Efficient Motion and Inspection Planning for Medical Robots with Theoretical Guarantees

Medical robots enable faster and safer patient care. Continuum medical robots (e.g., steerable needles) have great potential to accomplish procedures with less damage to patients compared to conventional instruments (e.g., reducing puncturing and cutting of tissues). Due to their complexity and degrees of freedom, such robots are often harder and less intuitive for physicians to operate directly. Automating robot-assisted medical procedures can enable physicians and patients to harness the full potential of medical robots in terms of safety, efficiency, accuracy, and precision.Motion planning methods compute motions for a robot that satisfy various constraints and accomplish a specific task, e.g., plan motions for a mobile robot to move to a target spot while avoiding obstacles. Inspection planning is the task of planning motions for a robot to inspect a set of points of interest, and it has applications in domains such as industrial, field, and medical robotics. With motion and inspection planning, medical robots would be able to automatically accomplish tasks like biopsy and endoscopy while minimizing safety risks and damage to the patient. Computing a motion or inspection plan can be computationally hard since we have to consider application-specific constraints, which come from the robotic system due to the mechanical properties of the robot or come from the environment, such as the requirement to avoid critical anatomical structures during the procedure.I develop motion and inspection planning algorithms that focus on efficiency and effectiveness. Given the same computing power, higher efficiency would shorten the procedure time, thus reducing costs and improving patient outcomes. Additionally, for the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the algorithms involved in procedure automation. Therefore, I focus on providing theoretical guarantees to certify the performance of planners. More specifically, it is important to certify if a planner is able to find a plan if one exists (i.e., completeness) and if a planner is able to find a globally optimal plan according to a given metric (i.e., optimality).Doctor of Philosoph

Autonomous Medical Needle Steering In Vivo

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.Comment: 22 pages, 6 figure

Towards Robot Autonomy in Medical Procedures Via Visual Localization and Motion Planning

Robots performing medical procedures with autonomous capabilities have the potential to positively effect patient care and healthcare system efficiency. These benefits can be realized by autonomous robots facilitating novel procedures, increasing operative efficiency, standardizing intra- and inter-physician performance, democratizing specialized care, and focusing the physician’s time on subtasks that best leverage their expertise. However, enabling medical robots to act autonomously in a procedural environment is extremely challenging. The deforming and unstructured nature of the environment, the lack of features in the anatomy, and sensor size constraints coupled with the millimeter level accuracy required for safe medical procedures introduce a host of challenges not faced by robots operating in structured environments such as factories or warehouses. Robot motion planning and localization are two fundamental abilities for enabling robot autonomy. Motion planning methods compute a sequence of safe and feasible motions for a robot to accomplish a specified task, where safe and feasible are defined by constraints with respect to the robot and its environment. Localization methods estimate the position and orientation of a robot in its environment. Developing such methods for medical robots that overcome the unique challenges in procedural environments is critical for enabling medical robot autonomy. In this dissertation, I developed and evaluated motion planning and localization algorithms towards robot autonomy in medical procedures. A majority of my work was done in the context of an autonomous medical robot built for enhanced lung nodule biopsy. First, I developed a dataset of medical environments spanning various organs and procedures to foster future research into medical robots and automation. I used this data in my own work described throughout this dissertation. Next, I used motion planning to characterize the capabilities of the lung nodule biopsy robot compared to existing clinical tools and I highlighted trade-offs in robot design considerations. Then, I conducted a study to experimentally demonstrate the benefits of the autonomous lung robot in accessing otherwise hard-to-reach lung nodules. I showed that the robot enables access to lung regions beyond the reach of existing clinical tools with millimeter-level accuracy sufficient for accessing the smallest clinically operable nodules. Next, I developed a localization method to estimate the bronchoscope’s position and orientation in the airways with respect to a preoperatively planned needle insertion pose. The method can be used by robotic bronchoscopy systems and by traditional manually navigated bronchoscopes. The method is designed to overcome challenges with tissue motion and visual homogeneity in the airways. I demonstrated the success of this method in simulated lungs undergoing respiratory motion and showed the method’s ability to generalize across patients.Doctor of Philosoph

Toward certifiable optimal motion planning for medical steerable needles

Medical steerable needles can follow 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable optimal planner for steerable needles. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. This is the first motion planner for steerable needles that guarantees to compute in finite time an obstacle-avoiding plan (or notify the user that no such plan exists), under clinically appropriate assumptions. Based on this planner, we then develop the first resolution-optimal motion planner for steerable needles that further provides theoretical guarantees on the quality of the computed motion plan, that is, global optimality, in finite time. Compared to state-of-the-art steerable needle motion planners, we demonstrate with clinically realistic simulations that our planners not only provide theoretical guarantees but also have higher success rates, have lower computation times, and result in higher quality plans

