The presence of force feedback in medical instruments has been proven to reduce tissue damage. In order to provide force feedback, information about the interaction forces between the instrument and the environment must be known. Direct measurement of these forces by commercial sensors is not feasible due to space limitations. Thus, in this study we propose to estimate the interaction forces using strain measurements from Fiber Bragg Grating (FBG) sensors. These measurements can also be used for shape sensing and as a result both force and shape can be sensed simultaneously. For force sensing two models are proposed and compared. The first is based on a Rigid Link approximation, while the second uses the Cosserat rod theory. The models are validated experimentally using a tendon-driven continuum manipulator that is subjected to forces at the tip. The force estimates from the models are compared to the measurements from a commercial force sensor. Mean absolute errors of 11.2 mN (6.9%) and 15.9 mN (8.3%) are observed for the Rigid Link model and Cosserat model, respectively.

Needle insertion procedures are commonly performed in current clinical practice for diagnostic and therapeutic purposes. Although prevailing technology allows accurate localization of lesions, they cannot yet be precisely targeted. Needle steering is a promising technique to overcome this challenge. In this paper, we describe the development of a novel steering system for an actuated-tip flexible needle. Strain measurements from an array of Fiber Bragg Grating (FBG) sensors are used for online reconstruction of the needle shape in 3D-space. FBG-sensor data is then fused with ultrasound images obtained from a clinically-approved Automated Breast Volume Scanner (ABVS) using an unscented Kalman filter. A new ultrasound-based tracking algorithm is developed for the robust tracking of the needle in biological tissue. Two experimental cases are presented to evaluate the proposed steering system. In the first case, the needle shape is reconstructed using the tracked tip position in ultrasound images and FBG-sensor measurements, separately. The reconstructed shape is then compared with the actual 3D needle shape obtained from the ABVS. In the second case, two steering experiments are performed to evaluate the overall system by fusing the FBG-sensor data and ultrasound images. Average targeting errors are 1.29±0.41 mm and 1.42±0.72 mm in gelatin phantom and biological tissue, respectively.

Accurate closed-loop control of continuum manipulators requires integration of both models that describe their motion and methods to evaluate manipulator shape. This work presents a model that approximates the continuous shape of a continuum manipulator by a serial chain of rigid links, connected by flexible rotational joints. This rigid-link model permits a description of manipulator shape under different loading conditions. A kinematic controller, based on the manipulator Jacobian of the proposed rigid-link model, is implemented and realizes trajectory tracking, while using the kinematic redundancy of the manipulator to perform a secondary task of avoiding obstacles. The controller is evaluated on an experimental testbed, consisting of a planar tendon-driven continuum manipulator with two bending segments. Fiber Bragg grating (FBG) sensors are used to reconstruct 3-D manipulator shape, and is used as feedback for closed-loop control of the manipulator. Manipulator steering is evaluated for two cases: the first case involving steering around a static obstacle and the second case involving steering along a straight path while avoiding a moving obstacle. Mean trajectory tracking errors are 0.24 and 0.09 mm with maximum errors of 1.37 and 0.52 mm for the first and second cases, respectively. Finally, we demonstrate the possibility of FBG sensors to measure interaction forces, while simultaneously using them for shape sensing.

Needle insertions are common during surgical procedures. Accurately delivering the needle at a specific location in the human body is of importance for the clinical outcome of the procedure. Studies have already shown that robotically inserting traditional needles with a bevel tip can improve targeting accuracy. However, steering of such needles requires spinning the needle, which may lead to additional tissue damage. Therefore, we propose a novel design consisting of a flexible needle with a tendon-driven actuated-tip. Changing the orientation of the actuated-tip allows to control the steering direction of the needle and the amount of deflection. We derive the kinematic model which describes the needle path given the actuated-tip orientation based on nonholonomic kinematics. We present a method for steering the needle towards a target location in soft tissue. This method incorporates online parameter estimation in order to adapt for changes in tissue stiffness. Needle insertion experiments are performed in soft-tissue simulants, made from porcine gelatin. Needle tip pose is measured during insertion using Fiber Bragg Grating (FBG) based shape reconstruction. Results show that the needle can be steered towards targets located at 20 mm from the initial insertion axis, at a depth of 100 mm with a mean targeting error of 2.02 mm.

Flexible minimally invasive surgical instruments can be used to target difficult-to-reach locations within the human body. Accurately steering these instruments requires information about the three-dimensional shape of the instrument. In the current study, we use an array of Fiber Bragg Grating (FBG) sensors to reconstruct the shape of a flexible instrument. FBG sensors have several advantages over existing imaging modalities, which makes them well-suited for use in a clinical environment. An experimental testbed is presented in this study, which includes a tendon-driven manipulator. A nitinol FBG-wire is fabricated, on which an array of twelve FBG sensors are integrated, and distributed over four different sets. This wire is positioned in the backbone of the manipulator. Axial strains are measured using the FBG sensors, from which the curvature of the manipulator is calculated. The three-dimensional manipulator shape is reconstructed from the curvature, which is used to steer the manipulator tip. We are able to steer the manipulator along various trajectories (two-dimensional and three-dimensional), and also reject disturbance loads. We observe a minimum mean tracking error of 0.67 mm for the circular trajectory in closed-loop control. This study demonstrates the potential of steering flexible minimally invasive surgical instruments using an array of FBG sensors.

Needle insertion procedures are commonly used for diagnostic and therapeutic purposes. In this paper, an image-guided control system is developed to robotically steer flexible needles with an asymmetric tip. Knowledge about needle deflection is required for accurate steering. Two different models to predict needle deflection are presented. The first is a kinematics-based model, and the second model predicts needle deflection that is based on the mechanics of needle-tissue interaction. Both models predict deflection of needles that undergo multiple bends. The maximum targeting errors of the kinematics-based and the mechanics-based models for 110-mm insertion distance using a φ 0.5-mm needle are 0.8 and 1.7 mm, respectively. The kinematics-based model is used in the proposed image-guided control system. The control system accounts for target motion during the insertion procedure by detecting the target position in each image frame. Five experimental cases are presented to validate the real-time control system using both camera and ultrasound images as feedback. The experimental results show that the targeting errors of camera and ultrasound image-guided steering toward a moving target are 0.35 and 0.42 mm, respectively. The targeting accuracy of the algorithm is sufficient to reach the smallest lesions (φ 2 mm) that can be detected using the state-of-the-art ultrasound imaging systems.

When a needle is inserted into soft tissue, interaction forces are developed at the needle tip and along the needle shaft. The needle tip force is due to cutting of the tissue, and the force along the needle shaft is due to friction between needle and tissue. In this study, the friction force is determined for needles inserted into a gelatine phantom at insertion velocities of 10 mm/s and 20 mm/s. The friction force is found to be dependent on the insertion velocity. The needle tip force is calculated using the friction and insertion force, and is used as input for a mechanics-based model which predicts the amount of needle deflection. In the model, the needle is considered to be a cantilever beam supported by springs which have needle-tissue interaction stiffness (Ke). The value of the interaction stiffness is evaluated by comparing results from experiments and simulation. A mechanical needle insertion device is used to insert needles. Needle deflection during insertion is determined using a needle tip tracking algorithm. Results of this study provide insight into the mechanics of needle-tissue interaction, and can be used in studies for robotically steering needles into soft tissue.