Modeling and Control of Steerable Ablation Catheters

Catheters are long, flexible tubes that are extensively used in vascular and cardiac interventions, e.g., cardiac ablation, coronary angiography and mitral valve annuloplasty. Catheter-based cardiac ablation is a well-accepted treatment for atrial fibrillation, a common type of cardiac arrhythmia. During this procedure, a steerable ablation catheter is guided through the vasculature to the left atrium to correct the signal pathways inside the heart and restore normal heart rhythm. The outcome of the ablation procedure depends mainly on the correct positioning of the catheter tip at the target location inside the heart and also on maintaining a consistent contact between the catheter tip and cardiac tissue. In the presence of cardiac and respiratory motions, achieving these goals during the ablation procedure is very challenging without proper 3D visualization, dexterous control of the flexible catheter and an estimate of the catheter tip/tissue contact force. This research project provides the required basis for developing a robotics-assisted catheter manipulation system with contact force control for use in cardiac ablation procedures. The behavior of the catheter is studied in free space as well in contact with the environment to develop mathematical models of the catheter tip that are well suited for developing control systems. The validity of the proposed modeling approaches and the performance of the suggested control techniques are evaluated experimentally. As the first step, the static force-deflection relationship for ablation catheters is described with a large-deflection beam model and an optimized pseudo-rigid-body 3R model. The proposed static model is then used in developing a control system for controlling the contact force when the catheter tip is interacting with a static environment. Our studies also showed that it is possible to estimate the tip/tissue contact force by analyzing the shape of the catheter without installing a force sensor on the catheter. During cardiac ablation, the catheter tip is in contact with a relatively fast moving environment (cardiac tissue). Robotic manipulation of the catheter has the potential to improve the quality of contact between the catheter tip and cardiac tissue. To this end, the frequency response of the catheter is investigated and a control technique is proposed to compensate for the cardiac motion and to maintain a constant tip/tissue contact force. Our study on developing a motion compensated robotics-assisted catheter manipulation system suggests that redesigning the actuation mechanism of current ablation catheters would provide a major improvement in using these catheters in robotics-assisted cardiac ablation procedures

Robotic catheter cardiac ablation combining ultrasound guidance and force control

Cardiac catheters allow physicians to access the inside of the heart and perform therapeutic interventions without stopping the heart or opening the chest. However, conventional manual and actuated cardiac catheters are currently unable to precisely track and manipulate the intracardiac tissue structures because of the fast tissue motion and potential for applying damaging forces. This paper addresses these challenges by proposing and implementing a robotic catheter system that uses 3D ultrasound image guidance and force control to enable constant contact with a moving target surface in order to perform interventional procedures, such as intracardiac tissue ablation. The robotic catheter system, consisting of a catheter module, ablation and force sensing end effector, drive system, and image-guidance and control system, was commanded to apply a constant force against a moving target using a position-modulated force control method. The control system uses a combination of position tracking, force feedback, and friction and backlash compensation to achieve accurate and safe catheter–tissue interactions. The catheter was able to maintain a 1 N force on a moving motion simulator target under ultrasound guidance with 0.08 N RMS error. In a simulated ablation experiment, the robotic catheter was also able to apply a consistent force on the target while maintaining ablation electrode contact with 97% less RMS contact resistance variation than a passive mechanical equivalent. In addition, the use of force control improved catheter motion tracking by approximately 20%. These results demonstrate that 3D ultrasound guidance and force tracking allow the robotic system to maintain improved contact with a moving tissue structure, thus allowing for more accurate and repeatable cardiac procedures.Engineering and Applied Science

Design, Development and Force Control of a Tendon-driven Steerable Catheter with a Learning-based Approach

In this research, a learning-based force control schema for tendon-driven steerable catheters with the application in robot-assisted tissue ablation procedures was proposed and validated. To this end, initially a displacement-based model for estimating the contact force between the catheter and tissue was developed. Afterward, a tendon-driven catheter was designed and developed. Next, a software-hardware-integrated robotic system for controlling and monitoring the pose of the catheter was designed and developed. Also, a force control schema was developed based on the developed contact force model as a priori knowledge. Furthermore, the position control of the tip of the catheter was performed using a learning-based inverse kinematic approach. By combining the position control and the contact model, the force control schema was developed and validated. Validation studies were performed on phantom tissue as well as excised porcine tissue. Results of the validation studies showed that the proposed displacement-based model was 91.5% accurate in contact force prediction. Also, the system was capable of following a set of desired trajectories with an average root-mean-square error of less than 5%. Further validation studies revealed that the system could fairly generate desired static and dynamic force profiles on the phantom tissue. In summary, the proposed force control system did not necessitate the utilization of force sensors and could fairly contribute in automatizing the ablation task for robotic tissue ablation procedures

Image-Based Force Estimation and Haptic Rendering For Robot-Assisted Cardiovascular Intervention

