Towards human-controlled, real-time shape sensing based flexible needle steering for MRI-guided percutaneous therapies

Hybrid Deep Learning and Model-Based Needle Shape Prediction

Needle insertion using flexible bevel tip needles are a common minimally-invasive surgical technique for prostate cancer interventions. Flexible, asymmetric bevel tip needles enable physicians for complex needle steering techniques to avoid sensitive anatomical structures during needle insertion. For accurate placement of the needle, predicting the trajectory of these needles intra-operatively would greatly reduce the need for frequently needle reinsertions thus improving patient comfort and positive outcomes. However, predicting the trajectory of the needle during insertion is a complex task that has yet to be solved due to random needle-tissue interactions. In this paper, we present and validate for the first time a hybrid deep learning and model-based approach to handle the intra-operative needle shape prediction problem through, leveraging a validated Lie-group theoretic model for needle shape representation. Furthermore, we present a novel self-supervised learning and method in conjunction with the Lie-group shape model for training these networks in the absence of data, enabling further refinement of these networks with transfer learning. Needle shape prediction was performed in single-layer and double-layer homogeneous phantom tissue for C- and S-shape needle insertions. Our method demonstrates an average root-mean-square prediction error of 1.03 mm over a dataset containing approximately 3,000 prediction samples with maximum prediction steps of 110 mm.

Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches

Flexible needle insertion procedures are common in minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively. Through expansive research in fiber optics, a plethora of bio-compatible, MRI-compatible, optical shape-sensors have been developed to provide real-time shape feedback, such as single-core and multicore fiber Bragg gratings. In this paper, we directly compare single-core fiber-based and multicore fiber-based needle shape-sensing through similarly constructed, four-active area sensorized bevel-tip needles inserted into phantom and ex-vivo tissue on the same experimental platform. In this work, we found that for shape-sensing in phantom tissue, the two needles performed identically with a p-value of 0.164 > 0.05, but in ex-vivo real tissue, the single-core fiber sensorized needle significantly outperformed the multicore fiber configuration with a p-value of 0.0005 < 0.05. This paper also presents the experimental platform and method for directly comparing these optical shape sensors for the needle shape-sensing task, as well as provides direction, insight and required considerations for future work in constructively optimizing sensorized needles.

Concepts and Trends n Autonomy for Robot-Assisted Surgery

Surgical robots have been widely adopted with over 4000 robots being used in practice daily. However, these are telerobots that are fully controlled by skilled human surgeons. Introducing "surgeon-assist"-some forms of autonomy-has the potential to reduce tedium and increase consistency, analogous to driver-assist functions for lanekeeping, cruise control, and parking. This article examines the scientific and technical backgrounds of robotic autonomy in surgery and some ethical, social, and legal implications. We describe several autonomous surgical tasks that have been automated in laboratory settings, and research concepts and trends.

Clinical flexible needle puncture path planning based on particle swarm optimization

BACKGROUND AND OBJECTIVE

Percutaneous puncture with flexible needle has important value in clinical application for its low trauma and flexible path. However, it is difficult to guarantee accuracy and avoid obstacles in puncture process. Therefore, developing a viable path planning method is particularly important. In this research, the bending deformation law of flexible needles during puncture was studied and the puncture path was planned to provide a feasible method for clinical application of flexible needles.

METHODS

According to the researchs that have been conducted, the path of the flexible needle in the tissue could be considered as a circular arc stitching. This research studied the calculation method of arc radius and obtained its actual value by the puncture experiments. Based on the arc model, the spatial transformation method of three-dimensional path planning was studied. In addition, a simplified particle swarm optimization (PSO) was applied into the process of path planning by changing the central angle of arc and the rotation angle of the needle body.

RESULTS

The results of Experiment 1 showed that the path radius of the flexible needle pierced into gelatin prosthesis was influenced by four parameters, which were needle diameter, tip angle, puncture speed and the weight ratio of gelatin powder. As the needle diameter and tip angle increased, the radius of needle path tended to increase, but when the puncture speed and gelatin mass ratio increased, the radius of needle path decreased. The results of Experiment 2 showed that the distances of needle tip apart from target point were 3.2 mm (barrier-free experiments) and 1.8 mm (obstacle experiments).

CONCLUSIONS

This research links the intelligent algorithm with the flexible needle puncture tissue process, which has high value in future clinical application. By setting the correct boundary conditions and parameters, the flexible needle can be planned to reach the target point accurately.

Remotely Actuated Needle Driving Device for MRI-Guided Percutaneous Interventions: Force and Accuracy Evaluation

This paper presents a 2 degrees-of-freedom (DOF) remotely actuated needle driving device for Magnetic Resonance Imaging (MRI) guided pain injections. The device is evaluated in phantom studies under real-time MRI guidance. The force and torque asserted by the device on the 4-DOF base robot are measured. The needle driving device consists of a needle driver, a 1.2-meter long beaded chain transmission, an actuation box, a robot controller and a Graphical User Interface (GUI). The needle driver can fit within a typical MRI scanner bore and is remotely actuated at the end of the MRI table through a novel beaded chain transmission. The remote actuation mechanism significantly reduces the weight and size of the needle driver at the patient end as well as the artifacts introduced by the motors. The clinician can manually steer the needle by rotating the knobs on the actuation box or remotely through a software interface in the MRI console room. The force and torque resulting from the needle driver in various configurations both in static and dynamic status were measured and reported. An accuracy experiment in the MRI environment under real-time image feedback demonstrates a small mean targeting error (<; 1.5 mm) in a phantom study.

Remotely Actuated Needle Driving Device for MRI-Guided Percutaneous Interventions

In this paper we introduce a remotely actuated MRI-compatible needle driving device for pain injections in the lower back. This device is able to manipulate the needle inside the closed-bore MRI scanner under the control of the interventional radiologist inside both the scanner room and the console room. The device consists of a 2 degrees of freedom (DOF) needle driver and an actuation box. The 2-DOF needle driver is placed inside the scanner bore and driven by the actuation box settled at the end of the table through a beaded chain transmission. This novel remote actuation design could reduce the weight and profile of the needle driver that is mounted on the patient, as well as minimize the potential imaging noise introduced by the actuation electronics. The actuation box is designed to perform needle intervention in both manual and motorized fashion by utilizing a mode switch mechanism. A mechanical hard stop is also incorporated to improve the device's safety. The bench-top accuracy evaluation of the device demonstrated a small mean needle placement error (< 1 mm) in a phantom study.

Modelling and Experiment Based on a Navigation System for a Cranio-Maxillofacial Surgical Robot

In view of the characteristics of high risk and high accuracy in cranio-maxillofacial surgery, we present a novel surgical robot system that can be used in a variety of surgeries. The surgical robot system can assist surgeons in completing biopsy of skull base lesions, radiofrequency thermocoagulation of the trigeminal ganglion, and radioactive particle implantation of skull base malignant tumors. This paper focuses on modelling and experimental analyses of the robot system based on navigation technology. Firstly, the transformation relationship between the subsystems is realized based on the quaternion and the iterative closest point registration algorithm. The hand-eye coordination model based on optical navigation is established to control the end effector of the robot moving to the target position along the planning path. The closed-loop control method, “kinematics + optics” hybrid motion control method, is presented to improve the positioning accuracy of the system. Secondly, the accuracy of the system model was tested by model experiments. And the feasibility of the closed-loop control method was verified by comparing the positioning accuracy before and after the application of the method. Finally, the skull model experiments were performed to evaluate the function of the surgical robot system. The results validate its feasibility and are consistent with the preoperative surgical planning.