Demonstration of Subretinal Injection Using Common-Path Swept Source OCT Guided Microinjector

Autonomous Needle Navigation in Subretinal Injections via iOCT

Subretinal injection is an effective method for direct delivery of therapeutic agents to treat prevalent subretinal diseases. Among the challenges for surgeons are physiological hand tremor, difficulty resolving single-micron scale depth perception, and lack of tactile feedback. The recent introduction of intraoperative Optical Coherence Tomography (iOCT) enables precise depth information during subretinal surgery. However, even when relying on iOCT, achieving the required micron-scale precision remains a significant surgical challenge. This work presents a robot-assisted workflow for high-precision autonomous needle navigation for subretinal injection. The workflow includes online registration between robot and iOCT coordinates; tool-tip localization in iOCT coordinates using a Convolutional Neural Network (CNN); and tool-tip planning and tracking system using real-time Model Predictive Control (MPC). The proposed workflow is validated using a silicone eye phantom and ex vivo porcine eyes. The experimental results demonstrate that the mean error to reach the user-defined target and the mean procedure duration are within an acceptable precision range. The proposed workflow achieves a 100% success rate for subretinal injection, while maintaining scleral forces at the scleral insertion point below 15mN throughout the navigation procedures.

3D nanoprinted catadioptric fiber sensor for dual-axis distance measurement during vitrectomy

Retinal damage is a common intraoperative complication during vitrectomy, caused by a complex interplay between the suction of the vitrectome, the cut- and aspiration rate, and the distance of the instrument to the retina. To control this last factor, we developed two miniaturized fiber-optic distance sensors based on low-coherence interferometry for direct integration into the vitrectome. Both sensors have a diameter of 250 µm, which makes them compatible with a 25G vitrectome. The first sensor measures distance in the lateral direction. The second sensor is capable of simultaneously measuring distance in both the lateral and the axial direction. Axial and lateral directions correspond to the direction of the cutter port of the vitrectome and the direction along the vitrectome's shaft, respectively. In both sensors, a free-form mirror deflects and focuses the beam in the lateral direction. In the dual-axis distance sensor, an additional lens is integrated into the free-form mirror for distance measurement in the axial direction. The beam-shaping micro-optics at the tip of the sensor fibers were fabricated through two-photon polymerization and are selectively gold coated for increased reflectivity of the mirror. Distance measurements were successfully demonstrated in artificial samples and in ex vivo pig eyes with a back-end that uses a current-tuned VCSEL as a swept-source. We experimentally demonstrate that the complete sensor system can attain a S N R max of up to 80 dB. The small dimensions of the developed sensors make them a potential solution for various other medical applications.

Microsurgery Robots: Applications, Design, and Development

Microsurgical techniques have been widely utilized in various surgical specialties, such as ophthalmology, neurosurgery, and otolaryngology, which require intricate and precise surgical tool manipulation on a small scale. In microsurgery, operations on delicate vessels or tissues require high standards in surgeons' skills. This exceptionally high requirement in skills leads to a steep learning curve and lengthy training before the surgeons can perform microsurgical procedures with quality outcomes. The microsurgery robot (MSR), which can improve surgeons' operation skills through various functions, has received extensive research attention in the past three decades. There have been many review papers summarizing the research on MSR for specific surgical specialties. However, an in-depth review of the relevant technologies used in MSR systems is limited in the literature. This review details the technical challenges in microsurgery, and systematically summarizes the key technologies in MSR with a developmental perspective from the basic structural mechanism design, to the perception and human-machine interaction methods, and further to the ability in achieving a certain level of autonomy. By presenting and comparing the methods and technologies in this cutting-edge research, this paper aims to provide readers with a comprehensive understanding of the current state of MSR research and identify potential directions for future development in MSR.

