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

Image-Guided Robot-Assisted Techniques with Applications in Minimally Invasive Therapy and Cell Biology

There are several situations where tasks can be performed better robotically rather than manually. Among these are situations (a) where high accuracy and robustness are required, (b) where difficult or hazardous working conditions exist, and (c) where very large or very small motions or forces are involved. Recent advances in technology have resulted in smaller size robots with higher accuracy and reliability. As a result, robotics is fi nding more and more applications in Biomedical Engineering. Medical Robotics and Cell Micro-Manipulation are two of these applications involving interaction with delicate living organs at very di fferent scales.Availability of a wide range of imaging modalities from ultrasound and X-ray fluoroscopy to high magni cation optical microscopes, makes it possible to use imaging as a powerful means to guide and control robot manipulators. This thesis includes three parts focusing on three applications of Image-Guided Robotics in biomedical engineering, including: Vascular Catheterization: a robotic system was developed to insert a catheter through the vasculature and guide it to a desired point via visual servoing. The system provides shared control with the operator to perform a task semi-automatically or through master-slave control. The system provides control of a catheter tip with high accuracy while reducing X-ray exposure to the clinicians and providing a more ergonomic situation for the cardiologists. Cardiac Catheterization: a master-slave robotic system was developed to perform accurate control of a steerable catheter to touch and ablate faulty regions on the inner walls of a beating heart in order to treat arrhythmia. The system facilitates touching and making contact with a target point in a beating heart chamber through master-slave control with coordinated visual feedback. Live Neuron Micro-Manipulation: a microscope image-guided robotic system was developed to provide shared control over multiple micro-manipulators to touch cell membranes in order to perform patch clamp electrophysiology. Image-guided robot-assisted techniques with master-slave control were implemented for each case to provide shared control between a human operator and a robot. The results show increased accuracy and reduced operation time in all three cases

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

GPU-based proximity query processing on unstructured triangular mesh model

This paper presents a novel proximity query (PQ) approach capable to detect the collision and calculate the minimal Euclidean distance between two non-convex objects in 3D, namely the robot and the environment. Such approaches are often considered as computationally demanding problems, but are of importance to many applications such as online simulation of haptic feedback and robot collision-free trajectory. Our approach enables to preserve the representation of unstructured environment in the form of triangular meshes. The proposed PQ algorithm is computationally parallel so that it can be effectively implemented on graphics processing units (GPUs). A GPU-based computation scheme is also developed and customized, which shows >200 times faster than an optimized CPU with single core. Comprehensive validation is also conducted on two simulated scenarios in order to demonstrate the practical values of its potential application in image-guided surgical robotics and humanoid robotic control.published_or_final_versio

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

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