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Review  |   January 2025
New Directions for Ophthalmic OCT – Handhelds, Surgery, and Robotics
Author Affiliations & Notes
  • Julia Foust
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
  • Morgan McCloud
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
  • Amit Narawane
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
  • Robert M. Trout
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
  • Xi Chen
    Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
  • Al-Hafeez Dhalla
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
  • Jianwei D. Li
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
  • Christian Viehland
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
  • Mark Draelos
    Department of Robotics, University of Michigan, Ann Arbor, MI, USA
    Department of Ophthalmology, University of Michigan School of Medicine, Ann Arbor, MI, USA
  • Lejla Vajzovic
    Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
  • Ryan P. McNabb
    Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
  • Anthony N. Kuo
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
    Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
  • Cynthia A. Toth
    Department of Biomedical Engineering, Duke University, Durham, NC, USA
    Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
  • Correspondence: Anthony N. Kuo, DUMC Box 3802, Durham, NC 27710, USA. e-mail: [email protected] 
  • Footnotes
     JF, MM, AN, and RMT contributed equally in the writing of this paper and should be considered co-first authors.
Translational Vision Science & Technology January 2025, Vol.14, 14. doi:https://doi.org/10.1167/tvst.14.1.14
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      Julia Foust, Morgan McCloud, Amit Narawane, Robert M. Trout, Xi Chen, Al-Hafeez Dhalla, Jianwei D. Li, Christian Viehland, Mark Draelos, Lejla Vajzovic, Ryan P. McNabb, Anthony N. Kuo, Cynthia A. Toth; New Directions for Ophthalmic OCT – Handhelds, Surgery, and Robotics. Trans. Vis. Sci. Tech. 2025;14(1):14. https://doi.org/10.1167/tvst.14.1.14.

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Abstract

The introduction of optical coherence tomography (OCT) in the 1990s revolutionized diagnostic ophthalmic imaging. Initially, OCT’s role was primarily in the adult ambulatory ophthalmic clinics. Subsequent advances in handheld form factors, integration into surgical microscopes, and robotic assistance have expanded OCT’s utility and impact outside of its initial environment in the adult outpatient ophthalmic clinic. In this review, we cover the use of OCT in the neonatal intensive care unit (NICU) environment with a handheld OCT, recent developments in intraoperative OCT for data visualization and measurements, and recent work and demonstration of robotically aligned OCT systems outside of eye clinics. Of note, advances in these areas are a legacy of our colleague, the late Joseph Izatt. OCT has been an important innovation for ocular diagnostics, and these advances have helped it continue to extend in new directions.

Introduction
Since the initial report of optical coherence tomography (OCT) in 19911 and its subsequent development and commercialization, OCT has changed and altered the diagnostic examination and care of the eyes. Beginning with its first reported demonstrations for visualizing the retina2 and cornea3 in vivo, OCT has provided clinicians with micro-scale cross-sectional views of ocular structures in their patients without need for ex vivo preparations. These novel views ultimately revolutionized the diagnostic management of glaucoma4 and retinal disease.5 
Despite the practice-changing role of OCT, it remained a tool of the adult, ambulatory outpatient clinic. OCT and its diagnostic benefits were less accessible for patients who were not adults and ambulatory – for instance, younger pediatric patients, patients undergoing surgery, and those in critical care environments. The recent pandemic with its physical distancing recommendations also made it difficult for physicians and their supporting staff to work in close proximity with their patients, including performing OCT examinations. 
In this paper, we briefly review handheld, intraoperative, and robotically tracked variants of OCT, how these variants increased accessibility of OCT by bringing it to the point of care, and recent developments for these variants with the potential to continue OCT's role in changing care for our patients for the better. 
Handhelds and Neonatal Critical Care Environments
One of the first forays of OCT outside of the adult ambulatory clinic was for pediatric and neonatal use. Pediatric retinal diagnostics are important for screening and monitoring retinal disease in our youngest patients. As an example, retinopathy of prematurity (ROP) is a leading cause of childhood blindness in the United States and worldwide6,7 and is caused by delayed or abnormal retinal vascular growth associated with preterm birth.8 Regular bedside screening is required to monitor the progress of retinal vascularization, identify pathological neovascularization, and administer timely treatment. The increased survival of the youngest infants and the paucity of pediatric ophthalmologists in many areas of the country lead to a persistently increasing screening burden.7,9 However, the current screening methods for ROP, including indirect ophthalmoscopy examination with scleral depression and contact widefield fundus photography, have been shown to be stressful for these young infants and may have lifelong neurological impact.1016 
OCT had a potential role to address these issues in pediatric retinal diagnostics given OCT's revolutionary role in adult retinal diagnostics in addition to OCT's use of infrared (non-visible) light instead of the bright white illumination used in indirect ophthalmoscopy or fundus photography. Although commercial tabletop OCT systems meant for adults have been used for older children and have informed the evaluation of many pediatric retinal diseases, infants and younger children do not have routine access to this current standard of care.17 At Duke, we have pioneered the development of multiple generations of investigational handheld OCT probes (Fig. 1) and have demonstrated acquisition of both OCT and OCT angiography (OCTA) data in infants and children under institutional review board (IRB) protocols.1827 The design of our handheld probes has evolved over time through a continuous feedback loop between engineering and clinical teams to meet the requirements of the pediatric and neonatal environments. The first-generation device primarily targeted non-seated adult ambulatory use (e.g. preclinical small animal use, human intraoperative use, and pediatric examination under anesthesia use), and was later commercialized through Bioptigen to provide general availability for those uses. With each subsequent generation, we have incorporated feedback from the preceding system to improve usability, durability, and ergonomics. These handheld OCT systems have made it possible to regularly image infants in a variety of settings, including the operating room, the neonatal intensive care unit, and pediatric clinics under clinical research protocols. 
Figure 1.
 
