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

Cyclic motion control for programmable bevel-tip needles 3D steering: a simulation study

Flexible, steerable, soft needles are desirable in Minimally Invasive Surgery to achieve complex trajectories while maintaining the benefits of percutaneous intervention compared to open surgery. One such needle is the multi-segment Programmable Bevel-tip Needle (PBN), which is inspired by the mechanical design of the ovipositor of certain wasps. PBNs can steer in 3D whilst minimizing the force applied to the surrounding substrate, due to the cyclic motion of the segments. Taking inspiration also from the control strategy of the wasp to perform insertions and lay their eggs, this paper presents the design of a cyclic controller that can steer a PBN to produce a desired trajectory in 3D. The performance of the controller is demonstrated in simulation in comparison to that of a direct controller without cyclic motion. It is shown that, while the same steering curvatures can be attained by both controllers, the time taken to achieve the configuration is longer for the cyclic controller, leading to issues of potential under-steering and longer insertion times

Full 3D motion control for programmable bevel-tip steerable needles

Minimally invasive surgery has been in the focus of many researchers due to its reduced intra- and post-operative risks when compared to an equivalent open surgery approach. In the context of minimally invasive surgery, percutaneous intervention, and particularly, needle insertions, are of great importance in tumour-related therapy and diagnosis. However, needle and tissue deformation occurring during needle insertion often results in misplacement of the needles, which leads to complications, such as unsuccessful treatment and misdiagnosis. To this end, steerable needles have been proposed to compensate for placement errors by allowing curvilinear navigation. A particular type of steerable needle is the programmable bevel-tip steerable needle (PBN), which is a bio-inspired needle that consists of relatively soft and slender segments. Due to its flexibility and bevel-tip segments, it can navigate through 3D curvilinear paths. In PBNs, steering in a desired direction is performed by actuating particular PBN segments. Therefore, the pose of each segment is needed to ensure that the correct segment is actuated. To this end, in this thesis, proprioceptive sensing methods for PBNs were investigated. Two novel methods, an electromagnetic (EM)-based tip pose estimation method and a fibre Bragg grating (FBG)-based full shape sensing method, were presented and evaluated. The error in position was observed to be less than 1.08 mm and 5.76 mm, with the proposed EM-based tip tracking and FBG-based shape reconstruction methods, respectively. Moreover, autonomous path-following controllers for PBNs were also investigated. A closed-loop, 3D path-following controller, which was closed via feedback from FBG-inscribed multi-core fibres embedded within the needle, was presented. The nonlinear guidance law, which is a well-known approach for path-following control of aerial vehicles, and active disturbance rejection control (ADRC), which is known for its robustness within hard-to-model environments, were chosen as the control methods. Both linear and nonlinear ADRC were investigated, and the approaches were validated in both ex vivo brain and phantom tissue, with some of the experiments involving moving targets. The tracking error in position was observed to be less than 6.56 mm.Open Acces

Modelling the deformation of biologically inspired flexible structures for needle steering

Recent technical advances in minimally invasive surgery have been enabled by the development of new medical instruments and technologies. To date, the vast majority of mechanisms used within a clinical context are rigid, contrasting with the compliant nature of biological tissues. The field of robotics has seen an increased interest in flexible and compliant systems, and in this paper we investigate the behaviour of deformable multi-segment structures, which take their inspiration from the ovipositor design of parasitic wood wasps. These configurable structures have been shown to steer through highly compliant substrates, potentially enabling percutaneous access to the most delicate of tissues, such as the brain. The model presented here sheds light on how the deformation of the unique structure is related to its shape, and allows comparison between different potential designs. A finite element study is used to evaluate the proposed model, which is shown to provide a good fit (root-mean-square deviation 0.2636 mm for 4-segment case). The results show that both 3-segment and 4-segment designs are able to achieve deformation in all directions, however the magnitude of deformation is more consistent in the 4-segment case

Human-robot visual interface for 3D steering of a flexible, bioinspired needle for neurosurgery

Robotic minimally invasive surgery has been a subject of intense research and development over the last three decades, due to the clinical advantages it holds for patients and doctors alike. Particularly for drug delivery mechanisms, higher precision and the ability to follow complex trajectories in three dimensions (3D), has led to interest in flexible, steerable needles such as the programmable bevel-tip needle (PBN). Steering in 3D, however, holds practical challenges for surgeons, as interfaces are traditionally designed for straight line paths. This work presents a pilot study undertaken to evaluate a novel human-machine visual interface for the steering of a robotic PBN, where both qualitative evaluation of the interface and quantitative evaluation of the performance of the subjects in following a 3D path are measured. A series of needle insertions are performed in phantom tissue (gelatin) by the experiment subjects. User could adequately use the system with little training and low workload, and reach the target point at the end of the path with millimeter range accuracy

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

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

Automatic multi-trajectory planning solution for steerable catheters

The present work describes a novel approach to trajectory planning for minimally invasive surgery consisting of an algorithm able to provide the surgeon with multiple curvilinear paths to connect an entry area defined on the brain cortex to a specific target point in the brain. A criterion based on the minimum distance from the safety-critical brain struc- tures (blood vessels, thalamus and ventricles) is used to rank the obtained trajectories. The solution is integrated onto the EDEN2020∗ programmable bevel-tip needle, a multi-segment probe whose steering ability derives from the offset generated on its tip, and provides a level of tolerance with respect to tracking errors arising from catheter model inaccuracies. The case of study of the work consists of a typical Deep Brain Stimulation scenario where tests have been performed in order to compare the result obtained from standard rectilinear trajectory planning against this novel curvilinear solution using the clearance from obstacles as an index of performance of the estimated solutions

Automatic optimized 3D path planner for steerable catheters with heuristic search and uncertainty tolerance

In this paper, an automatic planner for minimally invasive neurosurgery is presented. The solution can provide the surgeon with the best path to connect a user-defined entry point with a target in accordance with specific optimality criteria guaranteeing the clearance from obstacles which can be found along the insertion pathway. The method is integrated onto the EDEN2020∗ programmable bevel-tip needle, a multi-segment steerable probe intended to be used to perform drug delivery for glioblastomas treatment. A sample-based heuristic search inspired to the BIT* algorithm is used to define the optimal solution in terms of path length, followed by a smoothing phase required to meet the kinematic constraint of the catheter. To account for inaccuracies in catheter modeling, which could de- termine unexpected control errors over the insertion procedure, an uncertainty margin is defined so that to include a further level of safety for the planning algorithm. The feasibility of the proposed solution was demonstrated by testing the method in simulated neurosurgical scenarios with different degree of obstacles occupancy and against other sample-based algorithms present in literature: RRT, RRT* and an enhanced version of the RRT-Connect