Robotic System Development for Precision MRI-Guided Needle-Based Interventions

This dissertation describes the development of a methodology for implementing robotic systems for interventional procedures under intraoperative Magnetic Resonance Imaging (MRI) guidance. MRI is an ideal imaging modality for surgical guidance of diagnostic and therapeutic procedures, thanks to its ability to perform high resolution, real-time, and high soft tissue contrast imaging without ionizing radiation. However, the strong magnetic field and sensitivity to radio frequency signals, as well as tightly confined scanner bore render great challenges to developing robotic systems within MRI environment. Discussed are potential solutions to address engineering topics related to development of MRI-compatible electro-mechanical systems and modeling of steerable needle interventions. A robotic framework is developed based on a modular design approach, supporting varying MRI-guided interventional procedures, with stereotactic neurosurgery and prostate cancer therapy as two driving exemplary applications. A piezoelectrically actuated electro-mechanical system is designed to provide precise needle placement in the bore of the scanner under interactive MRI-guidance, while overcoming the challenges inherent to MRI-guided procedures. This work presents the development of the robotic system in the aspects of requirements definition, clinical work flow development, mechanism optimization, control system design and experimental evaluation. A steerable needle is beneficial for interventional procedures with its capability to produce curved path, avoiding anatomical obstacles or compensating for needle placement errors. Two kinds of steerable needles are discussed, i.e. asymmetric-tip needle and concentric-tube cannula. A novel Gaussian-based ContinUous Rotation and Variable-curvature (CURV) model is proposed to steer asymmetric-tip needle, which enables variable curvature of the needle trajectory with independent control of needle rotation and insertion. While concentric-tube cannula is suitable for clinical applications where a curved trajectory is needed without relying on tissue interaction force. This dissertation addresses fundamental challenges in developing and deploying MRI-compatible robotic systems, and enables the technologies for MRI-guided needle-based interventions. This study applied and evaluated these techniques to a system for prostate biopsy that is currently in clinical trials, developed a neurosurgery robot prototype for interstitial thermal therapy of brain cancer under MRI guidance, and demonstrated needle steering using both asymmetric tip and pre-bent concentric-tube cannula approaches on a testbed

Evaluation of Haptic and Visual Cues for Repulsive or Attractive Guidance in Nonholonomic Steering Tasks.

Remote control of vehicles is a difficult task for operators. Support systems that present additional task information may assist operators, but their usefulness is expected to depend on several factors such as 1) the nature of conveyed information, 2) what modality it is conveyed through, and 3) the task difficulty. In an exploratory experiment, these three factors were manipulated to quantify their effects on operator behavior. Subjects ( $n = {{15}}$) used a haptic manipulator to steer a virtual nonholonomic vehicle through abstract environments, in which obstacles needed to be avoided. Both a simple support conveying near-future predictions of the trajectory of the vehicle and a more elaborate support that continuously suggests the path to be taken were designed (factor 1). These types of information were offered either with visual or haptic cues (factor 2). These four support systems were tested in four different abstracted environments with decreasing amount of allowed variability in realized trajectories (factor 3). The results show improvements for the simple support only when this information was presented visually, but not when offered haptically. For the elaborate support, equally large improvements for both modalities were found. This suggests that the elaborate support is better: additional information is key in improving performance in nonholonomic steering tasks

Snake Robots for Surgical Applications: A Review

Although substantial advancements have been achieved in robot-assisted surgery, the blueprint to existing snake robotics predominantly focuses on the preliminary structural design, control, and human–robot interfaces, with features which have not been particularly explored in the literature. This paper aims to conduct a review of planning and operation concepts of hyper-redundant serpentine robots for surgical use, as well as any future challenges and solutions for better manipulation. Current researchers in the field of the manufacture and navigation of snake robots have faced issues, such as a low dexterity of the end-effectors around delicate organs, state estimation and the lack of depth perception on two-dimensional screens. A wide range of robots have been analysed, such as the i2Snake robot, inspiring the use of force and position feedback, visual servoing and augmented reality (AR). We present the types of actuation methods, robot kinematics, dynamics, sensing, and prospects of AR integration in snake robots, whilst addressing their shortcomings to facilitate the surgeon’s task. For a smoother gait control, validation and optimization algorithms such as deep learning databases are examined to mitigate redundancy in module linkage backlash and accidental self-collision. In essence, we aim to provide an outlook on robot configurations during motion by enhancing their material compositions within anatomical biocompatibility standards

