Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
This work addresses a key bottleneck in radiotherapy planning for medical professionals, offering incremental improvements over existing optimization methods.
The paper tackles leaf sequencing in radiotherapy planning by proposing a multi-agent deep reinforcement learning model, which reduces fluence reconstruction errors and shows potential for faster convergence compared to a leading optimization-based sequencer.
In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed as Reinforced Leaf Sequencer (RLS) in a multi-agent framework for leaf sequencing. The RLS model offers improvements to time-consuming iterative optimization steps via large-scale training and can control movement patterns through the design of reward mechanisms. We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. Our findings reveal that the proposed RLS model can achieve reduced fluence reconstruction errors, and potential faster convergence when integrated in an optimization planner. Additionally, RLS has shown promising results in a full artificial intelligence RTP pipeline. We hope this pioneer multi-agent RL leaf sequencer can foster future research on machine learning for RTP.