Multiple generations of handheld OCT probes. Our group has developed multiple generations of handheld OCT probes (references in the text). These images show one commercial (Bioptigen: left two images) and three investigational OCT and OCT angiography probes we have used for imaging infants at the bedside.
Figure 1.
 
Multiple generations of handheld OCT probes. Our group has developed multiple generations of handheld OCT probes (references in the text). These images show one commercial (Bioptigen: left two images) and three investigational OCT and OCT angiography probes we have used for imaging infants at the bedside.
Studies performed with these investigational systems enabled the discovery of multiple features of the infant retina that were simply not visible with the ophthalmoscope (Fig. 2) including infant macular edema, a common observation in preterm infants but rare in term-born infants.2830 They have also extended our fundamental knowledge of human retinal development; with the advancement of smaller handheld OCT probes and increased imaging speed, we visualized for the first time the process of human perifoveal vascular development in infants.31 Compared with standard bedside clinical ophthalmic examinations with indirect ophthalmoscopy, bedside imaging with noncontact handheld OCT is less stressful.21 Studies comparing commercial spectral-domain and investigational swept-source OCT systems have shown the reproducibility of imaging and utility of handheld OCT as a viable screening tool for retinal disease.2224,3236 OCT-based retinal thicknesses and vitreous biomarkers observed on images of the macula and of the vascular-avascular junction have been shown to associate with ROP severity.22,32,35 Handheld OCT has also been proven useful in determining the nature of retinal elevation, foveal involvement, and with research systems with sufficient speed, and vascular features in advanced ROP.37 In addition, retinal OCT features have been shown to associate with systemic health findings and oxygen requirement in the nursery.25,38,39 In light of the limited field of view with non-contact imaging, several investigational high-speed handheld widefield OCT systems have been developed for ROP and infant retinal disease imaging.4043 Although all of these handheld systems are currently in research use, with the advent of lower cost components, portable systems are likely to progress to address the unmet need for widespread imaging for infants and young children.44 
Figure 2.
 
Research handheld OCT imaging from the BabySTEPS study NCT02887157 with a non-contact investigational handheld OCT system demonstrating the wealth of structural information in the image of the retinal response to ROP treatment. Preterm infant eyes are prior to (top row) and one week after (bottom row) anti-VEGF treatment for type 1 ROP. The cross-sectional OCT scans in (B) and € (and arrows over extraretinal neovascularization) are extracted from the site of the green line in the OCT volumes, seen in retina view in (A) and (D). The blue asterisks mark retinal locations before (A, C) and after (D, E) treatment.
Figure 2.
 
Research handheld OCT imaging from the BabySTEPS study NCT02887157 with a non-contact investigational handheld OCT system demonstrating the wealth of structural information in the image of the retinal response to ROP treatment. Preterm infant eyes are prior to (top row) and one week after (bottom row) anti-VEGF treatment for type 1 ROP. The cross-sectional OCT scans in (B) and € (and arrows over extraretinal neovascularization) are extracted from the site of the green line in the OCT volumes, seen in retina view in (A) and (D). The blue asterisks mark retinal locations before (A, C) and after (D, E) treatment.
Regarding commercial availability, an armature-fixated OCT system is in commercial use in the United Kingdom, Europe, and the Asia Pacific region for supine imaging, although the mass of the system's base limits its mobility.4547 In the United States, handheld OCTs were previously available through Bioptigen (which was subsequently acquired by Leica), but they are no longer available as of the time of this article. We anticipate other commercial entities (e.g. Fig. 3) will fill this need for generally available handheld OCT systems given the important clinical role these handheld systems have. 
Figure 3.
 
