The ARMM System-Autonomous Steering of Magnetically-Actuated Catheters:Towards Endovascular Applications

Positioning conventional endovascular catheters is not without risk, and there is a multitude of complications that are associated with their use in manual surgical interventions. By utilizing surgical manipulators, the efficacy of remote-controlled catheters can be investigated in vivo. However, technical challenges, such as the duration of catheterizations, accurate positioning at target sites, and consistent imaging of these catheters using non-hazardous modalities, still exist. In this paper, we propose the integration of multiple sub-systems in order to extend the clinical feasibility of an autonomous surgical system designed to address these challenges. The system handles the full synchronization of co-operating manipulators that both actuate a clinical tool. The experiments within this study are conducted within a clinically-relevant workspace and inside a gelatinous phantom that represents a life-size human torso. A catheter is positioned using magnetic actuation and proportional-integral (PI) control in conjunction with real-time ultrasound images. Our results indicate an average error between the tracked catheter tip and target positions of 2:09 0:49 mm. The median procedure time to reach targets is 32:6 s. We expect that our system will provide a step towards collaborative manipulators employing mobile electromagnets, and possibly improve autonomous catheterization procedures within endovascular surgeries

Collaborative Surgical Robots:Optical Tracking During Endovascular Operations

Endovascular interventions usually require meticulous handling of surgical instruments and constant monitoring of the operating room workspace. To address these challenges, robotic- assisted technologies and tracking techniques are increasingly being developed. Specifically, the limited workspace and potential for a collision between the robot and surrounding dynamic obstacles are important aspects that need to be considered. This article presents a navigation system developed to assist clinicians with the magnetic actuation of endovascular catheters using multiple surgical robots. We demonstrate the actuation of a magnetic catheter in an experimental arterial testbed with dynamic obstacles. The motions and trajectory planning of two six degrees of freedom (6-DoF) robotic arms are established through passive markerguided motion planning. We achieve an overall 3D tracking accuracy of 2.3 ± 0.6 mm for experiments involving dynamic obstacles. We conclude that integrating multiple optical trackers with the online planning of two serial-link manipulators is useful to support the treatment of endovascular diseases and aid clinicians during interventions

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

3D vessel reconstruction based on intra-operative intravascular ultrasound for robotic autonomous catheter navigation

