SMART IMAGE-GUIDED NEEDLE INSERTION FOR TISSUE BIOPSY

M.S

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

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

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

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

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

Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches

Flexible needle insertion procedures are common for minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively. Through expansive research in fiber optics, a plethora of bio-compatible, MRI-compatible, optical shape-sensors have been developed to provide real-time shape feedback, such as single-core and multicore fiber Bragg gratings. In this paper, we directly compare single-core fiber-based and multicore fiber-based needle shape-sensing through identically constructed, four-active area sensorized bevel-tip needles inserted into phantom and \exvivo tissue on the same experimental platform. In this work, we found that for shape-sensing in phantom tissue, the two needles performed identically with a $p$-value of $0.164 > 0.05$, but in \exvivo real tissue, the single-core fiber sensorized needle significantly outperformed the multicore fiber configuration with a $p$-value of $0.0005 < 0.05$. This paper also presents the experimental platform and method for directly comparing these optical shape sensors for the needle shape-sensing task, as well as provides direction, insight and required considerations for future work in constructively optimizing sensorized needles

Robotics-Assisted Needle Steering for Percutaneous Interventions: Modeling and Experiments

Needle insertion and guidance plays an important role in medical procedures such as brachytherapy and biopsy. Flexible needles have the potential to facilitate precise targeting and avoid collisions during medical interventions while reducing trauma to the patient and post-puncture issues. Nevertheless, error introduced during guidance degrades the effectiveness of the planned therapy or diagnosis. Although steering using flexible bevel-tip needles provides great mobility and dexterity, a major barrier is the complexity of needle-tissue interaction that does not lend itself to intuitive control. To overcome this problem, a robotic system can be employed to perform trajectory planning and tracking by manipulation of the needle base. This research project focuses on a control-theoretic approach and draws on the rich literature from control and systems theory to model needle-tissue interaction and needle flexion and then design a robotics-based strategy for needle insertion/steering. The resulting solutions will directly benefit a wide range of needle-based interventions. The outcome of this computer-assisted approach will not only enable us to perform efficient preoperative trajectory planning, but will also provide more insight into needle-tissue interaction that will be helpful in developing advanced intraoperative algorithms for needle steering. Experimental validation of the proposed methodologies was carried out on a state of-the-art 5-DOF robotic system designed and constructed in-house primarily for prostate brachytherapy. The system is equipped with a Nano43 6-DOF force/torque sensor (ATI Industrial Automation) to measure forces and torques acting on the needle shaft. In our setup, an Aurora electromagnetic tracker (Northern Digital Inc.) is the sensing device used for measuring needle deflection. A multi-threaded application for control, sensor readings, data logging and communication over the ethernet was developed using Microsoft Visual C 2005, MATLAB 2007 and the QuaRC Toolbox (Quanser Inc.). Various artificial phantoms were developed so as to create a realistic medium in terms of elasticity and insertion force ranges; however, they simulated a uniform environment without exhibiting complexities of organic tissues. Experiments were also conducted on beef liver and fresh chicken breast, beef, and ham, to investigate the behavior of a variety biological tissues

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