Point-scanning OCT systems often use a pair of sequential, single-axis galvanometer scanners to acquire volumetric data. This can introduce uncertainty in the beam position at the ocular pupil plane, an effect known as beam wander or pupil wobble, which can distort the resulting images. We propose a new approach to characterize and optically correct the pupil wobble with an additional scanning mirror placed anti-conjugate to the pupil plane. We evaluate this method by modeling the pattern of pupil wobble present in a research OCT system both theoretically and experimentally, and correcting for it with the proposed method.
Real-time volumetric microscope-integrated OCT (MIOCT) visualization of ophthalmic surgeries is limited by the narrow field of view of OCT relative to the movement of the surgical instruments, requiring extensive manual repositioning by a trained operator. We developed a computer vision system for instrument tracking that utilizes a microscope video camera and a deep-learning object detector trained on synthetic data, which consisted of 3D rendered models of surgical instruments alongside an eye model. This system was then tested in a clinical MIOCT platform, providing high fidelity, video-rate (>40 Hz) object tracking of a cataract surgery instrument over a model eye phantom.
Optical Coherence Tomography (OCT) is a preferred technology with imaging ocular diseases. However, traditional methods require patients to be stable, preventing those with involuntary eye motion from obtaining accurate OCT images. We demonstrate a pupil tracking and aiming system that utilizes a CompactRIO embedded system for real-time image processing. We show that the pupil tracking and aiming yield an amplitude attenuation of up to 24.08 dB at stage frequencies below 0.5 Hz, outperforming our previous implementation with response latency sufficient to partially stabilize OCT images and potentially provide accurate OCT imaging for those with involuntary eye and head movement.
KEYWORDS: Image segmentation, Scanners, Eye models, Data acquisition, Systems modeling, Optical coherence tomography, Open source software, Real time imaging, Process control, Eye
Acquiring OCT images from dual sample arms at the same session is helpful in applications such as multi-directional imaging (angle independent Doppler, speckle reduction), spatially multiplexed imaging (multiple fields of view or depths), and acquisition of two separate imaging fields (whole eye or binocular imaging). However, dual sampling requires complex custom control, coordination, and processing. Vortex, an open-source OCT software library has a powerful and flexible OCT framework that accommodates custom OCT acquisitions and processing as well as support for auxiliary hardware utilized in research systems. Here we use Vortex based custom software to control interleaved B-Scan level switching between anterior segment and retinal imaging with a single OCT scanner utilizing real-time independent processing of the two samples.
Slit lamps are a common ophthalmic instrument used for examining the ocular anterior segment by projecting a rectangular beam of light onto the eye. Conventional slit lamp configurations require the patient to stabilize themselves using a chin rest and forehead band limiting access to patients who are mobility impaired. We developed a slit lamp module for a robotic arm to allow for autonomous imaging of a slit on the eye of individuals without physical head stabilization at a working distance of 125 mm. Here we describe the optical performance of the custom slit lamp module and present autonomous aligned imaging of a corneal phantom mounted in a mannequin head.
We demonstrate in vivo imaging with a robotically aligned OCT (RAOCT) platform that incorporates interchangeable imaging modules with integrated pupil tracking cameras. Our OCT imaging platform consisted of a fixed scan head mounted to a cooperative robot and interchangeable cornea and retinal imaging modules with their own integrated pupil cameras. We validated pupil tracking in both imaging modules (<11 µm accuracy, <±4.5 µm precision). We utilized this platform for in vivo imaging of multiple target tissues of interest in a single imaging session. This flexible design enables the ability to develop new imaging modules for new robotically aligned applications.
Conventional Optical Coherence Tomography (OCT) suffers from the frame-rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. We extended the conventional probabilistic adaptive scanning technique that overcomes this tradeoff with machine-learning-based scene prediction and kinodynamic path planning based on the Clustered Traveling Salesperson Problem. In online imaging, we found that our new technique produces an equivalent frame rate speed-up as previously reported while creating higher quality output OCT images. These results generalized across scenes of varying types, including those of intrasurgical relevance.
