A magnetically controlled steerable robot with the capability of flexible navigation and intraoperative ultrasonic imaging and position sensing in neurosurgery is introduced in this paper. The robot system uses a piezoelectric transducer as the ultrasonic imaging beacon and applies a permanent magnet as the actuation unit, which can steer the flexible robot following a planned complex path to access a target in the brain. In order to enable the robot to navigate flexibly within tissues, a method is proposed to enhance magnetic actuation force by utilizing an array of small magnetic blocks instead of using a large magnet. The improvement of the array for magnetic field distribution was verified by simulation. Simulation studies also involved modeling the ultrasonic transmitting and receiving functions of the piezoelectric transducer to verify the feasibility of using them for intraoperative imaging. In addition, a prototype of the robot is fabricated and tested. The feasibility of the proposed magnetically controlled steerable robot for navigation in soft tissue is verified.

Minimally invasive surgeries (MIS) or natural orifice transluminal endoscopic surgeries (NOTES) such as the transurethral resection of bladder tumor (TURBT) require the surgical robot to be miniaturized to perform surgical procedures in confined spaces. However, the surgical robot's tiny size poses problems in its fabrication and shape sensing. In this paper, a miniature continuum surgical robot is proposed with a unique laminated structure which can be fabricated through a 2D lamination process and converted into 3D through folding. This multi-material laminated structure also facilitates the integration of tiny piezoelectric transducers on the robot's surface as beacons to generate ultrasonic waves for shape detection. A novel beacon total focusing method (b-TFM) algorithm is developed to process the received ultrasonic data and create a high-quality ultrasonic image from which the shape of the continuum robot can be extracted. The proposed robot and the ultrasonic shape detection method are validated through simulations and experiments. The error in the open-loop trajectory control is less than 4 mm without compensation, and the error in the ultrasonic shape detection is less than 1 mm. This confirms the possibility of improving the trajectory control accuracy by using the detected shape as a feedback for closed-loop control.

Accurate estimation of the blood flow velocity in ultrasound imaging is an important tool for medical diagnostics. In this paper, we adopt an improved empirical mode decomposition (EMD) framework called ensemble EMD (EEMD). To reduce the errors caused by the outliers in data when using a uniform weight in conventional EEMD, a regularized LASSO EEMD algorithm is proposed to solve for the multiple regression weights. An adaptive clutter rejection filter can then be designed to remove the clutter components. According to our simulation study, the proposed LASSO EEMD approach performs better than the state-of-the-art eigen-based and EMD method in estimating the blood flow velocity. Although the LASSO EEMD derived filter only achieves slightly better results than the cubic regression derived filters at most part of the simulated blood flow center frequencies, the proposed LASSO EEMD algorithm achieves much improved performance over cubic regression at extreme cases when the blood flow center frequency is close to or much higher than that of the clutter.