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

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

Image-guided robots for dot-matrix tumor ablation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 203-208).Advances in medical imaging now provides detailed images of solid tumors inside the body and miniaturized energy delivery systems enable tumor destruction through local heating powered by a thin electrode. However, the use of thermal ablation as a first line of treatment is limited due to the difficulty in accurately matching a desired treatment and a limited region of active heating around an electrode. The purpose of this research is to identify and quantify the current limitations of image-guided interventional procedures and subsequently develop a procedure and devices to enable accurate and efficient execution of image-based interventional plans and thus ablation of a tumor of any shape with minimal damage to surrounding tissue. Current limitations of probe placement for ablation therapy were determined by a detailed retrospective study of 50 representative CT-guided procedures. On average, 21 CT scans were performed for a given procedure (range 11-38), with the majority devoted to needle orientation and insertion (mean number of scans was 54%) and trajectory planning (mean number of scans was 19%). A regression analysis yielded that smaller and deeper lesions were associated with a higher number of CT scans for needle orientation and insertion; highlighting the difficulty in targeting. Another challenge identified was repositioning the instrument distal tip within tissue. The first robot is a patient-mounted device that aligns an instrument along a desired trajectory via two motor-actuated concentric, crossed, and partially nested hoops. A carriage rides in the hoops and grips and inserts an instrument via a two degree-of-freedom friction drive. An imagebased point-and-click user interface relates appropriate clicks on the medical images to robot commands. Mounting directly on the patient provides a sufficiently stable and safe platform for actuation and eliminates the need to compensate for chest motion; thereby reducing the cost and complexity compared to other devices. Phantom experiments in a realistic clinical setting demonstrated a mean targeting accuracy of 3.5 mm with an average of five CT scans. The second robot is for repositioning the distal tip of a medical instrument to adjacent points within tissue. The steering mechanism is based on the concept of substantially straightening a pre-curved Nitinol stylet by retracting it into a concentric outer cannula, and re-deploying it at different axial and rotational cannula positions. The proximal end of the cannula is attached to the distal end of a screw-spline that enables it to be translated and rotated with respect to the casing. Translation of the stylet relative to the cannula is achieved with a second concentric, nested smaller diameter screw that is constrained to rotate with the cannula. The robot mechanism is compatible with the CT images, light enough to be supported on a patient's chest or attached to standard stereotactic frames. Targeting experiments in a gelatin phantom demonstrated a mean targeting error of 1.8 mm between the stylet tip and that predicted with a kinematic model. Ultimately, these types of systems are envisioned being used together as part of a highly dexterous patient-mounted positioning platform that can accurately perform ablation of large and irregularly shaped tumors inside medical imaging machines - offering the potential to replace expensive and traumatic surgeries with minimally invasive out-patient procedures.by Conor James Walsh.Ph.D

Medical device design within the ISO 13485 framework

The design and development of medical devices has become an increasing complex and regulated process. Little if any consideration is given to the regulatory requirements when developing medical devices in universities. This has resulted in an imposing barrier preventing academic innovation reaching clinical adoption. The scope of universities is not to become the legal manufacturer of medical devices. However, should the development of novel devices ever aim to benefit patient care and reach a clinical setting, design controls must be implemented throughout the project life cycle to demonstrate feasibility and safety. The aim of this thesis is to develop user-centred technologies which comply with industrial design control practices whilst helping to bolster and promote innovation within academia. Four projects relating to medical devices have been designed in response to well-defined and end-user-originated clinical needs. These devices can serve as the exemplar for the framework developed in this work with each reaching staggered phases of development within a controlled design process. Although unique, the devices have significant overlapping characteristics that lend the devices to parallel development, leveraging in-house know-how and ‘lessons learned’ into the process of innovation. This thesis focuses on the novelty and design of the aforementioned projects in a discrete structured approach and reflects on the development of each project within the context of a design control process which was developed as part of this work. It is the ultimate goal of this work to develop a flexible structured system compliant with the international requirements for product design and development which may be exported internationally. However, the full execution of this ambition was limited due physical, and financial limitations. This manuscript will describe the technical and commercial opportunity of devices and reflects on the success of developing same within a design control process developed as part of this work