OCT angiography (OCTA) is an extension of optical coherence tomography (OCT) that identifies motion contrast from moving red blood cells to map retinal vasculature in vivo. We propose to use robotically-aligned optical coherence tomography (RAOCT) to acquire OCTA data at multiple illumination angles on the retina in order to reduce shadowing artifacts and enhance vessel visualization. Using RAOCT, retinal volumes were automatically acquired from consented subjects at various pupil entry positions and processed to generate en face OCTA images. These OCTA images were compared to identify areas of changed visualization and reduced shadowing artifact when varying illumination angle.
We present a flexible optical coherence tomography (OCT) imaging platform that allows for interchangeable imaging modules for specific target tissues of interest while meeting the requirements for robotically aligned OCT including integrated pupil tracking cameras. Our OCT imaging platform consisted of a fixed scan head (analogous to an SLR camera body) mounted to the robot and interchangeable anterior chamber (AC) and retinal imaging modules with their own integrated pupil cameras. We validated our system in both phantom and ex vivo porcine eyes. This flexible design enables the ability to develop new imaging modules for new robotically aligned applications.
Ophthalmic optical coherence tomography (OCT) has achieved remarkable clinical success but remains sequestered in ophthalmology specialty offices. Recently introduced robotic OCT systems seek to expand patient access but fall short of their full potential due to significant imaging workspace and motion planning restrictions. Here, we present a next-generation robotic OCT system capable of imaging in any configuration that is mechanically reachable. This system overcomes prior restrictions by eliminating fixed-base tracking components, extending robot reach, and planning alignment in six degrees of freedom. With this robotic system, we show repeatable subject imaging independent of posture under widely varying head orientations.
Optical coherence tomography (OCT) has wide application in medicine, particularly ophthalmology. In the anterior chamber of the eye, OCT can potentially image blood cells to monitor cellular response to injury or inflammation. However, low volumetric refresh rates limit OCT in applications that require the tracking of individual, moving cells. Therefore, we propose an efficient 3D cell tracking using adaptive scanning OCT. Adaptive scanning prioritizes capturing regions of interest that change from volume to volume. Using depth information from OCT A-scans, our cell tracking method successfully localized simulated cells across multiple OCT volumes.
Optical coherence tomography (OCT) revolutionized diagnostics in ophthalmology.
Traditional OCT assumes static subjects and produces artifacts during motion.
Robotically-aligned OCT mitigates these artifacts via hardware motion compensation.
However, hardware imperfections result in residual motion error.
Here we present a digital motion correction approach where we utilize synchronized sensing of the pupil and scanner components to measure the state of the imaging process over time.
We subsequently map each A-scan to its corresponding location in the volume given sensor data during its acquisition.
We demonstrate our correction in human imaging and observed 68% reduction in maximum residual axial error.
Point-scan swept-source optical coherence tomography systems (SS-OCTs) are subject to volumetric framerate limits governed by source sweep rate and scanner dynamics. For scenes with dynamic features on static backgrounds, adaptive scanning escapes these limits by visiting scan positions only as needed. We implemented adaptive scanning using a probabilistic approach that balanced re-imaging of known dynamic positions with exploration for undiscovered ones. We evaluated our approach in model systems that simulated ophthalmic surgery and multi-target tracking using a 200 kHz SS-OCT system. We demonstrated framerate speedups of 6.7x and 8.0x, respectively, performance that would have otherwise required a significantly faster source.
Unlike conventional ophthalmic OCT, robotically aligning OCT (RAOCT) removes the requirement for close patient/operator proximity and enables remote patient imaging by autonomously aligning itself to the patient while the operator is physically elsewhere. We report kilometer-scale distance between OCT operator and patient and the first robotically aligned OCT angiography images. We acquired remote volumetric RAOCT retinal images from healthy and diseased eyes at the Duke Eye Center on both room-to-room (10m between imager and subject) and between clinic sites(>10km between imager and subject). This can serve as a foundation for socially distanced or telehealth retinal OCT without physically present technicians.