Autonomous Needle Navigation in Retinal Microsurgery: Evaluation in ex vivo Porcine Eyes

Important challenges in retinal microsurgery include prolonged operating time, inadequate force feedback, and poor depth perception due to a constrained top-down view of the surgery. The introduction of robot-assisted technology could potentially deal with such challenges and improve the surgeon's performance. Motivated by such challenges, this work develops a strategy for autonomous needle navigation in retinal microsurgery aiming to achieve precise manipulation, reduced end-to-end surgery time, and enhanced safety. This is accomplished through real-time geometry estimation and chance-constrained Model Predictive Control (MPC) resulting in high positional accuracy while keeping scleral forces within a safe level. The robotic system is validated using both open-sky and intact (with lens and partial vitreous removal) ex vivo porcine eyes. The experimental results demonstrate that the generation of safe control trajectories is robust to small motions associated with head drift. The mean navigation time and scleral force for MPC navigation experiments are 7.208 s and 11.97 mN, which can be considered efficient and well within acceptable safe limits. The resulting mean errors along lateral directions of the retina are below 0.06 mm, which is below the typical hand tremor amplitude in retinal microsurgery.

Convolutional neural network-based common-path optical coherence tomography A-scan boundary-tracking training and validation using a parallel Monte Carlo synthetic dataset

We present a parallel Monte Carlo (MC) simulation platform for rapidly generating synthetic common-path optical coherence tomography (CP-OCT) A-scan image dataset for image-guided needle insertion. The computation time of the method has been evaluated on different configurations and 100000 A-scan images are generated based on 50 different eye models. The synthetic dataset is used to train an end-to-end convolutional neural network (Ascan-Net) to localize the Descemet's membrane (DM) during the needle insertion. The trained Ascan-Net has been tested on the A-scan images collected from the ex-vivo human and porcine cornea as well as simulated data and shows improved tracking accuracy compared to the result by using the Canny-edge detector.

Robotic Assistance for Intraocular Microsurgery: Challenges and Perspectives

Intraocular surgery, one of the most challenging discipline of microsurgery, requires sensory and motor skills at the limits of human physiological capabilities combined with tremendously difficult requirements for accuracy and steadiness. Nowadays, robotics combined with advanced imaging has opened conspicuous and significant directions in advancing the field of intraocular microsurgery. Having patient treatment with greater safety and efficiency as the final goal, similar to other medical applications, robotics has a real potential to fundamentally change microsurgery by combining human strengths with computer and sensor-based technology in an information-driven environment. Still in its early stages, robotic assistance for intraocular microsurgery has been accepted with precaution in the operating room and successfully tested in a limited number of clinical trials. However, owing to its demonstrated capabilities including hand tremor reduction, haptic feedback, steadiness, enhanced dexterity, micrometer-scale accuracy, and others, microsurgery robotics has evolved as a very promising trend in advancing retinal surgery. This paper will analyze the advances in retinal robotic microsurgery, its current drawbacks and limitations, as well as the possible new directions to expand retinal microsurgery to techniques currently beyond human boundaries or infeasible without robotics.

Development and ex-vivo validation of 36G polyimide cannulas integrating a guiding miniaturized OCT probe for robotic assisted subretinal injections

We introduced and validated a method to encase guiding optical coherence tomography (OCT) probes into clinically relevant 36G polyimide subretinal injection (SI) cannulas. Modified SI cannulas presented consistent flow capacity and tolerated the typical mechanical stress encountered in clinical use without significant loss of sensitivity. We also developed an approach that uses a micromanipulator, modified SI cannulas, and an intuitive graphical user interface to enable precise SI. We tested the system using ex-vivo porcine eyes and we found a high SI success ratio 95.0% (95% CI: 83.1-99.4). We also found that 75% of the injected volume ends up at the subretinal space. Finally, we showed that this approach can be applied to transform commercial 40G SI cannulas to guided cannulas. The modified cannulas and guiding approach can enable precise and reproducible SI of novel gene and cell therapies targeting retinal diseases.

A Smart Vitrector Equipped by a Fiber-Based OCT Sensor Mitigates Intentional Attempts at Creating Iatrogenic Retinal Breaks During Vitrectomy in Pigs

Purpose

The occurrence of iatrogenic retinal breaks (RB) in pars plana vitrectomy (PPV) is a complication that compromises the overall efficacy of the surgery. A subset of iatrogenic RB occurs when the retina (rather than the vitreous gel) is cut accidentally by the vitrector. We developed a smart vitrector that can detect in real-time potential iatrogenic RB and activate promptly a PPV machine response to prevent them.

