Virtual and Augmented Reality Techniques for Minimally Invasive Cardiac Interventions: Concept, Design, Evaluation and Pre-clinical Implementation

While less invasive techniques have been employed for some procedures, most intracardiac interventions are still performed under cardiopulmonary bypass, on the drained, arrested heart. The progress toward off-pump intracardiac interventions has been hampered by the lack of adequate visualization inside the beating heart. This thesis describes the development, assessment, and pre-clinical implementation of a mixed reality environment that integrates pre-operative imaging and modeling with surgical tracking technologies and real-time ultrasound imaging. The intra-operative echo images are augmented with pre-operative representations of the cardiac anatomy and virtual models of the delivery instruments tracked in real time using magnetic tracking technologies. As a result, the otherwise context-less images can now be interpreted within the anatomical context provided by the anatomical models. The virtual models assist the user with the tool-to-target navigation, while real-time ultrasound ensures accurate positioning of the tool on target, providing the surgeon with sufficient information to ``see\u27\u27 and manipulate instruments in absence of direct vision. Several pre-clinical acute evaluation studies have been conducted in vivo on swine models to assess the feasibility of the proposed environment in a clinical context. Following direct access inside the beating heart using the UCI, the proposed mixed reality environment was used to provide the necessary visualization and navigation to position a prosthetic mitral valve on the the native annulus, or to place a repair patch on a created septal defect in vivo in porcine models. Following further development and seamless integration into the clinical workflow, we hope that the proposed mixed reality guidance environment may become a significant milestone toward enabling minimally invasive therapy on the beating heart

Remote Navigation and Contact-Force Control of Radiofrequency Ablation Catheters

Atrial fibrillation (AF), the most common and clinically significant heart rhythm disorder, is characterized by rapid and irregular electrical activity in the upper chambers resulting in abnormal contractions. Radiofrequency (RF) cardiac catheter ablation is a minimally invasive curative treatment that aims to electrically correct signal pathways inside the atria to restore normal sinus rhythm. Successful catheter ablation requires the complete and permanent elimination of arrhythmogenic signals by delivering transmural RF ablation lesions contiguously near and around key cardiac structures. These procedures are complex and technically challenging and, even when performed by the most skilled physician, nearly half of patients undergo repeat procedures due to incomplete elimination of the arrhythmogenic pathways. This thesis aims to incorporate innovative design to improve catheter stability and maneuverability through the development of robotic platforms that enable precise placement of reproducibly durable ablation lesions. The first part of this thesis deals with the challenges to lesion delivery imposed by cardiorespiratory motion. One of the main determinants of the delivery of durable and transmural RF lesions is the ability to define and maintain a constant contact force between the catheter tip electrode and cardiac tissue, which is hampered by the presence of cardiorespiratory motion. To address this need, I developed and evaluated a novel catheter contact-force control device. The compact electromechanical add-on tool monitors catheter-tissue contact force in real-time and simultaneously adjusts the position of a force-sensing ablation catheter within a steerable sheath to compensate for the change in contact force. In a series of in vitro and in vivo experiments, the contact-force control device demonstrated an ability to: a) maintain an average force to within 1 gram of a set level; b) reduce contact-force variation to below 5 grams (2-8-fold improvement over manual catheter intervention); c) ensure the catheter tip never lost contact with the tissue and never approached dangerous force levels; and importantly, d) deliver reproducible RF ablation lesions regardless of cardiac tissue motion, which were of the same depth and volume as lesions delivered in the absence of tissue motion. In the second part of the thesis, I describe a novel steerable sheath and catheter robotic navigation system, which incorporates the catheter contact-force controller. The robotic platform enables precise and accurate manipulation of a remote conventional steerable sheath and permits catheter-tissue contact-force control. The robotic navigation system was evaluated in vitro using a phantom that combines stationary and moving targets within an in vitro model representing a beating heart. An electrophysiologist used the robotic system to remotely navigate the sheath and catheter tip to select targets and compared the accuracy of reaching these targets performing the same tasks manually. Robotic intervention resulted in significantly higher accuracy and significantly improved the contact-force profile between the catheter tip and moving tissue-mimicking material. Our studies demonstrate that using available contact-force information within a robotic system can ensure precise and accurate placement of reliably transmural RF ablation lesions. These robotic systems can be valuable tools used to optimize RF lesion delivery techniques and ultimately improve clinical outcomes for AF ablation therapy