Representative widefield handheld OCT images from an investigational swept-source handheld OCT system (Theia Imaging, Durham, NC). The investigational device used to acquire these images has not been cleared by the FDA. Images were acquired as part of a research study conducted under an abbreviated investigational device exemption and IRB-approved protocols. The red dashed box (A) and the yellow dashed box (B) in the widefield images correspond to approximate field of view of non-contact scanning (C, D, respectively). Optic nerve head is marked with red/blue asterisk and fovea with yellow. Right columns show the retinal views segmented at the RPE to highlight retinal vasculature in the top row and choroidal vasculature in the bottom row in a healthy eye (E and F, and the same as shown in the cross section) and an eye with retinal vascular disease (G, H). Red dashed circle denotes typical ROP Zone I region, the asterisk marks the fovea, and the red arrows point to the ampulla of the vortex veins.
Figure 3.
 
Representative widefield handheld OCT images from an investigational swept-source handheld OCT system (Theia Imaging, Durham, NC). The investigational device used to acquire these images has not been cleared by the FDA. Images were acquired as part of a research study conducted under an abbreviated investigational device exemption and IRB-approved protocols. The red dashed box (A) and the yellow dashed box (B) in the widefield images correspond to approximate field of view of non-contact scanning (C, D, respectively). Optic nerve head is marked with red/blue asterisk and fovea with yellow. Right columns show the retinal views segmented at the RPE to highlight retinal vasculature in the top row and choroidal vasculature in the bottom row in a healthy eye (E and F, and the same as shown in the cross section) and an eye with retinal vascular disease (G, H). Red dashed circle denotes typical ROP Zone I region, the asterisk marks the fovea, and the red arrows point to the ampulla of the vortex veins.
Intraoperative OCT
Another environment for OCT outside of the adult ambulatory clinic was the operating room. OCT's exceptional ability to provide high resolution, depth-resolved imaging made it an attractive candidate not only for diagnostic applications in the clinic, but also for use in ophthalmic surgery in tandem with standard surgical microscopy to further enhance intraoperative visual guidance.4857 Initially, intraoperative OCT was limited to live cross-sectional (OCT B-scan) views, which provided a limited view of the surgical field. As OCT technology and computational processing speeds continued to improve, we saw the introduction of single 3D OCT volume capability, providing much-needed lateral context to the detailed depth information in individual B-scans. Most current commercial intraoperative OCT systems (e.g. Leica EnFocus and Zeiss ReScan) have this capability to provide multiple 2D B-scans per second at a single location or a few 3D volumes per second. Further improvements in the research domain have led to the introduction of live volumetric (4D-OCT) imaging with multiple volumes per second, providing surgeons with the ability to visualize entire surgical maneuvers in real-time. This multimodal imaging technique, termed 4D microscope-integrated optical coherence tomography (4D-MIOCT), has demonstrated potential for clinical utility in a variety of applications in ophthalmic surgery.5884 In this review, we highlight two features to enhance intraoperative OCT: multi-modal image fusion and quantitative measurements. 
Image Fusion of Color Microscopy With Intraoperative OCT
Although 4D-MIOCT is promising, there remains room for improvement in the processing and presentation of the information to the surgeon user. These include more effective methods of communicating important 3D information encoded in the volumetric OCT data, such as depth-based shading for improved depth perception, boundary-based shading for enhanced 3D structural visualization, and virtual reality interfaces for more flexible and contextualized viewing experience.8597 There is also the additional issue that due to volumetric OCT's relatively small fields of view and insufficient visualization of orienting features, it is still difficult to perform OCT-guided surgery without accompanying stereo color microscopy.98,99 In current implementations, visualizations from each mode are typically displayed separately side-by-side or overlaid, placing a significant cognitive overhead on the surgeon in identifying features of interest, mentally registering them across separate images, and integrating the information presented to make clinical decisions.100,101 Due to this effect, surgeons tend to focus on the microscopy channel the majority of the time, as this visualization is superior for general guidance, only utilizing the higher resolution OCT channel for a narrow set of specific maneuvers. Lowering this mental overhead associated with integration of OCT and microscopy information could allow the surgeon to use OCT guidance more frequently and effectively throughout the surgery. 
The most direct solution to this problem is the removal of the spatial separation between the channels on display, removing the need to mentally register them entirely. However, the optimal implementation of this proposal requires careful consideration to detail concerning which visual features from each modality are of importance, and how they might be combined within the reduced bandwidth of a single image while remaining appropriately represented. To this end, several key requirements for effective surgical image guidance are detailed to inform the strategy of how the OCT and microscopy channels are combined. First, the context of the surgical target must be maintained, such that the surgeon is able to effectively understand its environment. This demands a sufficiently large field of view (FOV) and/or visualization of familiar structures such that the broader positional relationship between the tool and target is clear. Second, it should possess fine resolution beyond that required to localize the target as described previously, to the degree that the necessary manipulation of the target can be performed precisely and accurately. Finally, the visualization must possess sufficiently high frame rate and low latency that the dynamics of both navigation and target interaction are clearly communicated. 
Projecting these requirements into the context of retinal 4D-MIOCT surgery, features of importance from OCT are identified as tissue surface topology (curves, indentations, bumps, etc.), subsurface structures (choroidal vessels and layer thicknesses) and interactions between surgical tools and tissues. From color microscopy imaging, it is desired to maintain the high amount of context it provides via color contrast for retinal vessels and colored tools, fundus tissue hue, and increased transverse and axial fields of view. Using detected feature surfaces (surgical tool, retina, RPE, cornea, iris, etc.) from 3D-OCT data (Fig. 4, row 1) and corresponding color microscopy imaging (Fig. 4, row 2), a feature-informed fusion of volumetric OCT and color digital microscopy of 4D-MIOCT was conceived (Fig. 4, row 3).102,103 
Figure 4.
 
