Prediction of respiratory motion based on sequential embedding combined with relational embedding

Abstract

To propose a deep learning model for modeling and prediction of the integration of respiratory motion in all directions. The respiratory motion signals in different directions were input into the sequential embedding layer composed of LSTM to capture the sequential dependence of the historical motion state and obtain the sequential embedding representation, which enabled relational embedding in all directions through the self-attention mechanism to obtain the relational embedding representation. The sequential embedding representation and the relational embedding representation were concatenated and input into a prediction layer consisting of a fully connected neural network to generate nonlinear prediction components, which were added to the linear prediction components generated by the autoregressive module parallel to the above structure to generate the final prediction. The model was trained using a 'pre-training + fine-tuning' approach. In the validation experiments, 304 respiratory motion trajectories were used for model pre-training, and 7 evaluation samples were used for model testing. The proposed prediction model achieved more accurate prediction results than other methods. For the 7 evaluation samples with different delay time, the proposed prediction model achieved a reduction of absolute deviations in the 3D directions by over 70%. The proposed model is capable of accurate prediction of respiratory motion and can thus help to reduce system delay in precise radiotherapy.