Modeling of path planning and needle steering with path tracking in anatomical soft tissues for minimally invasive surgery

Abstract

Steerable needles can potentially improve the effectiveness of diagnostic and therapeutic procedures, such as biopsy and cancer treatment, by increasing the targeting accuracy and reaching previously inaccessible targets. A discrete potential field algorithm based on three dimensional (3D) anatomical structures is proposed in this paper to plan the needle path in minimally invasive surgery. A 3D kinematic model of needle steering is formulated using Lie group theory. Model parameters are fitted using experimental data acquired via a 2-degree of freedom robotic device and an ultrasound imaging device. To execute the paths with variable curvatures, the model is incorporated with duty cycled spinning. Empirical formula between needle curvature and duty cycled factor is obtained through insertion experiments. To improve the targeting accuracy, a path tracking algorithm is developed by correcting for the heading error and cross-track error of the needle tip. The targeting error of the simulation is 0.29 mm. We experimentally evaluate the path tracking model and it achieves an average targeting error of 1.15 ± 0.56 mm in 3D environments with anatomical obstacles. The results of simulation are in agreement with steering experiments, showing that the discrete potential field algorithm and path tracking model have the potential to improve targeting accuracy and advance the therapeutic and diagnostic procedures.