A Platform for Robot-Assisted Intracardiac Catheter Navigation

Steerable catheters are routinely deployed in the treatment of cardiac arrhythmias. During invasive electrophysiology studies, the catheter handle is manipulated by an interventionalist to guide the catheter's distal section toward endocardium for pacing and ablation. Catheter manipulation requires dexterity and experience, and exposes the interventionalist to ionizing radiation. Through the course of this research, a platform was developed to assist and enhance the navigation of the catheter inside the cardiac chambers. This robotic platform replaces the interventionalist's hand in catheter manipulation and provides the option to force the catheter tip in arbitrary directions using a 3D input device or to automatically navigate the catheter to desired positions within a cardiac chamber by commanding the software to do so. To accomplish catheter navigation, the catheter was modeled as a continuum manipulator, and utilizing robot kinematics, catheter tip position control was designed and implemented. An electromagnetic tracking system was utilized to measure the position and orientation of two key points in catheter model, for position feedback to the control system. A software platform was developed to implement the navigation and control strategies and to interface with the robot, the 3D input device and the tracking system. The catheter modeling was validated through in-vitro experiments with a static phantom, and in-vivo experiments on three live swines. The feasibility of automatic navigation was also veri ed by navigating to three landmarks in the beating heart of swine subjects, and comparing their performance with that of an experienced interventionalist using quasi biplane fluoroscopy. The platform realizes automatic, assisted, and motorized navigation under the interventionalist's control, thus reducing the dependence of successful navigation on the dexterity and manipulation skills of the interventionalist, and providing a means to reduce the exposure to X-ray radiation. Upon further development, the platform could be adopted for human deployment

Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

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

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

Robotics and imaging in congenital heart surgery

The initial success seen in adult cardiac surgery with the application of available robotic systems has not been realized as broadly in pediatric cardiac surgery. The main obstacles include extended set-up time and complexity of the procedures, as well as the large size of the instruments with respect to the size of the child. Moreover, while the main advantage of robotic systems is the ability to minimize incision size, for intracardiac repairs, cardiopulmonary bypass is still required. Catheter-based interventions, on the other hand, have expanded rapidly in both application as well as the complexity of procedures and lesions being treated. However, despite the development of sophisticated devices, robotic systems to aid catheter procedures have not been commonly applied in children. In this article, we describe new catheter-like robotic delivery platforms, which facilitate safe navigation and enable complex repairs, such as tissue approximation and fixation, and tissue removal, inside the beating heart. Additional features including the tracking of rapidly moving tissue targets and novel imaging approaches are described, along with a discussion of future prospects for steerable robotic systems

The Role of Visualization, Force Feedback, and Augmented Reality in Minimally Invasive Heart Valve Repair

New cardiovascular techniques have been developed to address the unique requirements of high risk, elderly, surgical patients with heart valve disease by avoiding both sternotomy and cardiopulmonary bypass. However, these technologies pose new challenges in visualization, force application, and intracardiac navigation. Force feedback and augmented reality (AR) can be applied to minimally invasive mitral valve repair and transcatheter aortic valve implantation (TAVI) techniques to potentially surmount these challenges. Our study demonstrated shorter operative times with three dimensional (3D) visualization compared to two dimensional (2D) visualization; however, both experts and novices applied significantly more force to cardiac tissue during 3D robotics-assisted mitral valve annuloplasty than during conventional open mitral valve annuloplasty. This finding suggests that 3D visualization does not fully compensate for the absence of haptic feedback in robotics-assisted cardiac surgery. Subsequently, using an innovative robotics-assisted surgical system design, we determined that direct haptic feedback may improve both expert and trainee performance using robotics-assisted techniques. We determined that during robotics-assisted mitral valve annuloplasty the use of either visual or direct force feedback resulted in a significant decrease in forces applied to cardiac tissue when compared to robotics-assisted mitral valve annuloplasty without force feedback. We presented NeoNav, an AR-enhanced echocardiograpy intracardiac guidance system for NeoChord off-pump mitral valve repair. Our study demonstrated superior tool navigation accuracy, significantly shorter navigation times, and reduced potential for injury with AR enhanced intracardiac navigation for off-pump transapical mitral valve repair with neochordae implantation. In addition, we applied the NeoNav system as a safe and inexpensive alternative imaging modality for TAVI guidance. We found that our proposed AR guidance system may achieve similar or better results than the current standard of care, contrast enhanced fluoroscopy, while eliminating the use of nephrotoxic contrast and ionizing radiation. These results suggest that the addition of both force feedback and augmented reality image guidance can improve both surgical performance and safety during minimally invasive robotics assisted and beating heart valve surgery, respectively

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