Accuracy Compensation Based on Optimized Neural Network for a Robotic Patient Positioning System with 6 Degrees of Freedom Used in Proton Therapy System

Abstract

Proton therapy for tumor treatment is a typical application of nuclear technology. For proton therapy systems, robotic patient positioning systems (PPSs) are increasingly used because of their high flexibility and efficiency. Most robotic PPSs are developed based on industrial robots, which have good repeatability but low absolute position accuracy (1 to 3 mm) and do not satisfy the requirement of highly precise treatment. In this study, an optimized algorithm, named the Back Propagation Neural Network (BPNN) algorithm based on particle swarm optimization, is proposed to improve the performance of absolute positioning accuracy. A comparison of the training for the traditional BPNN and the optimized algorithm is presented. A series of experiments with different payload weights and tools is implemented to validate the performance of the proposed method. The training results show that the proposed method can improve the average predicted positioning error from 0.55 to 0.38 mm. The results of the experiment with a calibration tool show that the average position error is reduced from 4.10 to 0.32 mm. The results of the experiment with a carbon fiber couch top show that the average and maximal positioning errors are 0.35 and 0.77 mm, respectively. All the results verify the feasibility of the proposed method in this study in improving the position accuracy of the robotic PPS.