Force prediction and tracking for image-guided robotic system using neural network approach

Abstract

In contemporary brachytherapy procedure, needle placement at desired location is challenging due to a variety of reasons. We have designed a robot-assisted brachytherapy system to improve needle placement and seed delivery. In this paper, we have used neural network (NN) for predicting insertion force during prostate brachytherapy. The NN controller computes control inputs required for optimizing the robotic system. To verify efficacy of the control system we used in-vivo motion and force measurements during actual brachytherapy needle insertion while radioactive seeds were implanted in the prostate gland, as a real-time controller input signal. Both force prediction and force tracking processes are investigated. Information about insertion force values are used to adjust other insertion parameters like insertion velocity or acceleration in order to minimize the insertion force.