Surgical 3D-OCT (top) with corresponding surgical microscopy (middle) combined to generate a single fused visualization (bottom) for enhanced guidance of surgical maneuvers. Subjects include human retinal surgery with a soft-tip cannula (column 1), finesse loop (column 2), and forceps (column 3).102 Column 4 is of a cornea and a partially collapsed anterior chamber (4).103
Figure 4.
 
Surgical 3D-OCT (top) with corresponding surgical microscopy (middle) combined to generate a single fused visualization (bottom) for enhanced guidance of surgical maneuvers. Subjects include human retinal surgery with a soft-tip cannula (column 1), finesse loop (column 2), and forceps (column 3).102 Column 4 is of a cornea and a partially collapsed anterior chamber (4).103
In this embodiment of OCT and color microscopy fusion, color image information is combined at the retinal pigment epithelium (RPE) in posterior imaging and the iris in anterior, with subsurface OCT features like choroidal vessels visualized via their modulation (shadowing) of the rendered color's intensity. Combined with the rendering of the peripheral color image surrounding the OCT volume, the context available to the surgeon is greatly enhanced compared to the standard OCT volume. Simultaneously, the high level of detail in retinal and corneal surfaces associated with the OCT data is visualized via Fresnel- and Phong-type shaders (“glossy” appearance of these surfaces), effectively presenting surface features without causing a detrimental degree of occlusion to color and subsurface features. Overall, fused rendering of 4D-MIOCT data possess great potential to improve the clinical utility of OCT for guidance of surgical maneuvers, capturing desirable characteristics from each modality, and presenting them within a single, more readily interpretable result. 
Quantitative Measurements
Whereas the ability of intraoperative OCT to provide live volumetric visualization of surgical procedures is valuable, the information provided is purely qualitative. However, new surgical techniques to deliver therapeutics, such as subretinal injections like the US Food and Drug Administration (FDA)-approved Luxturna gene therapy,104 have highlighted the need for quantitative measurements to precisely and accurately track drug delivery dosage. Unlike telecentric imaging of the anterior segment, raw retinal OCT images are captured such that the lateral scan range is a function of degrees not millimeters. Distortions introduced by the OCT system optics and the individual's eye affect each captured retinal OCT image.105 Utilizing optical models of both, one can recreate imaging conditions and ray trace each A-scan to reconstruct the posterior eye in linear dimensions.106 
Our group's initial approach to this problem intraoperatively involved calibrating voxel dimensions and using an experimentally derived correction factor when computing quantitative measurements from segmented regions.107,108 More recently, we have taken advantage of knowledge of each component of the optical train to “customize” the calibration to each individual eye. In this way, an optical model of the OCT system, indirect viewing optics, and state-of-the-art eye models are leveraged to determine the voxel pitch for a given OCT image, which when combined with segmentation of a region of interest yields the volume of that region.109,110 In practice, during an operation utilizing our intraoperative MIOCT system, the surgeon records several measurements to define the variables needed to appropriately adjust the baseline optical model. With an Oculus BIOM 5c modified to include a ruler between the tops of the correction lens and wide field lens, the surgeon brings the retina into focus through the microscope and records the measurement on the ruler. Subsequently, the surgeon places a spacer of known thickness and refractive index on the eye and moves the wide field lens to touch the spacer, similarly recording the distance on the ruler. Finally, the OCT operator adjusts the reference arm position to bring the retina into the bottom of the B-scan image, recording the reference arm position. Importantly, this process was designed to be as efficient as possible and requires only a few measurements on the part of the surgical and OCT support team during the procedure. 
Thus, in addition to prior knowledge about the OCT system and the wide-field Polans eye model,111 the aforementioned information recorded during surgery is then sufficient to determine the entire sample arm up to the corneal apex as well as the axial eye length to be used in modeling the eye. Optical modeling of the sample arm path to the patient’s retina can be performed post-surgery using OpticStudio (Zemax LLC), and, subsequently, voxel pitch can be determined by simulating the galvonometer sweep pattern (lateral pitch) and using knowledge of the spectral sampling bandwidth and (assumed) eye refractive index (axial pitch). With knowledge of the voxel pitch throughout the volumetric image, the region of interest is segmented and the volume of that region is calculated. Our model has been validated by determining volumes of alumina ceramic spheres in a model eye phantom, diameters of surgical tools and volumes of subretinal injections in ex vivo porcine eyes, and diameters of surgical tools intraoperatively in human eyes.109 In addition, we have demonstrated the efficacy of this approach intraoperatively on three patients with submacular hemorrhages secondary to exudative age-related macular degeneration (AMD), with an example shown in Figure 5.110,112 Improvements in making the post-processing pipeline more efficient, for example, by building upon previous auto segmentation work,113 will be important to move this work so that a quantitative measurement procedure can be obtained during surgery instead of the current post-surgical calculation. 
Figure 5.
 