Clinical studies have indicated that the loss of haptic perception is the prime limitation of robot-assisted cardiovascular intervention technology, hindering its global adoption. It causes compromised situational awareness for the surgeon during the intervention and may lead to health risks for the patients. This doctoral research was aimed at developing technology for addressing the limitation of the robot-assisted intervention technology in the provision of haptic feedback. The literature review showed that sensor-free force estimation (haptic cue) on endovascular devices, intuitive surgeon interface design, and haptic rendering within the surgeon interface were the major knowledge gaps. For sensor-free force estimation, first, an image-based force estimation methods based on inverse finite-element methods (iFEM) was developed and validated. Next, to address the limitation of the iFEM method in real-time performance, an inverse Cosserat rod model (iCORD) with a computationally efficient solution for endovascular devices was developed and validated. Afterward, the iCORD was adopted for analytical tip force estimation on steerable catheters. The experimental studies confirmed the accuracy and real-time performance of the iCORD for sensor-free force estimation. Afterward, a wearable drift-free rotation measurement device (MiCarp) was developed to facilitate the design of an intuitive surgeon interface by decoupling the rotation measurement from the insertion measurement. The validation studies showed that MiCarp had a superior performance for spatial rotation measurement compared to other modalities. In the end, a novel haptic feedback system based on smart magnetoelastic elastomers was developed, analytically modeled, and experimentally validated. The proposed haptics-enabled surgeon module had an unbounded workspace for interventional tasks and provided an intuitive interface. Experimental validation, at component and system levels, confirmed the usability of the proposed methods for robot-assisted intervention systems

Robotic Catheters for Beating Heart Surgery

Compliant and flexible cardiac catheters provide direct access to the inside of the heart via the vascular system without requiring clinicians to stop the heart or open the chest. However, the fast motion of the intracardiac structures makes it difficult to modify and repair the cardiac tissue in a controlled and safe manner. In addition, rigid robotic tools for beating heart surgery require the chest to be opened and the heart exposed, making the procedures highly invasive. The novel robotic catheter system presented here enables minimally invasive repair on the fast-moving structures inside the heart, like the mitral valve annulus, without the invasiveness or risks of stopped heart procedures. In this thesis, I investigate the development of 3D ultrasound-guided robotic catheters for beating heart surgery. First, the force and stiffness values of tissue structures in the left atrium are measured to develop design requirements for the system. This research shows that a catheter will experience contractile forces of 0.5 – 1.0 N and a mean tissue structure stiffness of approximately 0.1 N/mm while interacting with the mitral valve annulus. Next, this thesis presents the catheter system design, including force sensing, tissue resection, and ablation end effectors. In order to operate inside the beating heart, position and force control systems were developed to compensate for the catheter performance limitations of friction and deadzone backlash and evaluated with ex vivo and in vivo experiments. Through the addition of friction and deadzone compensation terms, the system is able to achieve position tracking with less than 1 mm RMS error and force tracking with 0.08 N RMS error under ultrasound image guidance. Finally, this thesis examines how the robotic catheter system enhances beating heart clinical procedures. Specifically, this system improves resection quality while reducing the forces experienced by the tissue by almost 80% and improves ablation performance by reducing contact resistance variations by 97% while applying a constant force on the moving tissue.Engineering and Applied Science

imaged-based tip force estimation on steerable intracardiac catheters using learning-based methods

Minimally invasive surgery has turned into the most commonly used approach to treat cardiovascular diseases during the surgical procedure; it is hypothesized that the absence of haptic (tactile) feedback and force presented to surgeons is a restricting factor. The use of ablation catheters with the integrated sensor at the tip results in high cost and noise complications. In this thesis, two sensor-less methods are proposed to estimate the force at the intracardiac catheter’s tip. Force estimation at the catheter tip is of great importance because insufficient force in ablation treatment may result in incomplete treatment and excessive force leads to damaging the heart chamber. Besides, adding the sensor to intracardiac catheters adds complexity to their structures. This thesis is categorized into two sensor-less approaches: 1- Learning-Based Force Estimation for Intracardiac Ablation Catheters, 2- A Deep-Learning Force Estimator System for Intracardiac Catheters. The first proposed method estimates catheter-tissue contact force by learning the deflected shape of the catheter tip section image. A regression model is developed based on predictor variables of tip curvature coefficients and knob actuation. The learning-based approach achieved force predictions in close agreement with experimental contact force measurements. The second approach proposes a deep learning method to estimate the contact forces directly from the catheter’s image tip. A convolutional neural network extracts the catheter’s deflection through input images and translates them into the corresponding forces. The ResNet graph was implemented as the architecture of the proposed model to perform a regression. The model can estimate catheter-tissue contact force based on the input images without utilizing any feature extraction or pre-processing. Thus, it can estimate the force value regardless of the tip displacement and deflection shape. The evaluation results show that the proposed method can elicit a robust model from the specified data set and approximate the force with appropriate accuracy

Design, Modeling and Control of Micro-scale and Meso-scale Tendon-Driven Surgical Robots