Optical coherence tomography (OCT) has revolutionized diagnostics in ophthalmology. Montaging of multiple OCT volumes allows for wide field retinal volumes. However, OCT requires an operator to align the scanner and requires patient cooperation to fixate on multiple targets, one for each volume in the montage. We have developed a robot-mounted OCT scanner that automatically aligns with the subjects’ eye by compensating motion and gaze error at multiple entry angles, allowing acquisition of volumes from multiple regions of interest without chin or fore rest stabilization or a fixation target. We demonstrate our system by montaging a retinal volume acquired from a free-standing subject.
Optical coherence tomography (OCT) has revolutionized diagnostics in ophthalmology. Traditionally, OCT requires an operator and patient cooperation to compensate refraction error as well as align the scanner with the subject’s eye. We have developed a robot-mounted OCT scanner that automatically focuses and aligns with the subjects’ eye by compensating motion, gaze, and refraction error. The system utilizes a combination of face and pupil tracking cameras to align while focusing through digital control of a tunable lens. We demonstrate our system by self-aligning with human eyes subject to physiological motion and gaze change as well as correcting defocus of a phantom eye.
The physical distancing requirements necessary to prevent spread of the novel coronavirus, SARS-CoV-2, requires a change in approach for clinical ophthalmic imaging. Conventional optical coherence tomography (OCT) systems require patients to position themselves in chin/forehead rests for stabilization with the system operator in close proximity. We developed a robotically aligning OCT (RAOCT) system that provides volumetric retinal images encompassing both the optic nerve head and fovea. Our RAOCT system self aligned to subjects’ eyes (seated, no contact with restraints), acquired OCT images of both normal and diseased retinas, all with allowing the operator behind a barrier >2 m from the subjects.
Optical coherence tomography (OCT) has revolutionized diagnostics in ophthalmology. However, OCT traditionally requires an operator and patient cooperation to align a scanner with the subject’s eye and image a specific location of the retina. We have developed a robot-mounted OCT scanner that automatically aligns with the subjects’ eye by compensating motion and gaze. In addition to using face and pupil tracking feedback to align the scanner, the system uses gaze feedback to track a retinal region of interest, such as the fovea. We demonstrate our system by tracking the fovea of human eyes subject to physiological motion and gaze change.
High speed optical coherence tomography (OCT) systems with A-scan rates greater than 100 kHz allow for 4D visualizations in applications such as intraoperative OCT. However, traditional triangle or sawtooth waveforms used to drive galvanometer scanners often have frequency content that exceeds the bandwidth of the scanners, leading to distorted scans. Sinusoidal waveforms used to drive resonant scanners also lead to distorted scans due to the nonlinear scan velocity. Additionally, with raster scan patterns, the scanner needs time to stop and reverse direction in between B-scans, leading to significant acquisition dead time. Continuous scan patterns such as constant frequency spiral scanning or Lissajous scanning no longer have acquisition dead times, but suffer from non-uniform sampling across the imaging plane. We previously introduced constant linear velocity (CLV) spiral scanning as a novel scan pattern to maximize the data acquisition efficiency of high speed OCT systems. While this continuous scan pattern has no acquisition dead time and produces more uniform sampling compared to raster scanning, it required significant processing time. We introduce a processing pipeline implemented using CUDA in C++, which drastically reduces the amount of processing time needed, allowing real time visualization of 4D OCT data. To demonstrate its potential utility, we used CLV scanning with a 100 kHz swept-source OCT system to image retinas of enucleated porcine eyes undergoing mock ophthalmic surgery movements. Additionally, we rendered these volumes in virtual reality (VR) in real time, allowing for interactive manipulation and sectioning.
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