Optical coherence tomography (OCT) has revolutionized diagnostics in ophthalmology. However, it requires pa-tient cooperation to fixate on multiple targets and stabilize their head utilizing both chin and forehead rests. Patient cooperation is particularly important for image montaging, where patients are asked to fixate on multiple targets to sequentially image different regions of interest on the retina. These individual volumes are then combined into a single large field of view volume. To automate the OCT image acquisition process, we previously developed a robot-mounted OCT scanner that auto-aligned with the retinal region of interest while compensating for subject motion. We utilized this system to self-align at multiple regions-of-interest and acquire stabilized volumes. We then montaged volume projections into a larger field of view image. The system tracked the 3D location of the subject's eye as well as their gaze orientation using a combination of face and pupil tracking cameras. We demonstrated automated OCT acquisition for live image montaging on free-standing subjects and evaluated the consistency of our live volumetric mapping on human subject data. Our results demonstrate that the system not only stabilized images, but also provided sufficient control of region-of-interest specific alignment to automatically acquire and montage OCT images, synthetically increasing the system's field of view by$20^{\circ}$.

We present a feature-free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware. Our end-to-end, pixel-intensity-based approach jointly registers and stitches all the images by estimating a coaligned height map, which acts as a pixel-wise radial deformation field that orthorectifies each camera image to allow plane-plus-parallax registration. The height maps themselves are reparameterized as the output of an untrained encoder-decoder convolutional neural network (CNN) with the raw camera images as the input, which effectively removes many reconstruction artifacts. Our method also jointly estimates both the camera’s dynamic 6D pose and its distortion using a nonparametric model, the latter of which is especially important in mesoscopic applications when using cameras not designed for imaging at short working distances, such as smartphone cameras. We also propose strategies for reducing computation time and memory, applicable to other multi-frame registration problems. Finally, we demonstrate our method using sequences of multi-megapixel images captured by an un-stabilized smartphone on a variety of samples (e.g., painting brushstrokes, circuit board, seeds).

Robotic automation has the potential to assist human surgeons in performing suturing tasks in microsurgery, and in order to do so a robot must be able to guide a needle with sub-millimeter precision through soft tissue. This paper presents a robotic suturing system that uses 3D optical coherence tomography (OCT) system for imaging feedback. Calibration of the robot-OCT and robot-needle transforms, wound detection, keypoint identification, and path planning are all performed automatically. The calibration method handles pose uncertainty when the needle is grasped using a variant of iterative closest points. The path planner uses the identified wound shape to calculate needle entry and exit points to yield an evenly-matched wound shape after closure. Experiments on tissue phantoms and animal tissue demonstrate that the system can pass a suture needle through wounds with 0.200 mm overall accuracy in achieving the planned entry and exit points, and over 20× more precise than prior autonomous suturing robots.

Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. We demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This work shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT guided robotic ophthalmic surgery.

Optical coherence tomography (OCT) has found great success in ophthalmology where it plays a key role in screening and diagnostics. Clinical ophthalmic OCT systems are typically deployed as tabletop instruments that require chinrest stabilization and trained ophthalmic photographers to operate. These requirements preclude OCT diagnostics in bedbound or unconscious patients who cannot use a chinrest, and restrict OCT screening to ophthalmology offices. We present a robotically-aligned OCT scanner capable of automatic eye imaging without chinrests. The scanner features eye tracking from fixed-base RGB-D cameras for coarse and stereo pupil cameras for fine alignment, as well as galvanometer aiming for fast lateral tracking, reference arm adjustment for fast axial tracking, and a commercial robot arm for slow lateral and axial tracking. We demonstrate the system's performance autonomously aligning with stationary eyes, pursuing moving eyes, and tracking eyes undergoing physiologic motion. The system demonstrates sub-millimeter eye tracking accuracy, 12 μm lateral pupil tracking accuracy, 83.2 ms stabilization time following step disturbance, and 9.7 Hz tracking bandwidth.

Deep anterior lamellar keratoplasty (DALK) is a promising technique for corneal transplantation that avoids the chronic immunosuppression comorbidities and graft rejection risk associated with penetrating keratoplasty (PKP), the standard procedure. In DALK, surgeons must insert a needle 90% through the 500 μm cornea without penetrating its underlying membrane. This pushes surgeons to their manipulation and visualization limits such that 59% of DALK attempts fail due to corneal perforation or inadequate needle depth. We propose a robot-assisted solution to jointly solve the manipulation and visualization challenges using a cooperatively-controlled, precise robot arm and live optical coherence tomography (OCT) imaging, respectively. Our system features an interface handle, with which the surgeon and robot cooperatively hold the tool, and a posterior corneal boundary virtual fixture driven by real-time OCT segmentation. A study in which three operators performed DALK needle insertions manually and cooperatively in ex vivo human corneas demonstrated an 84% improvement in perforation-free needle depth without an increased perforation rate.

Optical coherence tomography (OCT) has revolutionized diagnosis and prognosis of ophthalmic diseases by visualization and measurement of retinal layers. To speed up the quantitative analysis of disease biomarkers, an increasing number of automatic segmentation algorithms have been proposed to estimate the boundary locations of retinal layers. While the performance of these algorithms has significantly improved in recent years, a critical question to ask is how far we are from a theoretical limit to OCT segmentation performance. In this paper, we present the Cramèr-Rao lower bounds (CRLBs) for the problem of OCT layer segmentation. In deriving the CRLBs, we address the important problem of defining statistical models that best represent the intensity distribution in each layer of the retina. Additionally, we calculate the bounds under an optimal affine bias, reflecting the use of prior knowledge in many segmentation algorithms. Experiments using in vivo images of human retina from a commercial spectral domain OCT system are presented, showing potential for improvement of automated segmentation accuracy. Our general mathematical model can be easily adapted for virtually any OCT system. Furthermore, the statistical models of signal and noise developed in this paper can be utilized for the future improvements of OCT image denoising, reconstruction, and many other applications.

Intraoperative 3D imaging has great potential for enhancing surgical visualization. This is especially so in ophthalmic surgery where live volumetric imaging from optical coherence tomography systems recently incorporated into surgical microscopes has freed surgeons from the otherwise universal top-down viewpoint. New viewpoints, however, disorient surgeons when directions of their hand motions and viewed tool motions do not align. We propose introducing a robotic surgery system to decouple surgeons' hands from their tools and ensure that viewed tool motions align in arbitrary viewpoints. We present a framework entitled Arbitrary Viewpoint Robotic Manipulation (AVRM) which governs how hand and tool motions should interact to minimize disorientation and thereby enable operations from desirable but previously untenable viewpoints. A crossover study in which 20 subjects completed mock surgical scenarios with an AVRM testbed system demonstrated that arbitrary viewpoints do not improve task performance unless automatic hand-tool misalignment correction is provided. When provided together with arbitrary viewpoints, automatic hand-tool misalignment correction reduces task completion time by 50% on average compared to a fixed top-down viewpoint.

We present a novel general-purpose compression method for tomographic images, termed 3D adaptive sparse representation based compression (3D-ASRC). In this paper, we focus on applications of 3D-ASRC for the compression of ophthalmic 3D optical coherence tomography (OCT) images. The 3D-ASRC algorithm exploits correlations among adjacent OCT images to improve compression performance, yet is sensitive to preserving their differences. Due to the inherent denoising mechanism of the sparsity based 3D-ASRC, the quality of the compressed images are often better than the raw images they are based on. Experiments on clinical-grade retinal OCT images demonstrate the superiority of the proposed 3D-ASRC over other well-known compression methods.

In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.