high spatial frequencies in the image through custom illumination patterns. This improves the reliability of image quality metric calculations and enables sensorless wavefront measurement even in samples with sparse spatial frequency content. We demonstrate the feasibility of IsoSense for aberration correction in a deformable-mirror-based structured illumination super-resolution fluorescence microscope.

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for 'active acquisition'-embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning-based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via 'active acquisition' serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning-based clinical data mining.