In Total Knee Replacement Arthroplasty (TKA), surgical robotics can provide image-guided navigation to fit implants with high precision. Its tracking approach highly relies on inserting bone pins into the bones tracked by the optical tracking system. This is normally done by invasive, radiative manners (implantable markers and CT scans), which introduce unnecessary trauma and prolong the preparation time for patients. To tackle this issue, ultrasound-based bone tracking could offer an alternative. In this study, we proposed a novel deep-learning structure to improve the accuracy of bone tracking by an A-mode ultrasound (US). We first obtained a set of ultrasound dataset from the cadaver experiment, where the ground truth locations of bones were calculated using bone pins. These data were used to train the proposed CasAtt-UNet to predict bone location automatically and robustly. The ground truth bone locations and those locations of US were recorded simultaneously. Therefore, we could label bone peaks in the raw US signals. As a result, our method achieved sub-millimeter precision across all eight bone areas with the only exception of one channel in the ankle. This method enables the robust measurement of lower extremity bone positions from 1D raw ultrasound signals. It shows great potential to apply A-mode ultrasound in orthopedic surgery from safe, convenient, and efficient perspectives.

Despite the recent advances in soft endoscopes, they could not yet fully fulfill the requirements for minimally invasive and natural orifice transluminal endoscopic surgeries. Maneuverability, bendability, different structural stiffness required for different endoscopic surgical interventions, the space needed for surgical manipulators and patient’s safety are amongst the main factors which can contribute to implementing the new soft robotics endoscope in practice. In this study, based on finite element analysis on an existing endoscopic segment, a new improved endoscopic module was developed. A novel approach for stiffening of the endoscopic module was proposed. The actuation and stiffening components were combined to introduce a multi-level stiffening mechanism to the endoscope, and also to provide a free lumen for manipulators. To increase patient’s safety, a force sensing module was developed to estimate the magnitude and direction of the force from tissues to the endoscope. The developed endoscopic system was integrated to a haptic control system. The 3D kinematics control and haptic feedback control of the endoscopic module were validated.

Flexible bevel-tipped needle steering has been an active research topic since the last decade. However, most of the work presented so far performs flexible needle steering in static phantoms or non-static virtual environments. The insertion of flexible bevel-tipped needles into soft-tissue subjected to motion disturbances is still an open research problem. In this paper, we propose a flexible needle steering algorithm that is able to handle physiological motion disturbances. The system estimates the disturbance using the force information provided by a force sensor placed in contact with the soft-tissue. The system is evaluated through experiments steering a flexible needle towards a physical target into a moving soft-tissue phantom. Three experimental cases with motion disturbances based on clinical procedures in the lung, kidney and heart are used to assess the targeting error. The average targeting error of all 15 experimental trials is 1:05±0:41mm. Our results demonstrate the ability of the proposed system to compensate for motion disturbances applied to the soft-tissue phantom.

Needle insertion in soft-tissue is a minimally invasive surgical procedure which demands high accuracy. In this respect, robotic systems with autonomous control algorithms have been exploited as the main tool to achieve high accuracy and reliability. However, for reasons of safety and acceptance by the surgical community, autonomous robotic control is not desirable. Thus, it is necessary to focus more on techniques enabling clinicians to directly control the motion of surgical tools. In this work we address that challenge and present a novel teleoperated robotic system able to steer flexible needles. The proposed system tracks the position of the needle using an ultrasound imaging system, and, from that, it computes needle's ideal position and orientation to reach a given target. The master haptic interface then provides mixed kinesthetic-vibratory navigation cues about this ideal position and orientation to the clinician as she steers the needle. Six subjects carried out an experiment of teleoperated needle insertion into a soft-tissue phantom. They showed a mean targeting error of 1.36 mm. An additional experiment of remote teleoperation has been carried out to highlight the passivity-based stability of the proposed system.

Needle insertion procedures are commonly used for surgical interventions. In this paper, we develop a three-dimensional (3D) closed-loop control algorithm to robotically steer flexible needles with an asymmetric tip towards a target in a soft-tissue phantom. Twelve Fiber Bragg Grating (FBG) sensors are embedded on the needle shaft. FBG sensors measure the strain applied on the needle during insertion. A method is developed to reconstruct the needle shape using the strain data obtained from the FBG sensors. Four experimental cases are conducted to validate the reconstruction method (single-bend, double-bend, 3D double-bend and drilling insertions). In the experiments, the needle is inserted 120 mm into a soft-tissue phantom. Camera images are used as a reference for the reconstruction experiments. The results show that the mean needle tip accuracy of the reconstruction method is 1.8 mm. The reconstructed needle shape is used as feedback for the steering algorithm. The steering algorithm estimates the region that the needle can reach during insertion, and controls the needle to keep the target in this region. Steering experiments are performed for 110 mm insertion, and the mean targeting accuracy is 1.3 mm. The results demonstrate the capability of using FBG sensors to robotically steer needles.

Needle insertion is one of the most commonly performed minimally invasive procedures. Visualization of the needle during insertion is key for either successful diagnosis or therapy. This work presents the real-time three-dimensional tracking of flexible needles during insertion into a soft-tissue simulant using a two-dimensional (2D) ultrasound transducer. The transducer is placed perpendicular to the needle tip to measure its position. During insertion the transducer is robotically repositioned to track the needle tip. Positioning of the transducer is accomplished by a compensator, that uses the needle insertion velocity corrected by needle tip velocities to determine out-of-plane motion. Experiments are performed to validate the needle tip pose during tracking. The maximum mean errors in needle tip position along the x-, y- and z-axes are 0.64 mm, 0.25 mm and 0.27 mm, respectively. The error in tip orientations (θ-about the y-axis and φ-about the z-axis) are 2.68° and 2.83°, respectively. This study demonstrates the ability to compute the needle tip pose using a 2D ultrasound transducer. The tip pose can be used to robotically steer needles, and thereby improve accuracy of medical procedures.

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.