Needle Steering in 3-D Via Rapid Replanning

Steerable needles have the potential to improve the effectiveness of needle-based clinical procedures such as biopsy and drug delivery by improving targeting accuracy and reaching previously inaccessible targets that are behind sensitive or impenetrable anatomical regions. We present a new needle steering system capable of automatically reaching targets in 3-D environments while avoiding obstacles and compensating for real-world uncertainties. Given a specification of anatomical obstacles and a clinical target (e.g., from preoperative medical images), our system plans and controls needle motion in a closed-loop fashion under sensory feedback to optimize a clinical metric. We unify planning and control using a new fast algorithm that continuously replans the needle motion. Our rapid replanning approach is enabled by an efficient sampling-based rapidly exploring random tree (RRT) planner that achieves orders-of-magnitude reduction in computation time compared with prior 3-D approaches by incorporating variable curvature kinematics and a novel distance metric for planning. Our system uses an electromagnetic tracking system to sense the state of the needle tip during the procedure. We experimentally evaluate our needle steering system using tissue phantoms and animal tissue ex vivo. We demonstrate that our rapid replanning strategy successfully guides the needle around obstacles to desired 3-D targets with an average error of less than 3 mm

3D Dynamic Motion Planning for Robot-Assisted Cannula Flexible Needle Insertion into Soft Tissue

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning. © 2016 Author

Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles

© 2016 IEEE. Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation

Planning for steerable needles in neurosurgery

The increasing adoption of robotic-assisted surgery has opened up the possibility to control innovative dexterous tools to improve patient outcomes in a minimally invasive way. Steerable needles belong to this category, and their potential has been recognised in various surgical fields, including neurosurgery. However, planning for steerable catheters' insertions might appear counterintuitive even for expert clinicians. Strategies and tools to aid the surgeon in selecting a feasible trajectory to follow and methods to assist them intra-operatively during the insertion process are currently of great interest as they could accelerate steerable needles' translation from research to practical use. However, existing computer-assisted planning (CAP) algorithms are often limited in their ability to meet both operational and kinematic constraints in the context of precise neurosurgery, due to its demanding surgical conditions and highly complex environment. The research contributions in this thesis relate to understanding the existing gap in planning curved insertions for steerable needles and implementing intelligent CAP techniques to use in the context of neurosurgery. Among this thesis contributions showcase (i) the development of a pre-operative CAP for precise neurosurgery applications able to generate optimised paths at a safe distance from brain sensitive structures while meeting steerable needles kinematic constraints; (ii) the development of an intra-operative CAP able to adjust the current insertion path with high stability while compensating for online tissue deformation; (iii) the integration of both methods into a commercial user front-end interface (NeuroInspire, Renishaw plc.) tested during a series of user-controlled needle steering animal trials, demonstrating successful targeting performances. (iv) investigating the use of steerable needles in the context of laser interstitial thermal therapy (LiTT) for maesial temporal lobe epilepsy patients and proposing the first LiTT CAP for steerable needles within this context. The thesis concludes with a discussion of these contributions and suggestions for future work.Open Acces

Closed-Loop Planning and Control of Steerable Medical Needles

Steerable needles have the potential to increase the effectiveness of needle-based clinical procedures such as biopsy, drug delivery, and radioactive seed implantation for cancer treatment. These needles can trace curved paths when inserted into tissue, thereby increasing maneuverability and targeting accuracy while reaching previously inaccessible targets that are behind sensitive or impenetrable anatomical regions. Guiding these flexible needles along an intended path requires continuously inserting and twisting the needle at its base, which is not intuitive for a human operator. In addition, the needle often deviates from its intended trajectory due to factors such as tissue deformation, needle-tissue interaction, noisy actuation and sensing, modeling errors, and involuntary patient motions. These challenges can be addressed with the assistance of robotic systems that automatically compensate for these perturbations by performing motion planning and feedback control of the needle in a closed-loop fashion under sensory feedback. We present two approaches for efficient closed-loop guidance of steerable needles to targets within clinically acceptable accuracy while safely avoiding sensitive or impenetrable anatomical structures. The first approach uses a fast motion planning algorithm that unifies planning and control by continuously replanning, enabling correction for perturbations as they occur. We evaluate our method using a needle steering system in phantom and ex vivo animal tissues. The second approach integrates motion planning and feedback control of steerable needles in highly deformable environments. We demonstrate that this approach significantly improves the probability of success compared to prior approaches that either consider uncertainty or deformations but not both simultaneously. We also propose a data-driven method to estimate parameters of stochastic models of steerable needle motion. These models can be used to create realistic medical simulators for clinicians wanting to train for steerable needle procedures and to improve the effectiveness of existing planning and control methods. This dissertation advances the state of the art in planning and control of steerable needles and is an important step towards realizing needle steering in clinical practice. The methods developed in this dissertation also generalize to important applications beyond medical needle steering, such as manipulating deformable objects and control of mobile robots.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