Demonstration of quantitative measurement setup with MIOCT system used in wetlab studies (top row112) and example bleb image and segmentation in a human subject using this measurement procedure (bottom row110).
Figure 5.
 
Demonstration of quantitative measurement setup with MIOCT system used in wetlab studies (top row112) and example bleb image and segmentation in a human subject using this measurement procedure (bottom row110).
Robotically Aligned OCT
The final topic of this review regards automated alignment of OCT systems with the assistance of robotic platforms. A common feature of ophthalmic OCT systems is that they require precise alignment to the eye. Conventional clinical systems often utilize a translatable tabletop interface that features mechanical head stabilization, such as a chin and forehead rests. Unfortunately, the tabletop form factor restricts clinical OCT system use to conventional ambulatory clinical environments with upright and awake patients.114 Handheld systems, as discussed earlier, eliminate some of these issues and can bring the system to the patient115; however, obtaining well-resolved images requires significant skill and steady operators.18 Although self-aligning tabletop scanners can reduce the operator burden to obtain OCT data, they still require head stabilization and cooperation of the patient. The optical and mechanical components necessary to enable self-alignment make handheld systems larger and heavier, and as a result, more difficult to operate handheld.114 To expand OCT beyond the ambulatory ophthalmic clinical environment and bring OCT to individuals that cannot be positioned in stabilization mechanisms, robotically aligned OCT (RAOCT) imaging has become an active area of development.114,116126 
This newer approach to OCT pairs multi-axis, cooperative robotic arms with vision systems and mounted OCT scanners to enable completely contactless, automatically aligned imaging of non-stabilized individuals. Here, we briefly describe the operational considerations behind the major components of the RAOCT and its use in clinical research studies. 
RAOCT System Operation
The RAOCT features a dual-tier approach for measuring and actively correcting patient motion. Multiple cameras locate both the face and target eye of the patient in 3D space. Robotic and dynamic opto-mechanical systems align to and maintain position relative to the patient, enabling volumetric OCT imaging with minimal motion artifacts (Fig. 6). 
Figure 6.
 
Robotically aligned OCT (RAOCT) system design. (A) OCT scanner mounted on a collaborative robotic arm.140 (B) Optical system diagram of the OCT engine used in RAOCT that includes dynamic system components for real-time beam steering. (C) Top down view of RAOCT auto-aligning to a freestanding individual. (D) Face tracking cameras detecting and segmenting the target eye. (E) View from the pupil tracking cameras. Pupil center (magenta cross), calculated center of corneal curvature (yellow cross), and gaze angle (green arrow) are projected onto the images. (F) Registered and averaged foveal b-scan of a healthy freestanding individual.129
Figure 6.
 
