A novel material point method (MPM) based needle-tissue interaction model

A method for predicting needle insertion deflection in soft tissue based on cutting force identification

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young's modulus (E ) and Poisson's ratio (ν ) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Low-velocity nail penetration response of muscle tissue and gelatin

Quantitative estimation of soft tissue injuries due to penetration of sharp objects is a challenging task for forensic pathologists. The severity of injury depends on the force required to penetrate the tissue. This study focuses on investigating the amount of force required to penetrate porcine muscle tissue and gelatin simulants (10 % wt) to mimic human muscle tissue when subjected to sharp objects like nail at velocities below 5 m/s. A custom-made experimental setup was used to examine the influence of penetration velocity and nail diameter on penetration forces. Images captured by a high-speed camera were used to track the position and velocity of the nail. A finite element (FE) model was established to simulate the penetration behavior of the tissue and gelatin. The FE simulations of the nail penetration were validated through direct comparison with the experimental results. In tissues as well as in the simulant, penetration forces were seen to increase with increasing nail diameter and velocity. Porcine muscle tissue showed 23.9-46.5 % higher penetration forces than gelatin simulants (10 % wt) depending on nail diameter and velocity; the difference being higher for higher nail diameter and velocity. The ranges of maximum penetration forces measured were 8.6-59.1 N for porcine muscle tissue and 6.8-34.9 N for gelatin simulant. This study helps to quantify injuries caused by sharp nails at low velocities and offers insights with potential applications in injury management strategies and forensic studies.

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.

Electro-localization method using a muscle conductive phantom for needle position detection towards medical training

Simulation in healthcare can help train, improve, and evaluate medical personnel's skills. In the case of needle insertion/manipulation inside the muscle during an nEMG examination, a training simulator Requires estimating the position of the needle to output the electrical muscle activity in real time according to the training plan. External cameras can be used to estimate the needle location; however, different error sources can make its implementation difficult and new medical sensing technologies are needed. This study introduces and demonstrates the feasibility of a conductive phantom that serves as the medium for needle insertion and senses the 3D needle position based on a technique named electro-localization for the first time. The proposed conductive phantom is designed so that different voltage distributions are generated in the phantom using electrodes placed on its borders. The needle is inserted in the phantom, and the recorded voltages are mapped to spatial coordinates using a finite element method (FEM)-based computational model of the conductive phantom to estimate the 3D needle tip position. Experimental and simulation results of phantom voltage distributions agreed. In 2D mapping (no depth consideration), the needle position error was 1.7 mm, which was marginally reduced if only the central area of the phantom was used (1.5 mm). In 3D mapping, the error was 4 mm. This study showed the feasibility of using a conductive muscle phantom as a new embedded sensor that estimates needle position for medical training of nEMG without relying on external sensors.