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.