Robotically aligned OCT (RAOCT) system design. (A) OCT scanner mounted on a collaborative robotic arm.140 (B) Optical system diagram of the OCT engine used in RAOCT that includes dynamic system components for real-time beam steering. (C) Top down view of RAOCT auto-aligning to a freestanding individual. (D) Face tracking cameras detecting and segmenting the target eye. (E) View from the pupil tracking cameras. Pupil center (magenta cross), calculated center of corneal curvature (yellow cross), and gaze angle (green arrow) are projected onto the images. (F) Registered and averaged foveal b-scan of a healthy freestanding individual.129
The high-level strategy of the system is as follows: 
  • 1. Large FOV depth-resolving face tracking cameras guide robot and scanner grossly to the eye in space.
  • 2. High accuracy pupil tracking cameras activate and inform fine robot and optical alignment to the eye.
  • 3. Once aligned, OCT data are obtained while actively compensating for real-time movement to maintain alignment.
To identify the face, two depth sensing cameras (one for each eye) located on fixed external mounts or on the robot end-effector locate faces in the robot workspace.117 If a face is identified, the robot and its OCT sample arm payload move toward the position of the target eye. This brings the eye into view of multiple monochromatic pupil tracking cameras placed around the OCT sample arm carried by the robot. These calibrated cameras then segment the pupil with near infrared (NIR; <950 nm) illumination and triangulate the pupil centroid in space with high accuracy and precision (24 ± 7 µm / 31 ± 6 µm lateral/axial translation).117,127 The robot and alignment optical components can then be positioned to negate motion from the patient. This occurs without any need for contact stabilization of the individual's head, and the individual only needs to be inaccessible within the robot's workspace. In addition to the gross motion compensation provided by the robot, the OCT system itself includes two additional components to compensate for small amplitude, high frequency patient motions: (1) a linear voice coil motor which enables dynamic reference arm adjustment for axial motion correction, and (2) a fast-steering mirror located anti-conjugate to the ocular pupil for lateral motion correction (see Fig. 6). 
Gaze Compensation in RAOCT
With a highly maneuverable robot system, we can reliably measure and compensate for ocular gaze angle in real-time.116,123,124 Applying a generalized method for calculating the gaze angle (which correlates to the ocular optical axis) of the eye,128 we first calculate the center of corneal curvature (CCC) by segmenting the first Purkinje reflections from the NIR LED illumination. For every corneal reflection, we create a plane that contains the nodal point of the camera (determined during camera calibration), the triangulated pupil center, and the corneal reflection position. We find the CCC as the intersection point of all measured planes. Finally, the optic axis is defined as the axis through the CCC and the triangulated pupil center.123 
Gaze compensation with RAOCT does not rely on the skill or steadiness of human handheld operators nor does it rely on the subject's ability to fixate on targets around the scanner. With gaze tracking, one can systematically capture multiple OCT volumes at different positions relative to the ocular optical axis to synthetically increase the FOV of the system.124,129 Work has also been done to demonstrate RAOCT's ability to capture structures that are otherwise difficult to visualize from non-gaze compensated systems. For example, we have used RAOCT to automate imaging of the retina from different pupil entry positions, which prior work has shown to better visualize Henle's fiber layer in the outer retina.127,130134 
Pilot Clinical Research Studies With RAOCT
Research RAOCT systems can perform either operator-guided automatic imaging or completely autonomous, operator-free imaging. This allows for a more contactless experience for the participant and creates opportunities for imaging unstable individuals or individuals with mobility impairment, which can be necessary particularly in acute care environments like the emergency department or for physically distanced operation. We have been actively investigating RAOCT's use and performance in these settings. 
During the coronavirus disease 2019 (COVID-19) pandemic, there was an emphasis on contactless and physically distanced protocols to reduce the risk of disease transmission among medical professionals and patients. Conventional tabletop systems require the patient to contact the chin and forehead rest while the operator remains in close proximity to the patient. RAOCT is an inherently contactless imaging platform and allows for distancing between the operator and the patient and hence has the potential for physically distanced operation. We demonstrated initial distancing with RAOCT wherein the operator was behind a barrier and more than 2 m away from the RAOCT system and the imaged individual.135 The RAOCT system automatically aligned to the subject's pupil and maintained the alignment. The operator then verified and recorded the OCT image captured by the RAOCT system. In an ophthalmic clinic population, there was no measured difference in retinal thickness as compared to a clinical OCT system.135 Although additional work is necessary to implement this for larger distances, this preliminary demonstration shows the feasibility of distanced imaging with RAOCT. 
We recently reported an initial pilot study of the original RAOCT concept in an emergent care environment. The eye examination is difficult for non-eye specialists to perform,136139 and RAOCT could potentially provide modern, semi-automated diagnostic ophthalmic imaging in non-specialist settings like the emergency department. In our study utilizing RAOCT to image emergency care patients, we found that it was effective in imaging posterior eye abnormalities and that emergency physicians’ interpretation of the data from RAOCT outperformed direct ophthalmoscopy.122 With the goal of further aiding the detection of referable posterior eye pathology, a deep learning network (RobOCTNet) was developed to asynchronously classify OCT images obtained from the RAOCT system.120 RobOCTNet was shown to perform comparably to two expert human graders in identifying referral pathology. 
Initial work in RAOCT expected seated or standing cooperative patients. However, in non-ambulatory environments those assumptions may not hold. Given the maneuverability of RAOCT, there is an opportunity to expand its ability to image freely positioned individuals beyond standing or sitting generally in front of the robot.118,121,140 Head orientation and location was originally calculated via tracking cameras on fixed mounts relative to the robot base. Mounting the face tracking cameras on the robot end-effector provides a tracking workspace that moves with the scanner and allows the robot to image in any configuration that is mechanically reachable by the robot arm. As a result, when paired with gaze tracking, RAOCT can image an individual in a variety of head positions (Fig. 7) besides sitting or standing directly in front of the system. Although additional development is needed, these promising pilot studies show that RAOCT is a potential platform for automation of both the image acquisition and interpretation components of OCT. 
Figure 7.
 