In recent years, robotic technology has improved instrument navigation precision and accuracy, and helped decrease the complexity of minimally invasive surgery. Still, the inherent restricted access to the anatomy of the patients severely complicates many procedures. Interventionists frequently depend on external technologies for visual guidance, usually employing ionizing radiation, due to the limited view upon the surgical scene. In the case of endovascular procedures, fluoroscopy is the common imaging modality used for visualization. This modality is based on X-rays and only offers a two- dimensional (2D) view of the surgical scene. Having a real-time, up-to-date understanding of the surrounding environment of the surgical instruments within the vasculature and not depending on using ionizing radiation would not only be very helpful for interventionists, but also paramount for the navigation of an intraluminal robot. Therefore, the aim of this thesis is to develop an algorithm able to do an intra-operative and real-time three-dimensional (3D) vessel reconstruction. The algorithm is divided into two parts: the reconstruction and the merging. In the first one, it is obtained the 3D vessel reconstruction of a section of the vessel and in the second one, the different sections of 3D vessel reconstruction are combined. A real vessel mesh is used to calculate the fitting errors of the reconstructed vessel which are very smallEn los últimos años, la tecnología robótica ha mejorado la precisión y fiabilidad de la navegación de instrumentos y ha ayudado a disminuir la complejidad de la cirugía mínimamente invasiva. Aún así, el acceso restringido inherente a la anatomía de los pacientes complica gravemente muchos procedimientos. Los intervencionistas dependen con frecuencia de tecnologías externas para la guía visual, generalmente empleando radiación ionizante, debido a la visión limitada de la escena quirúrgica. En el caso de los procedimientos endovasculares, la fluoroscopia es la modalidad de imagen común utilizada para la visualización. Esta modalidad se basa en rayos X y solo ofrece una vista bidimensional (2D) de la escena quirúrgica. Poder saber en tiempo real y de forma actualizada como es el entorno alrededor de los instrumentos quirúrgicos que se encuentran dentro de la vasculatura y no depender del uso de radiación ionizante no solo sería muy útil para los intervencionistas, sino también fundamental para la navegación de un robot intraluminal. Por lo tanto, el objetivo de esta tesis es desarrollar un algoritmo capaz de realizar una reconstrucción tridimensional (3D) del vaso sanguíneo de forma intraoperatoria y en tiempo real. El algoritmo se divide en dos partes: la reconstrucción y la unión. En la primera se obtiene la reconstrucción 3D de una sección del vaso sanguíneo y en el segundo se combinan las diferentes secciones obtenidas de vasos sanguíneos reconstruidos en 3D. Se utiliza una malla de un vaso sanguíneo real para calcular los errores de ajuste del vaso sanguíneo reconstruido, son errores muy pequeñosEn els últims anys, la tecnologia robòtica ha millorat la precisió i la fiabilitat de la navegació dels instruments i ha ajudat a disminuir la complexitat de la cirurgia mínimament invasiva. Tot i així, l'accés restringit inherent a l'anatomia dels pacients complica greument molts procediments. Els intervencionistes sovint depenen de tecnologies externes per a la guia visual, normalment emprant radiacions ionitzants, a causa de la visió limitada de l'escena quirúrgica. En el cas dels procediments endovasculars, la fluoroscòpia és la modalitat d'imatge comuna utilitzada per a la visualització. Aquesta modalitat es basa en raigs X i només ofereix una visió bidimensional (2D) de l'escena quirúrgica. Poder saber en temps real i de forma actualitzada com és l'entorn al voltant dels instruments quirúrgics que es troben dins de la vasculatura i no depèn de l'ús de radiació ionitzant no només seria molt útil per als intervencionistes, sinó també fonamental per a la navegació d'un robot intraluminal. Per tant, l'objectiu d'aquesta tesi és desenvolupar un algorisme capaç de fer una reconstrucció tridimensional (3D) del vas sanguini de forma intraoperatòria i en temps real. L'algorisme es divideix en dues parts: la reconstrucció i la fusió. En la primera s'obté la reconstrucció en 3D d'una secció del vas sanguini i en la segona, es combinen les diferents seccions obtingudes de vasos sanguinis reconstruïts en 3D. S'utilitza una malla d’un vas sanguini real per calcular els errors d'ajust del vas sanguini reconstruït, els errors son molt petit

A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery

Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training and assessment of new surgeons, that deviates from the “see one, do one, teach one” model introduced by William Halsted, so that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar, IEEXplore and known journals using the keywords, “endovascular surgery”, “surgical skills”, “endovascular skills”, “surgical training endovascular” and “catheter skills”. Focusing explicitly on endovascular surgical skills, we group related works into three categories based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated surgical assessment solutions

Position-based dynamics simulator of vessel deformations for path planning in robotic endovascular catheterization

A major challenge during autonomous navigation in endovascular interventions is the complexity of operating in a deformable but constrained workspace with an instrument. Simulation of deformations for it can provide a cost-effective training platform for path planning. Aim of this study is to develop a realistic, auto-adaptive, and visually plausible simulator to predict vessels’ global deformation induced by the robotic catheter’s contact and cyclic heartbeat motion. Based on a Position-based Dynamics (PBD) approach for vessel modeling, Particle Swarm Optimization (PSO) algorithm is employed for an auto-adaptive calibration of PBD deformation parameters and of the vessels movement due to a heartbeat. In-vitro experiments were conducted and compared with in-silico results. The end-user evaluation results were reported through quantitative performance metrics and a 5-Point Likert Scale questionnaire. Compared with literature, this simulator has an error of 0.23±0.13% for deformation and 0.30±0.85mm for the aortic root displacement. In-vitro experiments show an error of 1.35±1.38mm for deformation prediction. The end-user evaluation results show that novices are more accustomed to using joystick controllers, and cardiologists are more satisfied with the visual authenticity. The real-time and accurate performance of the simulator make this framework suitable for creating a dynamic environment for autonomous navigation of robotic catheters