Evaluation of optical tracking and augmented reality for needle navigation in sacral nerve stimulation

Real needle for minimal invasive procedures training using motion sensors and optical flow

Minimally invasive percutaneous insertion procedures are widely used techniques in medicine. Their success is highly dependent on the skills of the practitioner. This paper presents a haptic simulator for training in these procedures, whose key component is a real percutaneous insertion needle with a sensory system incorporated to track its 3D location at every instant. By means of the proposed embedded vision system, the attitude (spatial orientation) and depth of insertion of a real needle are estimated. The proposal is founded on a novel depth estimation procedure based on optical flow techniques, complemented by sensory fusion techniques with the attitude calculated with data from an Inertial Measurement Unit (IMU) sensor. This procedure allows estimating the needle attitude with an accuracy of tenths of a degree and the displacement with an accuracy of millimeters. The computational algorithm runs on an embedded computer with real-time constraints for tracking the movement of a real needle. This haptic needle location data is used to reproduce the movement of a virtual needle within a simulation app. As a fundamental result, an ergonomic and realistic training simulator has been successfully constructed for healthcare professionals to acquire the mental model and motor skills necessary to practice percutaneous procedures successfully.

A robotic system for transthoracic puncture of pulmonary nodules based on gated respiratory compensation

BACKGROUND AND OBJECTIVE

With the urgent demands for rapid and precise localization of pulmonary nodules in procedures such as transthoracic puncture biopsy and thoracoscopic surgery, many surgical navigation and robotic systems are applied in the clinical practice of thoracic operation. However, current available positioning methods have certain limitations, including high radiation exposure, large errors from respiratory, complicated and time-consuming procedures, etc. METHODS: To address these issues, a preoperative computed tomography (CT) image-guided robotic system for transthoracic puncture was proposed in this study. Firstly, an algorithm for puncture path planning based on constraints from clinical knowledge was developed. This algorithm enables the calculation of Pareto optimal solutions for multiple clinical targets concerning puncture angle, puncture length, and distance from hazardous areas. Secondly, to eradicate intraoperative radiation exposure, a fast registration method based on preoperative CT and gated respiration compensation was proposed. The registration process could be completed by the direct selection of points on the skin near the sternum using a hand-held probe. Gating detection and joint optimization algorithms are then performed on the collected point cloud data to compensate for errors from respiratory motion. Thirdly, to enhance accuracy and intraoperative safety, the puncture guide was utilized as an end effector to restrict the movement of the optically tracked needle, then risky actions with patient contact would be strictly limited.

RESULTS

The proposed system was evaluated through phantom experiments on our custom-designed simulation test platform for patient respiratory motion to assess its accuracy and feasibility. The results demonstrated an average target point error (TPE) of 2.46 ± 0.68 mm and an angle error (AE) of 1.49 ± 0.45° for the robotic system.

CONCLUSIONS

In conclusion, our proposed system ensures accuracy, surgical efficiency, and safety while also reducing needle insertions and radiation exposure in transthoracic puncture procedures, thus offering substantial potential for clinical application.

Needle-tissue interaction model based needle path planning method

BACKGROUND AND OBJECTIVE

In needle insertion procedure, needle deflection and target movement will affect targeting accuracy. Existing planning algorithms rely on predetermined interaction force and parameters, which increase the targeting error for the patient-specific difference. In this paper, we proposed a needle-tissue interaction model based needle path planning method with patient-specific parameter identification algorithm, which is able to use iteration learning control and interaction model predicted information to improve targeting accuracy with the consideration of patient-specific differences.

METHODS

A 3D needle-tissue interaction deformation model has been constructed using local constraint method. The model, termed as the full computation model, predicts the needle-tissue interaction force using a Kriging-based model as well as the target movement and needle deflection simultaneously only requiring patient specific parameters. Needle paths without incorporating deformation, which is called static path, are generated by rapidly-exploring random trees algorithm first. Then, the needle-tissue interaction deformation model can calculate force and deformation of the static path and iterative learning control can correct the targeting error of moved target. In addition, the intraoperative parameter identification algorithm is proposed to identify patient-specific parameter. Simulations are carried out to verify the full computation model and needle path planning. A testbed is constructed and experiments are designed to validate the proposed method using phantom with common lesion-size obstacle markers and target markers. The deformation of tissue and needle are captured through charge coupled device camera.

RESULTS

Simulation results indicated the full computation model can simulate the needle-tissue interaction process and the proposed method can achieve needle path planning incorporating tissue deformation. Experiment results indicated the tissue deformation and needle deflection agree between model prediction and experiments. The proposed path planning method can reduce targeting error from maximum of 3.89 mm without incorporating deformation to less than 1 mm in 4 phantom experiments.

CONCLUSIONS

The full computation model based needle path planning is verified to be effective by experiments. The planning accuracy is improved based on the deformation predicted by full computation model and the desired accuracy is achieved.

Invasive and non-invasive electrodes for successful drug and gene delivery in electroporation-based treatments

Electroporation is an effective physical method for irreversible or reversible permeabilization of plasma membranes of biological cells and is typically used for tissue ablation or targeted drug/DNA delivery into living cells. In the context of cancer treatment, full recovery from an electroporation-based procedure is frequently dependent on the spatial distribution/homogeneity of the electric field in the tissue; therefore, the structure of electrodes/applicators plays an important role. This review focuses on the analysis of electrodes and in silico models used for electroporation in cancer treatment and gene therapy. We have reviewed various invasive and non-invasive electrodes; analyzed the spatial electric field distribution using finite element method analysis; evaluated parametric compatibility, and the pros and cons of application; and summarized options for improvement. Additionally, this review highlights the importance of tissue bioimpedance for accurate treatment planning using numerical modeling and the effects of pulse frequency on tissue conductivity and relative permittivity values.