A long short-term memory network for vessel reconstruction based on laser doppler flowmetry via a steerable needle

Hemorrhage is one risk of percutaneous intervention in the brain that can be life-threatening. Steerable needles can avoid blood vessels thanks to their ability to follow curvilinear paths, although knowledge of vessel pose is required. To achieve this, we present the deployment of laser Doppler flowmetry (LDF) sensors as an in-situ vessel detection method for steerable needles. Since the perfusion value from an LDF system does not provide positional information directly, we propose the use of a machine learning technique based on a Long Short-term Memory (LSTM) network to perform vessel reconstruction online. Firstly, the LSTM is used to predict the diameter and position of an approaching vessel based on successive measurements of a single LDF probe. Secondly, a "no-go" area is predicted based on the measurement from four LDF probes embedded within a steerable needle, which accounts for the full vessel pose. The network was trained using simulation data and tested on experimental data, with 75 % diameter prediction accuracy and 0.27 mm positional Root Mean Square (RMS) Error for the single probe network, and 77 % vessel volume overlap for the 4-probe setup

Nonlinear energy-based control of soft continuum pneumatic manipulators

This paper investigates the model-based nonlinear control of a class of soft continuum pneumatic manipulators that bend due to pressurization of their internal chambers and that operate in the presence of disturbances. A port-Hamiltonian formulation is employed to describe the closed loop system dynamics, which includes the pressure dynamics of the pneumatic actuation, and new nonlinear control laws are constructed with an energy-based approach. In particular, a multi-step design procedure is outlined for soft continuum manipulators operating on a plane and in 3D space. The resulting nonlinear control laws are combined with adaptive observers to compensate the effect of unknown disturbances and model uncertainties. Stability conditions are investigated with a Lyapunov approach, and the effect of the tuning parameters is discussed. For comparison purposes, a different control law constructed with a backstepping procedure is also presented. The effectiveness of the control strategy is demonstrated with simulations and with experiments on a prototype. To this end, a needle valve operated by a servo motor is employed instead of more sophisticated digital pressure regulators. The proposed controllers effectively regulate the tip rotation of the prototype, while preventing vibrations and compensating the effects of disturbances, and demonstrate improved performance compared to the backstepping alternative and to a PID algorithm

Vessel pose estimation for obstacle avoidance in needle steering surgery using multiple forward looking sensors

During percutaneous interventions in the brain, puncturing a vessel can cause life-threatening complications. To avoid such a risk, current research has been directed towards the development of steerable needles. However, there is a risk that vessels of a size which is close to or smaller than the resolution of commonly used preoperative imaging modalities (0.59 x 0.59 x 1 mm) would not be detected during procedure planning, with a consequent increase in risk to the patient. In this work, we present a novel ensemble of forward-looking sensors based on laser Doppler flowmetry, which are embedded within a biologically inspired steerable needle to enable vessel detection during the insertion process. Four Doppler signals are used to classify the pose of a vessel in front of the advancing needle with a high degree of accuracy (2$^{\circ}$ and 0.1 mm RMS errors), where relative measurements between sensors are used to correct for ambiguity. By using a robotic-assisted needle insertion process, and thus a precisely controlled insertion speed, we also demonstrate how the setup can be used to discriminate between tissue bulk motion and vessel motion. In doing so, we describe a sensing apparatus applicable to a variety of needle steering systems, with the potential to eliminate the risk of haemorrhage during percutaneous procedures

A novel sensing method to detect tissue boundaries during robotic needle insertion based on laser Doppler flowmetry

This study investigates the use of Laser Doppler Flowmetry (LDF) as a method to detect tissue transitions during robotic needle insertions. Insertions were performed in gelatin tissue phantoms with different optical and mechanical properties and into ex-vivo sheep brain. The effect of changing the optical properties of gelatin tissue phantoms was first investigated and it was shown that using gelatin concentration to modify the stiffness of samples was suitable. Needle insertion experiments were conducted into both one-layer and two-layer gelatin phantoms. In both cases, three stages could be observed in the perfusion values: tissue loading, rupture and tissue cutting. These were correlated to force values measured from the tip of the needle during insertion. The insertions into ex-vivo sheep brain also clearly showed the time of rupture in both force and perfusion values, demonstrating that tissue puncture can be detected using an LDF sensor at the tip of a needle

Laser Doppler sensing for blood vessel detection with a biologically inspired steerable needle

Puncturing blood vessels during percutaneous intervention in minimally invasive brain surgery can be a life threatening complication. Embedding a forward looking sensor in a rigid needle has been proposed to tackle this problem but, when using a rigid needle, the procedure needs to be interrupted and the needle extracted if a vessel is detected. As an alternative, we propose a novel optical method to detect a vessel in front of a steerable needle. The needle itself is based on a biomimetic, multi-segment design featuring four hollow working channels. Initially, a laser Doppler flowmetry probe is characterized in a tissue phantom with optical properties mimicking those of human gray matter. Experiments are performed to show that the probe has a 2.1 mm penetration depth and a 1 mm off-axis detection range for a blood vessel phantom with 5 mm/s flow velocity. This outcome demonstrates that the probe fulfills the minimum requirements for it to be used in conjunction with our needle. A pair of Doppler probes is then embedded in two of the four working channels of the needle and vessel reconstruction is performed using successive measurements to determine the depth and the off-axis position of the vessel from each laser Doppler probe. The off-axis position from each Doppler probe is then used to generate a "detection circle" per probe, and vessel orientation is predicted using tangent lines between the two. The vessel reconstruction has a depth Root Mean Square Error (RMSE) of 0.3 mm and an RMSE of 15° in the angular prediction, showing real promise for a future clinical application of this detection system

Design and manufacturing of an affordable soft robotic manipulator for minimally invasive diagnosis

Soft robotic manipulators are inherently compliant thus they are ideally suited for minimally invasive diagnosis and intervention. In addition, soft robotics allows for affordable manufacturing, thus it could be adopted in low and middle-income countries where conventional robotics is prohibitively expensive. In this work, the design, manufacturing, and actuation strategy of an affordable soft robotic manipulator is presented. The manufacturing process does not rely on sophisticated technologies, and the pneumatic actuation does not require digital pressure regulators. Instead, a low-cost solution consisting of a needle valve operated by a servo motor is employed. The prototype is assessed with experiments that demonstrate its functionality