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

A Novel Flexible and Steerable Probe for Minimally Invasive Soft Tissue Intervention

Current trends in surgical intervention favour a minimally invasive (MI) approach, in which complex procedures are performed through increasingly small incisions. Specifically, in neurosurgery, there is a need for minimally invasive keyhole access, which conflicts with the lack of maneuverability of conventional rigid instruments. In an attempt to address this fundamental shortcoming, this thesis describes the concept design, implementation and experimental validation of a novel flexible and steerable probe, named “STING” (Soft Tissue Intervention and Neurosurgical Guide), which is able to steer along curvilinear trajectories within a compliant medium. The underlying mechanism of motion of the flexible probe, based on the reciprocal movement of interlocked probe segments, is biologically inspired and was designed around the unique features of the ovipositor of certain parasitic wasps. Such insects are able to lay eggs by penetrating different kinds of “host” (e.g. wood, larva) with a very thin and flexible multi-part channel, thanks to a micro-toothed surface topography, coupled with a reciprocating “push and pull” motion of each segment. This thesis starts by exploring these foundations, where the “microtexturing” of the surface of a rigid probe prototype is shown to facilitate probe insertion into soft tissue (porcine brain), while gaining tissue purchase when the probe is tensioned outwards. Based on these findings, forward motion into soft tissue via a reciprocating mechanism is then demonstrated through a focused set of experimental trials in gelatine and agar gel. A flexible probe prototype (10 mm diameter), composed of four interconnected segments, is then presented and shown to be able to steer in a brain-like material along multiple curvilinear trajectories on a plane. The geometry and certain key features of the probe are optimised through finite element models, and a suitable actuation strategy is proposed, where the approach vector of the tip is found to be a function of the offset between interlocked segments. This concept of a “programmable bevel”, which enables the steering angle to be chosen with virtually infinite resolution, represents a world-first in percutaneous soft tissue surgery. The thesis concludes with a description of the integration and validation of a fully functional prototype within a larger neurosurgical robotic suite (EU FP7 ROBOCAST), which is followed by a summary of the corresponding implications for future work

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

Risk-Based Path Planning for a Steerable Flexible Probe for Neurosurgical Intervention

presentazione Poste

Towards a procedure-optimised steerable catheter for deep-seated neurosurgery

In recent years, steerable needles have attracted significant interest in relation to minimally invasive surgery (MIS). Specifically, the flexible, programmable bevel-tip needle (PBN) concept was successfully demonstrated in vivo in an evaluation of the feasibility of convection-enhanced delivery (CED) for chemotherapeutics within the ovine model with a 2.5 mm PBN prototype. However, further size reductions are necessary for other diagnostic and therapeutic procedures and drug delivery operations involving deep-seated tissue structures. Since PBNs have a complex cross-section geometry, standard production methods, such as extrusion, fail, as the outer diameter is reduced further. This paper presents our first attempt to demonstrate a new manufacturing method for PBNs that employs thermal drawing technology. Experimental characterisation tests were performed for the 2.5 mm PBN and the new 1.3 mm thermally drawn (TD) PBN prototype described here. The results show that thermal drawing presents a significant advantage in miniaturising complex needle structures. However, the steering behaviour was affected due to the choice of material in this first attempt, a limitation which will be addressed in future work

Sensorisation of a novel biologically inspired flexible needle

Percutaneous interventions are commonly performed during minimally invasive brain surgery, where a straight rigid instrument is inserted through a small incision to access a deep lesion in the brain. Puncturing a vessel during this procedure can be a life-threatening complication. Embedding a forward-looking sensor in a rigid needle has been proposed to tackle this problem; however, using a rigid needle, the procedure needs to be interrupted if a vessel is detected. Steerable needle technology could be used to avoid obstacles, such as blood vessels, due to its ability to follow curvilinear paths, but research to date was lacking in this respect. This thesis aims to investigate the deployment of forward-looking sensors for vessel detection in a steerable needle. The needle itself is based on a bioinspired programmable bevel-tip needle (PBN), a multi-segment design featuring four hollow working channels. In this thesis, laser Doppler flowmetry (LDF) is initially characterised to ensure that the sensor fulfils the minimum requirements for it to be used in conjunction with the needle. Subsequently, vessel reconstruction algorithms are proposed. To determine the axial and off-axis position of the vessel with respect to the probe, successive measurements of the LDF sensor are used. Ideally, full knowledge of the vessel orientation is required to execute an avoidance strategy. Using two LDF probes and a novel signal processing method described in this thesis, the predicted possible vessel orientations can be reduced to four, a setup which is explored here to demonstrate viable obstacle detection with only partial sensor information. Relative measurements from four LDF sensors are also explored to classify possible vessel orientations in full and without ambiguity, but under the assumption that the vessel is perpendicular to the needle insertion axis. Experimental results on a synthetic grey matter phantom are presented, which confirm these findings. To release the perpendicularity assumption, the thesis concludes with the description of a machine learning technique based on a Long Short-term memory network, which enables a vessel's spatial position, cross-sectional diameter and full pose to be predicted with sub-millimetre accuracy. Simulated and in-vitro examinations of vessel detection with this approach are used to demonstrate effective predictive ability. Collectively, these results demonstrate that the proposed steerable needle sensorisation is viable and could lead to improved safety during robotic assisted needle steering interventions.Open Acces

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

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