Integrating Optimization and Sampling for Robot Motion Planning with Applications in Healthcare

Robots deployed in human-centric environments, such as a person's home in a home-assistance setting or inside a person's body in a surgical setting, have the potential to have a large, positive impact on human quality of life. However, for robots to operate in such environments they must be able to move efficiently while avoiding colliding with obstacles such as objects in the person's home or sensitive anatomical structures in the person's body. Robot motion planning aims to compute safe and efficient motions for robots that avoid obstacles, but home assistance and surgical robots come with unique challenges that can make this difficult. For instance, many state of the art surgical robots have computationally expensive kinematic models, i.e., it can be computationally expensive to predict their shape as they move. Some of these robots have hybrid dynamics, i.e., they consist of multiple stages that behave differently. Additionally, it can be difficult to plan motions for robots while leveraging real-world sensor data, such as point clouds. In this dissertation, we demonstrate and empirically evaluate methods for overcoming these challenges to compute high-quality and safe motions for robots in home-assistance and surgical settings. First, we present a motion planning method for a continuum, parallel surgical manipulator that accounts for its computationally expensive kinematics. We then leverage this motion planner to optimize its kinematic design chosen prior to a surgical procedure. Next, we present a motion planning method for a 3-stage lung tumor biopsy robot that accounts for its hybrid dynamics and evaluate the robot and planner in simulation and in inflated porcine lung tissue. Next, we present a motion planning method for a home-assistance robot that leverages real-world, point-cloud obstacle representations. We then expand this method to work with a type of continuum surgical manipulator, a concentric tube robot, with point-cloud anatomical representations. Finally, we present a data-driven machine learning method for more accurately estimating the shape of concentric tube robots. By effectively addressing challenges associated with home assistance and surgical robots operating in human-centric environments, we take steps toward enabling robots to have a positive impact on human quality of life.Doctor of Philosoph

Avancées du framework de simulation biomécanique inverse pour le pilotage automatique d’aiguilles robotisé

International audienc

Realistic tool-tissue interaction models for surgical simulation and planning

Surgical simulators present a safe and potentially effective method for surgical training, and can also be used in pre- and intra-operative surgical planning. Realistic modeling of medical interventions involving tool-tissue interactions has been considered to be a key requirement in the development of high-fidelity simulators and planners. The soft-tissue constitutive laws, organ geometry and boundary conditions imposed by the connective tissues surrounding the organ, and the shape of the surgical tool interacting with the organ are some of the factors that govern the accuracy of medical intervention planning.\ud \ud This thesis is divided into three parts. First, we compare the accuracy of linear and nonlinear constitutive laws for tissue. An important consequence of nonlinear models is the Poynting effect, in which shearing of tissue results in normal force; this effect is not seen in a linear elastic model. The magnitude of the normal force for myocardial tissue is shown to be larger than the human contact force discrimination threshold. Further, in order to investigate and quantify the role of the Poynting effect on material discrimination, we perform a multidimensional scaling study. Second, we consider the effects of organ geometry and boundary constraints in needle path planning. Using medical images and tissue mechanical properties, we develop a model of the prostate and surrounding organs. We show that, for needle procedures such as biopsy or brachytherapy, organ geometry and boundary constraints have more impact on target motion than tissue material parameters. Finally, we investigate the effects surgical tool shape on the accuracy of medical intervention planning. We consider the specific case of robotic needle steering, in which asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. We present an analytical and finite element (FE) model for the loads developed at the bevel tip during needle-tissue interaction. The analytical model explains trends observed in the experiments. We incorporated physical parameters (rupture toughness and nonlinear material elasticity) into the FE model that included both contact and cohesive zone models to simulate tissue cleavage. The model shows that the tip forces are sensitive to the rupture toughness. In order to model the mechanics of deflection of the needle, we use an energy-based formulation that incorporates tissue-specific parameters such as rupture toughness, nonlinear material elasticity, and interaction stiffness, and needle geometric and material properties. Simulation results follow similar trends (deflection and radius of curvature) to those observed in macroscopic experimental studies of a robot-driven needle interacting with gels