Ultrasound (US) is widely used in image-guided needle procedures. Correctly tracking the needle tip position in US images during the procedure plays an important role in improving the needle targeting accuracy and patient safety. This paper presents a leaning-based visual tracking network with a Siamese architecture, which makes full use of the attention mechanism to explore the potential of global features and takes advantage of an online target model prediction module to robustly track the needle tip in US images. Several self- and cross-attention modules are applied to learn global features from the whole US image. A discriminative target model is also learned as a complementary part to improve the discriminability of the proposed tracker. The template used during the tracking is updated frequently according to the tracking results to ensure that the tracker can always capture the latest characteristics of the appearance of the needle tip. Experimental results in both phantom and tissue showed that the proposed tracking network was more robust than other state-of-the-art visual trackers. The mean success rates of the proposed tracker are 7.1% and 9.2% higher than the second best performing visual tacker when the needle was inserted by motors and human hands in the tissue experiments.

Magnetic resonance imaging (MRI) is increasingly used for robotic needle-based clinical diagnosis and therapy due to its high resolution and high soft tissue contrast. A needle driver therefore becomes an essential part of these MRI -guided robotic systems to perform in-bore needle placement under continuous MR imaging. However, existing actuator designs for needle drivers are constrained by the high magnetic field and the bore size of the MR scanner, and most lack high- performance capability, especially in terms of large output force and long stroke. In this work, we introduce an MR-safe concentric bellows linear actuator (CBLA) with improved performance over the existing designs. This soft actuator achieves 45N output force, 77 % stroke-length ratio for the bellows, and around 250% stroke-diameter ratio. A mathematical model was built to characterize the behavior of the actuator to provide guidance for the actuator design. The fabrication and experimental evaluation of the actuator are also presented. The results demonstrate the effectiveness of the CBLA design and the strong potential of its application in MR-guided surgical procedures.

Most existing surgical robots employ straight rigid instruments and are not compatible with imaging modalities, especially magnetic resonance imaging (MRI) that presents restrictive constraints on the robot and actuator materials. Employing continuum distal end effector and fulfilling multi-imager compatibility will potentially lead to wide adoption of surgical robots in intraoperative image-guided minimally invasive surgery (MIS). This paper introduces a 3-dimensional (3D) printed polyamide continuum robot with 2-degree of freedom, driven by modular shape memory alloy (SMA) spring actuators that enable real-time distal manipulation in 3D workspace. The multi-imager compatibility is conditionally satisfied by utilizing no ferromagnetic materials and MRI-conditional actuators in the robotic system. The use of modular SMA allows repeatable configuration setting, easy integration with active cooling strategies, and facilitates the creation of a sterile barrier between the end effector module and the actuation module. Detailed design of the robot, kinematics modeling and actuator modeling are discussed in the paper. We experimentally verified the robot kinematics and evaluated the dynamic performance of the continuum robot. It features an operating bandwidth of 0.12 Hz at -3 dB and a root-mean-square error of 0.98 mm under model predictive control when tracking sinusoidal signals, both with ±10 mm amplitude (distal bending angle of ±80◦). As a demonstration, the robot was used as a flexible endoscope manipulator and steered under inputs from a joystick to show its real-time performance.