Robotically aligned OCT (RAOCT) system with scanner mounted face tracking. The original RAOCT used fixed base face tracking with a single point of view; this meant the subject's face could only be seen when directly in front of the fixed tracking cameras. By mounting the face tracking cameras to the robot and scanner, the face tracking can “see” wherever the robot and scanner can reach as seen in the various face orientations in the image.121 (Second row: en face OCT, third row: B-scans, and bottom row: OCT volume 3D renders.)
Figure 7.
 
Robotically aligned OCT (RAOCT) system with scanner mounted face tracking. The original RAOCT used fixed base face tracking with a single point of view; this meant the subject's face could only be seen when directly in front of the fixed tracking cameras. By mounting the face tracking cameras to the robot and scanner, the face tracking can “see” wherever the robot and scanner can reach as seen in the various face orientations in the image.121 (Second row: en face OCT, third row: B-scans, and bottom row: OCT volume 3D renders.)
Conclusions and Tribute
OCT changed ophthalmic visualization since its inception. Each of the changes we described in this review – converting OCT into a handheld form factor, integrating OCT into a surgical microscope, and utilizing robotic tracking – increased the accessibility of the key information provided by OCT. We want to take a moment to acknowledge the individual who was critical to the development of these handheld, intraoperative, and robotic OCT systems – the late Joseph A. Izatt, PhD. Dr. Izatt completed his post-doctoral training at the Massachusetts Institute of Technology (MIT) under James Fujimoto, PhD, during the advent of OCT in the early 1990s. Dr. Izatt subsequently continued to contribute to the development of OCT, including developing the rationale for Fourier domain OCT systems over time domain systems, and in the context of this review, developing the first commercial handheld OCT systems for ophthalmology, the first surgical microscope integrated OCT systems, and the first robot tracked OCT systems. As Dr. Izatt himself would have pointed out, science requires teamwork and collaboration, and each of these developments was shepherded by talented graduate students and willing collaborators. In turn, each of us was inspired by Dr. Izatt's curiosity and drive to develop new technologies and make an impact for others. As OCT continues to develop along new frontiers and directions, we hope that Dr. Izatt's example inspires others in this field as he did for us. 
Acknowledgments
Supported by NIH U01 EY028079, R01 EY025009, R01 EY035534, P30 EY005722; Research to Prevent Blindness Unrestricted Grant to the Duke Eye Center. 
Disclosure: J. Foust, None; M. McCloud, None; A. Narawane, None; R.M. Trout, None; X. Chen, None; A.-H. Dhalla, Theia Imaging (F, O, P), Horizon Surgical (F, P), Alcon (C), Leica (P, R); J.D. Li, Leica (E); C. Viehland, Theia Imaging (F, O, P); M. Draelos, Horizon Surgical (C); L. Vajzovic, Alcon (F, C), Novartis (C, R), Clearside Biomedical (C); R.P. McNabb, Johnson & Johnson (F), Leica (P, R); A.N. Kuo, Johnson & Johnson (F), Leica (P, R); C.A. Toth, Theia Imaging (F, O, P), Zeiss Meditec (F) 
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Figure 1.
 
Multiple generations of handheld OCT probes. Our group has developed multiple generations of handheld OCT probes (references in the text). These images show one commercial (Bioptigen: left two images) and three investigational OCT and OCT angiography probes we have used for imaging infants at the bedside.
Figure 1.
 
Multiple generations of handheld OCT probes. Our group has developed multiple generations of handheld OCT probes (references in the text). These images show one commercial (Bioptigen: left two images) and three investigational OCT and OCT angiography probes we have used for imaging infants at the bedside.
Figure 2.
 
Research handheld OCT imaging from the BabySTEPS study NCT02887157 with a non-contact investigational handheld OCT system demonstrating the wealth of structural information in the image of the retinal response to ROP treatment. Preterm infant eyes are prior to (top row) and one week after (bottom row) anti-VEGF treatment for type 1 ROP. The cross-sectional OCT scans in (B) and € (and arrows over extraretinal neovascularization) are extracted from the site of the green line in the OCT volumes, seen in retina view in (A) and (D). The blue asterisks mark retinal locations before (A, C) and after (D, E) treatment.
Figure 2.
 
Research handheld OCT imaging from the BabySTEPS study NCT02887157 with a non-contact investigational handheld OCT system demonstrating the wealth of structural information in the image of the retinal response to ROP treatment. Preterm infant eyes are prior to (top row) and one week after (bottom row) anti-VEGF treatment for type 1 ROP. The cross-sectional OCT scans in (B) and € (and arrows over extraretinal neovascularization) are extracted from the site of the green line in the OCT volumes, seen in retina view in (A) and (D). The blue asterisks mark retinal locations before (A, C) and after (D, E) treatment.
Figure 3.
 