Path replanning for orientation-constrained needle steering

Introduction: Needle-based neurosurgical procedures require high accuracy in catheter positioning to achieve high clinical efficacy. Significant challenges for achieving accurate targeting are (i) tissue deformation (ii) clinical obstacles along the insertion path (iii) catheter control. Objective: We propose a novel path-replanner able to generate an obstacle-free and curvature bounded three-dimensional (3D) path at each time step during insertion, accounting for a constrained target pose and intraoperative anatomical deformation. Additionally, our solution is sufficiently fast to be used in a closed-loop system: needle tip tracking via electromagnetic sensors is used by the path-replanner to automatically guide the programmable bevel-tip needle (PBN) while surgical constraints on sensitive structures avoidance are met. Methods: The generated path is achieved by combining the ”Bubble Bending” method for online path deformation and a 3D extension of a convex optimisation method for path smoothing. Results: Simulation results performed on a realistic dataset show that our replanning method can guide a PBN with bounded curvature to a predefined target pose with an average targeting error of 0.65 ± 0.46 mm in position and 3.25 ± 5.23 degrees in orientation under a deformable simulated environment. The proposed algorithm was also assessed in-vitro on a brain-like gelatin phantom, achieving a target error of 1.81 ± 0.51 mm in position and 5.9 ± 1.42 degrees in orientation. Conclusion: The presented work assessed the performance of a new online steerable needle path-planner able to avoid anatomical obstacles while optimizing surgical criteria. Significance: This method is particularly suited for surgical procedures demanding high accuracy on the desired goal pose under tissue deformations and real-world inaccuracies

High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning

As sampling-based motion planners become faster, they can be re-executed more frequently by a robot during task execution to react to uncertainty in robot motion, obstacle motion, sensing noise, and uncertainty in the robot’s kinematic model. We investigate and analyze high-frequency replanning (HFR), where, during each period, fast sampling-based motion planners are executed in parallel as the robot simultaneously executes the first action of the best motion plan from the previous period. We consider discrete-time systems with stochastic nonlinear (but linearizable) dynamics and observation models with noise drawn from zero mean Gaussian distributions. The objective is to maximize the probability of success (i.e., avoid collision with obstacles and reach the goal) or to minimize path length subject to a lower bound on the probability of success. We show that, as parallel computation power increases, HFR offers asymptotic optimality for these objectives during each period for goal-oriented problems. We then demonstrate the effectiveness of HFR for holonomic and nonholonomic robots including car-like vehicles and steerable medical needles

Semi-Automated Needle Steering in Biological Tissue Using an Ultrasound-Based Deflection Predictor

The performance of needle-based interventions depends on the accuracy of needle tip positioning. Here, a novel needle steering strategy is proposed that enhances accuracy of needle steering. In our approach the surgeon is in charge of needle insertion to ensure the safety of operation, while the needle tip bevel location is robotically controlled to minimize the targeting error. The system has two main components: (1) a real-time predictor for estimating future needle deflection as it is steered inside soft tissue, and (2) an online motion planner that calculates control decisions and steers the needle toward the target by iterative optimization of the needle deflection predictions. The predictor uses the ultrasound-based curvature information to estimate the needle deflection. Given the specification of anatomical obstacles and a target from preoperative images, the motion planner uses the deflection predictions to estimate control actions, i.e., the depth(s) at which the needle should be rotated to reach the target. Ex-vivo needle insertions are performed with and without obstacle to validate our approach. The results demonstrate the needle steering strategy guides the needle to the targets with a maximum error of 1.22 mm

Insights into infusion-based targeted drug delivery in brain: perspectives, challenges and opportunities

Targeted drug delivery in the brain is instrumental in the treatment of lethal brain diseases, such as glioblastoma multiforme, the most aggressive primary central nervous system tumour in adults. Infusion-based drug delivery techniques, which directly administer to the tissue for local treatment, as in convection-enhanced delivery (CED), provide an important opportunity; however, poor understanding of the pressure-driven drug transport mechanisms in the brain has hindered its ultimate success in clinical applications. In this review, we focus on the biomechanical and biochemical aspects of infusion-based targeted drug delivery in the brain and look into the underlying molecular level mechanisms. We discuss recent advances and challenges in the complementary field of medical robotics and its use in targeted drug delivery in the brain. A critical overview of current research in these areas and their clinical implications is provided. This review delivers new ideas and perspectives for further studies of targeted drug delivery in the brain