Microsurgery robots: addressing the needs of high-precision surgical interventions

Robots can help surgeons perform better quality operations, leading to reductions in the hospitalisation time of patients and in the impact of surgery on their postoperative quality of life

Optical Coherence Tomography Distal Sensor Based Handheld Microsurgical Tools

Microsurgery is typically differentiated from a general surgery in that it requires a precise sub-millimeter manipulation that could only be achievable under optical magnification. For instance, microsurgeons use surgical microscopes to view surgical sites and train themselves several years to acquire surgical skills to perform the delicate procedures. However, such microsurgical approach imposes considerable physical stress and mental fatigue on the surgeons and these could be sources for surgical risks and complications. For these reasons, a variety of robotic based surgical guidance methods have been developed and studied with the hope of providing safer and more precise microsurgery. These robotic arm based systems have been developed to provide precise tool movement and to remove physiological hand tremor, which is one of the main limiting factors that prevents precise tool manipulation. In another approaches use simpler system that adds robotic functions to existing handheld surgical tools. It is a hybrid system that incorporates the advantages of conventional manual system and robot-assist system. The advantages of such hybrid handheld systems include portability, disposability, and elimination of the large robotic-assist systems in complex surgical environment. The most critical benefit of the hybrid handheld system is its ease of use since it allows surgeons to manipulate tools mostly using their hand. However due to the imprecise nature of tool control using hands, tool tracking is more critical in handheld microsurgical tool systems than that of robotic arm systems. In general, the accuracy of the tool control is largely determined by the resolution of the sensors and the actuators. Therefore, it is essential to develop a real-time high resolution sensor in order to develop a practical microsurgical tools. For this reason, a novel intuitive targeting and tracking scheme that utilizes a common-path swept source optical coherence tomography (CP-SSOCT) distal sensor was developed integrated with handheld microsurgical tools. To achieve micron-order precision control, a reliable and accurate OCT distal sensing method was developed. The method uses a prediction algorithm is necessary to compensate for the system delay associated with the computational, mechanical and electronic latencies. Due to the multi-layered structure of retina, it was also necessary to develop effective surface detection methods rather than simple peak detection. The OCT distal sensor was integrated into handheld motion-guided micro-forceps system for highly accurate depth controlled epiretinal membranectomy. A touch sensor and two motors were used in the forceps design to minimize the motion artifact induced by squeezing, and to independently control the depth guidance of the tool-tip and the grasping action. We also built a depth guided micro-injector system that enables micro-injection with precise injection depth control. For these applications, a smart motion monitoring and a guiding algorithm were developed to provide precise and intuitive freehand control. Finally, phantom and ex-vivo bovine eye experiments were performed to evaluate the performance of the proposed OCT distal sensor and validate the effectiveness of the depth-guided micro-forceps and micro-injector over the freehand performance

Development of a Novel Handheld Device for Active Compensation of Physiological Tremor

In microsurgery, the human hand imposes certain limitations in accurately positioning the tip of a device such as scalpel. Any errors in the motion of the hand make microsurgical procedures difficult and involuntary motions such as hand tremors can make some procedures significantly difficult to perform. This is particularly true in the case of vitreoretinal microsurgery. The most familiar source of involuntary motion is physiological tremor. Real-time compensation of tremor is, therefore, necessary to assist surgeons to precisely position and manipulate the tool-tip to accurately perform a microsurgery. In this thesis, a novel handheld device (AID) is described for compensation of physiological tremor in the hand. MEMS-based accelerometers and gyroscopes have been used for sensing the motion of the hand in six degrees of freedom (DOF). An augmented state complementary Kalman filter is used to calculate 2 DOF orientation. An adaptive filtering algorithm, band-limited Multiple Fourier linear combiner (BMFLC), is used to calculate the tremor component in the hand in real-time. Ionic Polymer Metallic Composites (IPMCs) have been used as actuators for deflecting the tool-tip to compensate for the tremor

Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing

In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT introduces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans, a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems

Dynamic Active Constraints for Surgical Robots using Vector Field Inequalities

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still underrepresented in procedures with constrained workspaces, such as deep brain neurosurgery and endonasal surgery. In these procedures, surgeons have restricted vision to areas near the surgical tooltips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings. In this work, our vector-field-inequalities method is extended to provide dynamic active-constraints to any number of robots and moving objects sharing the same workspace. The method is evaluated with experiments and simulations in which robot tools have to avoid collisions autonomously and in real-time, in a constrained endonasal surgical environment. Simulations show that with our method the combined trajectory error of two robotic systems is optimal. Experiments using a real robotic system show that the method can autonomously prevent collisions between the moving robots themselves and between the robots and the environment. Moreover, the framework is also successfully verified under teleoperation with tool-tissue interactions.Comment: Accepted on T-RO 2019, 19 Page

Augmentation Of Human Skill In Microsurgery

Surgeons performing highly skilled microsurgery tasks can benefit from information and manual assistance to overcome technological and physiological limitations to make surgery safer, efficient, and more successful. Vitreoretinal surgery is particularly difficult due to inherent micro-scale and fragility of human eye anatomy. Additionally, surgeons are challenged by physiological hand tremor, poor visualization, lack of force sensing, and significant cognitive load while executing high-risk procedures inside the eye, such as epiretinal membrane peeling. This dissertation presents the architecture and the design principles for a surgical augmentation environment which is used to develop innovative functionality to address the fundamental limitations in vitreoretinal surgery. It is an inherently information driven modular system incorporating robotics, sensors, and multimedia components. The integrated nature of the system is leveraged to create intuitive and relevant human-machine interfaces and generate a particular system behavior to provide active physical assistance and present relevant sensory information to the surgeon. These include basic manipulation assistance, audio-visual and haptic feedback, intraoperative imaging and force sensing. The resulting functionality, and the proposed architecture and design methods generalize to other microsurgical procedures. The system's performance is demonstrated and evaluated using phantoms and in vivo experiments