Tip estimation approach for concentric tube robots using 2D ultrasound images and kinematic model

Concentric tube robot (CTR) is an efficient approach for minimally invasive surgery (MIS) and diagnosis due to its small size and high dexterity. To manipulate the robot accurately and safely inside the human body, tip position and shape information need to be well measured. In this paper, we propose a tip estimation method based on 2D ultrasound images with the help of the forward kinematic model of CTR. The forward kinematic model can help to provide a fast ultrasound scanning path and narrow the region of interest in ultrasound images. For each tube, only three scan positions are needed by combining the kinematic model prediction as prior knowledge. After that, the curve fitting method is used for its shape reconstruction, while its tip position can be estimated based on the constraints of its structure and length.7 This method provides the advantage that only three scan positions are needed for estimating the tip of each telescoping section. Moreover, no structure modification is needed on the robot, which makes it an appropriate approach for existing flexible surgical robots. Experimental results verified the feasibility of the proposed method and the tip estimation error is 0.59 mm. Graphical abstract In this paper, we propose a tip estimation method based on 2D Ultrasound images with the help of the forward kinematic model of CTR. The forward kinematic model can help to provide a fast Ultrasound scanning path and narrow the region of interest in Ultrasound images. For each tube, only three scan positions are needed by combining the kinematic model prediction as prior knowledge. After that, the curve fitting method is used for its shape reconstruction, while its tip position can be estimated based on the constraints of its structure and length.

Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images

Flexible needles have been widely used in minimally invasive surgeries, especially in percutaneous interventions. Among the interventions, tip position of the curved needle is very important, since it directly affects the success of the surgeries. In this paper, we present a method to estimate the tip position of a long-curved needle by using 2D transverse ultrasound images from a robotic ultrasound system. Ultrasound is first used to detect the cross section of long-flexible needle. A new imaging approach is proposed based on the selection of numbers of pixels with a higher gray level, which can directly remove the lower gray level to highlight the needle. After that, the needle shape tracking method is proposed by combining the image processing with the Kalman filter by using 3D needle positions, which develop a robust needle tracking procedure from 1 mm to 8 mm scan intervals. Shape reconstruction is then achieved using the curve fitting method. Finally, the needle tip position is estimated based on the curve fitting result. Experimental results showed that the estimation error of tip position is less than 1 mm within 4 mm scan intervals. The advantage of the proposed method is that the shape and tip position can be estimated through scanning the needle’s cross sections at intervals along the direction of needle insertion without detecting the tip.

A simple insertion technique to reduce the bending of thinbevel-point needles

Objective: Needle insertion is a common component of most diagnostic and therapeutic interventions. Needles with asymmetrically sharpened points such as the bevel point are ubiquitous. Their insertion path is typically curved due to the rudder effect at the point. However, the common planned path is straight, leading to targeting errors. We present a simple technique that may substantially reduce these errors. The method was inspired by practical experience, conceived mathematically, and refined experimentally. Methods: Targeting errors are reduced by flipping the bevel on the opposite side (rotating the needle 180° about its axis), at a certain depth during insertion. The ratio of the flip depth to the full depth of insertion is defined as the flip depth ratio (FDR). Based on a model, FDR is constant 0.3. Results: Experimentally, the ratio depends on the needle diameter, 0.35 for 20Ga and 0.45 for 18Ga needles. Thinner needles should be flipped a little shallower, but never less than 0.3. Conclusion: Practically, a physician may expect to reduce ∼80% of needle deflection errors by simply flipping the needle. The technique may be used by hand or with guidance devices.

A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modelling

Global modelling has traditionally been the approach taken to estimate needle deflection in soft tissue. In this paper, we propose a new method based on local data-driven modelling of needle deflection. External measurement of needle-tissue interactions is collected from several insertions in ex vivo tissue to form a cloud of data. Inputs to the system are the needle insertion depth, axial rotations, and the forces and torques measured at the needle base by a force sensor. When a new insertion is performed, the just-in-time learning method estimates the model outputs given the current inputs to the needle-tissue system and the historical database. The query is compared to every observation in the database and is given weights according to some similarity criteria. Only a subset of historical data that is most relevant to the query is selected and a local linear model is fit to the selected points to estimate the query output. The model outputs the 3D deflection of the needle tip and the needle insertion force. The proposed approach is validated in ex vivo multilayered biological tissue in different needle insertion scenarios. Experimental results in five different case studies indicate an accuracy in predicting needle deflection of 0.81 and 1.24 mm in the horizontal and vertical lanes, respectively, and an accuracy of 0.5 N in predicting the needle insertion force over 216 needle insertions.