Methods

We fabricated the smart vitrectors by attaching a miniaturized fiber-based OCT sensor on commercial vitrectors (25G). The system's response time to an iatrogenic RB onset was measured and compared to the literature reported physiologically limited response time of the average surgeon. Two surgeons validated its ability to prevent simulated iatrogenic RB by performing PPV in pigs. Note that the system is meant to control the PPV machine and requires no visual or audio signal interpretation by the surgeons.

Results

We found that the response time of the system (28.9 ± 6.5 ms) is 11 times shorter compared to the literature reported physiologically limited reaction time of the average surgeon (P < 0.0001). Ex vivo validation (porcine eyes) showed that the system prevents 78.95% (15/19) (95% confidence interval [CI] 54.43–93.95) of intentional attempts at creating RB, whereas in vivo validation showed that the system, prevents 55.68% (30/54) (95% CI 41.40–69.08), and prevents or mitigates 70.37% (38/54) (95% CI 56.39–82.02) of such attempts. A subset of failures was classified as “early stop” (i.e., false positive), having a prevalence of 5.26% (1/19) in ex vivo tests and 24.07% (13/54) in in vivo tests.

Conclusions

Our results indicate the smart vitrector can prevent iatrogenic RB by providing seamless intraoperative feedback to the PPV machine. Importantly, the use of the smart vitrector requires no modifications of the established PPV procedure. It can mitigate a significant proportion of iatrogenic RB and thus improve the overall efficacy of the surgery.

Translational Relevance

Potential clinical adoption of the smart vitrector can reduce the incidence of iatrogenic RB in PPV and thus increase the therapeutic outcome of the surgery.

CNN-based CP-OCT sensor integrated with a subretinal injector for retinal boundary tracking and injection guidance

SIGNIFICANCE

Subretinal injection is an effective way of delivering transplant genes and cells to treat many degenerative retinal diseases. However, the technique requires high-dexterity and microscale precision of experienced surgeons, who have to overcome the physiological hand tremor and limited visualization of the subretinal space.

AIM

To automatically guide the axial motion of microsurgical tools (i.e., a subretinal injector) with microscale precision in real time using a fiber-optic common-path swept-source optical coherence tomography distal sensor.

APPROACH

We propose, implement, and study real-time retinal boundary tracking of A-scan optical coherence tomography (OCT) images using a convolutional neural network (CNN) for automatic depth targeting of a selected retinal boundary for accurate subretinal injection guidance. A simplified 1D U-net is used for the retinal layer segmentation on A-scan OCT images. A Kalman filter, combining retinal boundary position measurement by CNN and velocity measurement by cross correlation between consecutive A-scan images, is applied to optimally estimate the retinal boundary position. Unwanted axial motions of the surgical tools are compensated by a piezoelectric linear motor based on the retinal boundary tracking.

RESULTS

CNN-based segmentation on A-scan OCT images achieves the mean unsigned error (MUE) of ∼3  pixels (8.1  μm) using an ex vivo bovine retina model. GPU parallel computing allows real-time inference (∼2  ms) and thus real-time retinal boundary tracking. Involuntary tremors, which include low-frequency draft in hundreds of micrometers and physiological tremors in tens of micrometers, are compensated effectively. The standard deviations of photoreceptor (PR) and choroid (CH) boundary positions get as low as 10.8  μm when the depth targeting is activated.

CONCLUSIONS

A CNN-based common-path OCT distal sensor successfully tracks retinal boundaries, especially the PR/CH boundary for subretinal injection, and automatically guides the tooltip's axial position in real time. The microscale depth targeting accuracy of our system shows its promising possibility for clinical application.

Oblique injection depth correction by a two parallel OCT sensor guided handheld SMART injector

We present a SMART injector with two parallel common-path optical coherence tomography fibers to enable angle measurements and injection depth corrections for oblique subretinal injection. The two optical fibers are attached to opposite sides of a 33 G needle with known offsets and designed to pass through a 23 G trocar that has an inner diameter of 0.65 mm. By attaching a SMART system to a rotational stage, the measured angles are calibrated for minimal error from reference angles. A commercial eye model was used to evaluate the control performance, and injection experiments were performed on a phantom made of agarose gel and a porcine eye.

