Hybrid control algorithm for flexible needle steering: Demonstration in phantom and human cadaver

A Survey of Needle Steering Approaches in Minimally Invasive Surgery

In virtue of a curved insertion path inside tissues, needle steering techniques have revealed the potential with the assistance of medical robots and images. The superiority of this technique has been preliminarily verified with several maneuvers: target realignment, obstacle circumvention, and multi-target access. However, the momentum of needle steering approaches in the past decade leads to an open question-"How to choose an applicable needle steering approach for a specific clinical application?" This survey discusses this question in terms of design choices and clinical considerations, respectively. In view of design choices, this survey proposes a hierarchical taxonomy of current needle steering approaches. Needle steering approaches of different manipulations and designs are classified to systematically review the design choices and their influences on clinical treatments. In view of clinical consideration, this survey discusses the steerability and acceptability of the current needle steering approaches. On this basis, the pros and cons of the current needle steering approaches are weighed and their suitable applications are summarized. At last, this survey concluded with an outlook of the needle steering techniques, including the potential clinical applications and future developments in mechanical design.

Flexible Needle Steering with Tethered and Untethered Actuation: Current States, Targeting Errors, Challenges and Opportunities

Accurate needle targeting is critical for many clinical procedures, such as transcutaneous biopsy or radiofrequency ablation of tumors. However, targeting errors may arise, limiting the widespread adoption of these procedures. Advances in flexible needle (FN) steering are emerging to mitigate these errors. This review summarizes the state-of-the-art developments of FNs and addresses possible targeting errors that can be overcome with steering actuation techniques. FN steering techniques can be classified as passive and active. Passive steering directly results from the needle-tissue interaction forces, whereas active steering requires additional forces to be applied at the needle tip, which enhances needle steerability. Therefore, the corresponding targeting errors of most passive FNs and active FNs are between 1 and 2 mm, and less than 1 mm, respectively. However, the diameters of active FNs range from 1.42 to 12 mm, which is larger than the passive steering needle varying from 0.5 to 1.4 mm. Therefore, the development of active FNs is an area of active research. These active FNs can be steered using tethered internal direct actuation or untethered external actuation. Examples of tethered internal direct actuation include tendon-driven, longitudinal segment transmission and concentric tube transmission. Tendon-driven FNs have various structures, and longitudinal segment transmission needles could be adapted to reduce tissue damage. Additionally, concentric tube needles have immediate advantages and clinical applications in natural orifice surgery. Magnetic actuation enables active FN steering with untethered external actuation and facilitates miniaturization. The challenges faced in the fabrication, sensing, and actuation methods of FN are analyzed. Finally, bio-inspired FNs may offer solutions to address the challenges faced in FN active steering mechanisms.

Steerable needles for radio-frequency ablation in cirrhotic livers

Accurate needle placement in deep-seated liver tumours can be difficult. In this work, we disclose two new manually controlled steerable needles for 17G radio-frequency ablation probe placement. The needles contain stylets with embedded compliant joints for active tip articulations, and concentric tubes for (curved-path) guidance. Needle steering was evaluated sequentially by intended users and in intended-use tissue types. Six interventional radiologists evaluated the needle in repeated ultrasound-guided steering tasks in liver-mimicking phantoms. Targets were located at a 100 mm depth and 20 mm lateral offset from the initial insertion line. The resulting mean absolute tip placement error was 1.0 ± 1.0 mm. Subsequently, steering-induced tissue damage was evaluated in fresh cirrhotic human liver explants. The surface area of puncture holes was estimated in scanned histology slides, using a connected-components analysis. The mean surface area was 0.26 ± 0.16 mm² after steering with a median radius of curvature of 0.7 × 10³ mm, versus 0.35 ± 0.15 mm² after straight-path insertions with the steerable needle and 0.15 ± 0.09 mm² after straight-path RFA probe insertions. The steering mechanisms proposed enable clinically relevant path corrections for 17G needles. Radiologists were quickly adept in curved-path RFA probe placement and the evaluation of histological tissue damage demonstrated a potentially safe use during liver interventions.

Dynamic path planning for percutaneous procedures in the abdomen during free breathing

PURPOSE

Percutaneous procedures are increasingly used for the treatment of tumors in abdominal structures. Most of the time, these procedures are planned based on static preoperative images and do not take into account any motions, while breathing control is not always applicable. In this paper, we present a method to automatically adjust the planned path in real time according to the breathing.

METHODS

First, an estimation of the organs motions during breathing is performed during an observation phase. Then we propose an approach named Real Time Intelligent Trajectory (RTIT) that consists in finding the most appropriate moments to push the needle along the initially planned path, based on the motions and the distance to surrounding organs. We also propose a second approach called Real Time Straight Trajectory (RTST) that examines sixteen scenarios of needle insertion at constant speed, starting at eight different moments of the breathing cycle with two different speeds.

RESULTS

We evaluated our methods on six 3D models of abdominal structures built using image datasets and a real-time simulation of breathing movements. We measured the deviation from the initial path, the target positioning error, and the distance of the actual path to risky structures. The path proposed by RTIT approach is compared to the best path proposed by RTST.

CONCLUSIONS

We show that the RTIT approach is relevant and adapted to breathing movements. The modification of the path remains minimal while collisions with obstacles are avoided. This study on simulations constitutes a first step towards intelligent robotic insertion under real-time image guidance.

Developing a novel force forecasting technique for early prediction of critical events in robotics

Safety critical events in robotic applications can often be characterized by forces between the robot end-effector and the environment. One application in which safe interaction between the robot and environment is critical is in the area of medical robots. In this paper, we propose a novel Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) technique to predict future values of any time-series sensor data, such as interaction forces. Existing time series forecasting methods have high computational times which motivates the development of a novel technique. Using Autoregressive Integrated Moving Average (ARIMA) forecasting as benchmark, the performance of the proposed model was evaluated in terms of accuracy, computation efficiency, and stability on various force profiles. The proposed algorithm was 11% more accurate than ARIMA and maximum computation time of CFDL-MFP was 4ms, compared to ARIMA (7390ms). Furthermore, we evaluate the model in the special case of predicting needle buckling events, before they occur, by using only axial force and needle-tip position data. The model was evaluated experimentally for robustness with steerable needle insertions into different tissues including gelatin and biological tissue. For a needle insertion velocity of 2.5mm/s, the proposed algorithm was able to predict needle buckling 2.03s sooner than human detections. In biological tissue, no false positive or false negative buckling detections occurred and the rates were low in artificial tissue. The proposed forecasting model can be used to ensure safe robot interactions with delicate environments by predicting adverse force-based events before they occur.