Li Jianqiao, Dai Xuesong, Li Peng
Faculty of Engineering, Monash University, Melbourne, Australian.
Automation College, Wuxi University, Wuxi, China.
PLoS One. 2025 Aug 13;20(8):e0329065. doi: 10.1371/journal.pone.0329065. eCollection 2025.
This paper enhances prostate brachytherapy robot accuracy by developing a needle deflection prediction model and a controlled puncturing strategy, addressing current challenges and trends. The study addresses the challenges in needle deflection prediction by proposing a correction force-based prediction model. The puncture control strategy comprises two phases: preoperative needle trajectory planning and intraoperative approach adjustment, both relying on corrective force. During operative adjustment, a model predicting and counteracting needle tip deflection ensures accurate corrective force application. An adaptive PID controller, utilizing Reinforcement Learning (RL), regulates corrective force for precise puncture accuracy. A dedicated experimental platform was constructed to validate the puncture control strategy for prostate seed implantation. The seed implantation's average error was 1.96 mm, with a standard error of 0.56 mm. Experiments show that correction force in the strategy significantly reduces tip deflection, enhancing seed implantation precision.
本文通过开发针偏转预测模型和可控穿刺策略，应对当前的挑战和趋势，提高了前列腺近距离治疗机器人的准确性。该研究通过提出基于校正力的预测模型来应对针偏转预测中的挑战。穿刺控制策略包括两个阶段：术前针轨迹规划和术中进针调整，两者均依赖于校正力。在手术调整期间，一个预测并抵消针尖偏转的模型可确保准确施加校正力。一个利用强化学习（RL）的自适应PID控制器调节校正力，以实现精确的穿刺精度。构建了一个专用实验平台来验证前列腺籽源植入的穿刺控制策略。籽源植入的平均误差为1.96毫米，标准误差为0.56毫米。实验表明，该策略中的校正力显著降低了针尖偏转，提高了籽源植入精度。