Demonstration of Optical Coherence Tomography Guided Big Bubble Technique for Deep Anterior Lamellar Keratoplasty (DALK)

Deep anterior lamellar keratoplasty (DALK) is a highly challenging procedure for cornea transplant that involves removing the corneal layers above Descemet’s membrane (DM). This is achieved by a “big bubble” technique where a needle is inserted into the stroma of the cornea down to DM and the injection of either air or liquid. DALK has important advantages over penetrating keratoplasty (PK) including lower rejection rate, less endothelial cell loss, and increased graft survival. In this paper, we successfully designed and evaluated the optical coherence tomography (OCT) distal sensor integrated needle for a precise big bubble technique. We successfully used this sensor for micro-control of a robotic DALK device termed AUTO-DALK for autonomous big bubble needle insertion. The OCT distal sensor was integrated inside a 25-gauge needle, which was used for pneumo-dissection. The AUTO-DALK device is built on a manual trephine platform which includes a vacuum ring to fix the device on the eye and add a needle driver at an angle of 60 degrees from vertical. During the test on five porcine eyes with a target depth of 90%, the measured insertion depth as a percentage of cornea thickness for the AUTO-DALK device was 90.05%±2.33% without any perforation compared to 79.16%±5.68% for unassisted free-hand insertion and 86.20%±5.31% for assisted free-hand insertion. The result showed a higher precision and consistency of the needle placement with AUTO-DALK, which could lead to better visual outcomes and fewer complications.

Analysis and evaluation of BC-mode OCT image visualization for microsurgery guidance

Optical coherence tomography (OCT) has been gaining acceptance in image-guided microsurgery as a noninvasive imaging technique. However, when using B-mode OCT imaging, it is difficult to continuously keep the surgical tool in the imaging field, and the image of the tissue beneath the tool is corrupted by shadow effects. The alternative using C-mode OCT imaging is either too slow in imaging speed when operating in a high-resolution mode, or provides a poor image resolution in a high-speed mode, with the sweep rate less than one million hertz. Moreover, the 3-dimensional rendering of C-mode OCT image makes it difficult to visualize the tissue structure and track the surgical tool beneath the tissue surface. To solve these problems, we propose a BC-mode OCT image visualization method. This method uses a sparse C-scanning scheme, which provides a set of high-resolution B-mode OCT images at sparsely spaced cross sections. The final BC-mode OCT image is obtained by averaging the image set, with inter frame variance processing to enhance the signal of the surgical tool and tissue layers. The performance of BC-mode OCT images, such as image resolution, signal to noise ratio (SNR), imaging speed, and surgical tool tracking accuracy, is analyzed theoretically and verified experimentally. The feasibility of the proposed method is evaluated by guiding the insertion of a 30-gauge needle into the cornea of an ex-vivo human eye freehand. The results show that this provides better visualization of both the surgical tool and the tissue structure than the conventional B- or C- mode OCT image.

Dual-Stiffness Force-Sensing Cannulation Tool for Retinal Microsurgery

Retinal vein cannulation is a promising treatment for retinal vein occlusion that involves the injection of an anticoagulant directly into the occluded vein to dissolve the blockage. However, excessive forces applied by the injection tool during the procedure, at either the scleral incision or injection site, can result in injury to the eye. Furthermore, the force required to puncture retinal veins (around 10 mN) is well below human sensing ability and an order of magnitude smaller than those that can be safely applied at the sclera (around 100 mN). Detection and management of tool-to-tissue forces on these different scales are some of the most challenging aspects of the cannulation procedure. This work describes the development of a sensorized cannulation tool capable of detecting both tool-to-vein puncture forces and tool-to-sclera contact forces. By combining two materials, nitinol alloy for the tool tip and stainless steel for the tool shaft, to achieve dual stiffness, the tool possesses a flexible tip to capture small vein puncture forces and a stiffer shaft to maintain straightness during use. Three segments of fiber Bragg grating sensors are calibrated to measure the transverse forces at both the tool tip and sclerotomy, as well as to determine the tool insertion depth within the eye. The results of the validation experiments show that the root mean square error of the measurements for the force at the tip, the force at the sclerotomy, and the tool position are 0.70 mN, 1.59 mN, and 0.69 mm, respectively.