Manual manipulation of passive surgical tools is time consuming with uncertain results in cases of navigating tortuous anatomy, avoiding critical anatomical landmarks, and reaching targets not located in the linear range of these tools. For example, in many cardiovascular procedures, manual navigation of a micro-scale passive guidewire results in increased procedure times and radiation exposure. This thesis introduces the design of two steerable guidewires: 1) A two degree-of-freedom (2-DoF) robotic guidewire with orthogonally oriented joints to access points in a three dimensional workspace, and 2) a micro-scale coaxially aligned steerable (COAST) guidewire robot that demonstrates variable and independently controlled bending length and curvature of the distal end. The 2-DoF guidewire features two micromachined joints from a tube of superelastic nitinol of outer diameter 0.78 mm. Each joint is actuated with two nitinol tendons. The joints that are used in this robot are called bidirectional asymmetric notch (BAN) joints, and the advantages of these joints are explored and analyzed. The design of the COAST robotic guidewire involves three coaxially aligned tubes with a single tendon running centrally through the length of the robot. The outer tubes are made from micromachined nitinol allowing for tendon-driven bending of the robot at variable bending curvatures, while an inner stainless steel tube controls the bending length of the robot. By varying the lengths of the tubes as well as the tendon, and by insertion and retraction of the entire assembly, various joint lengths and curvatures may be achieved. Kinematic and static models, a compact actuation system, and a controller for this robot are presented. The capability of the robot to accurately navigate through phantom anatomical bifurcations and tortuous angles is also demonstrated in three dimensional phantom vasculature. At the meso-scale, manual navigation of passive pediatric neuroendoscopes for endoscopic third ventriculostomy may not reach target locations in the patient's ventricle. This work introduces the design, analysis and control of a meso-scale two degree-of-freedom robotic bipolar electrocautery tool that increases the workspace of the neurosurgeon. A static model is proposed for the robot joints that avoids problems arising from pure kinematic control. Using this model, a control system is developed that comprises of a disturbance observer to provide precise force control and compensate for joint hysteresis. A handheld controller is developed and demonstrated in this thesis. To allow the clinician to estimate the shape of the steerable tools within the anatomy for both micro-scale and meso-scale tools, a miniature tendon force sensor and a high deflection shape sensor are proposed and demonstrated. The force sensor features a compact design consisting of a single LED, dual-phototransistor, and a dual-screen arrangement to increase the linear range of sensor output and compensate for external disturbances, thereby allowing force measurement of up to 21 N with 99.58 % accuracy. The shape sensor uses fiber Bragg grating based optical cable mounted on a micromachined tube and is capable of measuring curvatures as high as 145 /m. These sensors were incorporated and tested in the guidewire and the neuroendoscope tool robots and can provide robust feedback for closed-loop control of these devices in the future.Ph.D

Development of an image guidance system for laparoscopic liver surgery and evaluation of optical and computer vision techniques for the assessment of liver tissue

Introduction: Liver resection is increasingly being carried out via the laparoscopic approach (keyhole surgery) because there is mounting evidence that it benefits patients by reducing pain and length of hospitalisation. There are however ongoing concerns about oncological radicality (i.e. ability to completely remove cancer) and an inability to control massive haemorrhage. These issues can partially be attributed to a loss of sensation such as depth perception, tactile feedback and a reduced field of view. Utilisation of optical imaging and computer vision may be able to compensate for some of the lost sensory input because these modalities can facilitate visualisation of liver tissue and structural anatomy. Their use in laparoscopy is attractive because it is easy to adapt or integrate with existing technology. The aim of this thesis is to explore to what extent this technology can aid in the detection of normal and abnormal liver tissue and structures. / Methods: The current state of the art for optical imaging and computer vision in laparoscopic liver surgery is assessed in a systematic review. Evaluation of confocal laser endomicroscopy is carried out on a murine and porcine model of liver disease. Multispectral near infrared imaging is evaluated on ex-vivo liver specimen. Video magnification is assessed on a mechanical flow phantom and a porcine model of liver disease. The latter model was also employed to develop a computer vision based image guidance system for laparoscopic liver surgery. This image guidance system is further evaluated in a clinical feasibility study. Where appropriate, experimental findings are substantiated with statistical analysis. / Results: Use of confocal laser endomicroscopy enabled discrimination between cancer and normal liver tissue with a sub-millimetre precision. This technology also made it possible to verify the adequacy of thermal liver ablation. Multispectral imaging, at specific wavelengths was shown to have the potential to highlight the presence of colorectal and hepatocellular cancer. An image reprocessing algorithm is proposed to simplify visual interpretation of the resulting images. It is shown that video magnification can determine the presence of pulsatile motion but that it cannot reliably determine the extent of motion. Development and performance metrics of an image guidance system for laparoscopic liver surgery are outlined. The system was found to improve intraoperative orientation more development work is however required to enable reliable prediction of oncological margins. / Discussion: The results in this thesis indicate that confocal laser endomicroscopy and image guidance systems have reached a development stage where their intraoperative use may benefit surgeons by visualising features of liver anatomy and tissue characteristics. Video magnification and multispectral imaging require more development and suggestions are made to direct this work. It is also highlighted that it is crucial to standardise assessment methods for these technologies which will allow a more direct comparison between the outcomes of different groups. Limited imaging depth is a major restriction of these technologies but this may be overcome by combining them with preoperatively obtained imaging data. Just like laparoscopy, optical imaging and computer vision use functions of light, a shared characteristic that makes their combined use complementary

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