The correct orientation of an ultrasound (US) probe is one of the main parameters governing the US image quality. With the rise of robotic ultrasound systems (RUSS), methods that can automatically compute the orientation promise repeatable, automatic acquisition from predefined angles resulting in high-quality US imaging. In this paper, we propose a method to automatically position a US probe orthogonally to the tissue surface, thereby improving sound propagation and enabling RUSS to reach predefined orientations relatively to the surface normal at the contact point. The method relies on the derivation of the underlying mechanical model. Two rotations around orthogonal axes are carried out, while the contact force is being recorded. Then, the force data are fed into the model to estimate the normal direction. Accordingly, the probe orientation can be computed without requiring visual features. The method is applicable to the convex and linear probes. It has been evaluated on a phantom with varying tilt angles and on multiple human tissues (forearm, upper arm, lower back, leg). As a result, it has outperformed existing methods. The mean (±SD) absolute angular difference on in-vivo tissues averaged over all anatomies and probe types is 2.9±1.6 and 2.2±1.5 on the phantom.

Ultrasound examination is widely used for diagnosis of breast cancer, which requires a full-coverage scan of the whole organ and stable probe–breast interaction for high-quality image acquisition. In this study, a robotic system for automated breast ultrasound scanning is proposed. To avoid occlusion, point cloud of the breast is obtained from multiple angles and registered together for accurate tissue shape reconstruction, then scan path planning is performed with isometric 3-D point cloud searching algorithm for full and uniform coverage. To reduce the adverse influence of large tissue deformation during ultrasound scanning, a contact force–strain regression model is built for tissue deformation estimation and used as a feedforward to correct the planned scanning path, and a probe–tissue interaction model with scanning resistance is built for normal probe orientation adjustment. To maintain normal probe orientation and stable contact force during robotic ultrasound scanning, a hybrid force–velocity control framework is proposed based on admittance control algorithm. Experiment results show that the robotic system can successfully maintain a constant contact force and keep the probe's posture along the normal orientation of probe–breast interaction both in in-plane direction and out-of-plane direction.

Natural orifice transluminal endoscopic surgery (NOTES) has received significant attentions due to its minimal incision trauma compared with traditional multi-port robot assisted surgery. Continuum robot can be used in NOTES due to its high flexibility which can adapt to circuitous paths. However, the modeling of continuum robot is complex due to its nonlinear dynamics, especially with uncertain external disturbance. In this letter a hybrid adaptive control framework is proposed, combining offline trained robot inverse kinematics with neural network and online adaptive adjustment of PID controller parameters with another neural network, providing additional driving amount to the robot system to compensate the positioning error caused by external disturbance. Path tracking and point tracking experiments with external load are conducted and the results validate that the proposed hybrid adaptive control framework can compensate uncertain factors such as friction, driving tendon relaxation and external load during robot movement, and obtain relatively high positioning accuracy (maximum absolute error less than 2 mm) with quickresponse.

Goal: In the context of robot-assisted laminectomy surgery, an analytical force model is introduced to guarantee procedural safety. The aim of the method is to intraoperatively monitor the cutting depth via modeling the milling status. Methods: The theoretical dynamic model for the surgical milling process is based on the flute geometry of the ball-end milling tool. A particle swarm optimization algorithm is exploited to calibrate the model using the local average force, and to validate it using the denoised dynamic force. A wear detection method based on the fast Fourier transform is proposed to determine the quality of the tool geometry and to avoid using worn tools, which may lead to imprecise and unsafe operations. Results: Milling experiments were performed on machined fresh bovine femur bones. The experimental results thus obtained from the mechanical model are in good accordance with the numerical model. The proposed method can monitor the current cutting depth with an accuracy of ±0.1 mm in regions located within the depth [0.8-1.2 mm], and ±0.2 mm within [1.2-1.6 mm]. Conclusion: The proposed model can successfully estimate the milling force and the cutting depth intraoperatively in experimental conditions. Significance: This approach has the potential to improve the safety of laminectomy operations in humans, and make it more accessible to younger surgeons by lowering the required manual skills threshold.

Due to good fixation and obvious clinical advantages in the treatment of spinal diseases, pedicle screw technology is widely employed in spinal surgery. However, most screw placement paths are manually selected, which is a complicated and time-consuming process. Therefore, various kinds of spine surgery robots have been invented to assist during surgeries. An automatic path planning method of pedicle screw placement is proposed in this article. First, based on preoperative CT scans, the spine of the patient can be segmented and each individual vertebra is classified by using a trained deep learning network model. Additionally, a local coordinate system in every single vertebra is established according to its anatomical characteristics. Second, after analyzing the images to recognize the feature points, the pedicle region and route of screw placement are identified, which renders completion of the surgical path planning automatic. The experimental results show that 93% of the planning results can be directly applied in surgeries after surgeon evaluation. In addition to the improvement in accuracy, the whole algorithm does not rely on any human assistance. This fully automatic process greatly reduces the time required for surgical planning.