Representative widefield handheld OCT images from an investigational swept-source handheld OCT system (Theia Imaging, Durham, NC). The investigational device used to acquire these images has not been cleared by the FDA. Images were acquired as part of a research study conducted under an abbreviated investigational device exemption and IRB-approved protocols. The red dashed box (A) and the yellow dashed box (B) in the widefield images correspond to approximate field of view of non-contact scanning (C, D, respectively). Optic nerve head is marked with red/blue asterisk and fovea with yellow. Right columns show the retinal views segmented at the RPE to highlight retinal vasculature in the top row and choroidal vasculature in the bottom row in a healthy eye (E and F, and the same as shown in the cross section) and an eye with retinal vascular disease (G, H). Red dashed circle denotes typical ROP Zone I region, the asterisk marks the fovea, and the red arrows point to the ampulla of the vortex veins.
Figure 3.
 
Representative widefield handheld OCT images from an investigational swept-source handheld OCT system (Theia Imaging, Durham, NC). The investigational device used to acquire these images has not been cleared by the FDA. Images were acquired as part of a research study conducted under an abbreviated investigational device exemption and IRB-approved protocols. The red dashed box (A) and the yellow dashed box (B) in the widefield images correspond to approximate field of view of non-contact scanning (C, D, respectively). Optic nerve head is marked with red/blue asterisk and fovea with yellow. Right columns show the retinal views segmented at the RPE to highlight retinal vasculature in the top row and choroidal vasculature in the bottom row in a healthy eye (E and F, and the same as shown in the cross section) and an eye with retinal vascular disease (G, H). Red dashed circle denotes typical ROP Zone I region, the asterisk marks the fovea, and the red arrows point to the ampulla of the vortex veins.
Figure 4.
 
Surgical 3D-OCT (top) with corresponding surgical microscopy (middle) combined to generate a single fused visualization (bottom) for enhanced guidance of surgical maneuvers. Subjects include human retinal surgery with a soft-tip cannula (column 1), finesse loop (column 2), and forceps (column 3).102 Column 4 is of a cornea and a partially collapsed anterior chamber (4).103
Figure 4.
 
Surgical 3D-OCT (top) with corresponding surgical microscopy (middle) combined to generate a single fused visualization (bottom) for enhanced guidance of surgical maneuvers. Subjects include human retinal surgery with a soft-tip cannula (column 1), finesse loop (column 2), and forceps (column 3).102 Column 4 is of a cornea and a partially collapsed anterior chamber (4).103
Figure 5.
 
Demonstration of quantitative measurement setup with MIOCT system used in wetlab studies (top row112) and example bleb image and segmentation in a human subject using this measurement procedure (bottom row110).
Figure 5.
 
Demonstration of quantitative measurement setup with MIOCT system used in wetlab studies (top row112) and example bleb image and segmentation in a human subject using this measurement procedure (bottom row110).
Figure 6.
 
Robotically aligned OCT (RAOCT) system design. (A) OCT scanner mounted on a collaborative robotic arm.140 (B) Optical system diagram of the OCT engine used in RAOCT that includes dynamic system components for real-time beam steering. (C) Top down view of RAOCT auto-aligning to a freestanding individual. (D) Face tracking cameras detecting and segmenting the target eye. (E) View from the pupil tracking cameras. Pupil center (magenta cross), calculated center of corneal curvature (yellow cross), and gaze angle (green arrow) are projected onto the images. (F) Registered and averaged foveal b-scan of a healthy freestanding individual.129
Figure 6.
 
Robotically aligned OCT (RAOCT) system design. (A) OCT scanner mounted on a collaborative robotic arm.140 (B) Optical system diagram of the OCT engine used in RAOCT that includes dynamic system components for real-time beam steering. (C) Top down view of RAOCT auto-aligning to a freestanding individual. (D) Face tracking cameras detecting and segmenting the target eye. (E) View from the pupil tracking cameras. Pupil center (magenta cross), calculated center of corneal curvature (yellow cross), and gaze angle (green arrow) are projected onto the images. (F) Registered and averaged foveal b-scan of a healthy freestanding individual.129
Figure 7.
 
Robotically aligned OCT (RAOCT) system with scanner mounted face tracking. The original RAOCT used fixed base face tracking with a single point of view; this meant the subject's face could only be seen when directly in front of the fixed tracking cameras. By mounting the face tracking cameras to the robot and scanner, the face tracking can “see” wherever the robot and scanner can reach as seen in the various face orientations in the image.121 (Second row: en face OCT, third row: B-scans, and bottom row: OCT volume 3D renders.)
Figure 7.
 
Robotically aligned OCT (RAOCT) system with scanner mounted face tracking. The original RAOCT used fixed base face tracking with a single point of view; this meant the subject's face could only be seen when directly in front of the fixed tracking cameras. By mounting the face tracking cameras to the robot and scanner, the face tracking can “see” wherever the robot and scanner can reach as seen in the various face orientations in the image.121 (Second row: en face OCT, third row: B-scans, and bottom row: OCT volume 3D renders.)
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