A Respiratory Motion Prediction Method Based on LSTM Combined with Weighted Attention Mechanism

Developing a high-precision respiratory motion prediction model is crucial to optimize radiotherapy accuracy. This paper proposes a respiratory motion prediction method based on a Long Short-term Memory (LSTM) network with a weighted attention mechanism. Using the temporal modeling capability of LSTM and the adaptive weight allocation strategy of the attention mechanism, the proposed model effectively captures the nonlinear dynamic characteristics of respiratory signals. Experimental results demonstrate that, compared to conventional prediction methods, the proposed approach achieves lower prediction errors across multiple datasets and exhibits strong generalization capability, providing a novel technical solution for high-precision real-time tracking in radiotherapy.