Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy

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. T...

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Bibliographic Details
Published inarXiv.org
Main Authors Gao, Riqiang, Ghesu, Florin C, Arberet, Simon, Basiri, Shahab, Kuusela, Esa, Kraus, Martin, Comaniciu, Dorin, Kamen, Ali
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.06.2024
Subjects
Artificial intelligence
Deep learning
Fluence
Machine learning
Multiagent systems
Optimization
Radiation therapy
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Summary: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.
ISSN:2331-8422