Algorithm for Predicting Respiratory Motion of Liver Tissue Based on Short-Term Respiratory Monitoring

In this study, an algorithm for predicting respiratory motion of liver tissue based on the combination of subject-specific external surrogate signals and 2D ultrasound image sequences was investigated to achieve better respiratory monitoring in clinical procedures. To achieve non-invasiveness in clinical procedures, an EM position tracker and a Doppler ultrasound diagnostic system were used as data collectors. Firstly, the mapping relationship between the magnetic sensing surrogate signal and the internal motion of liver tissue was learned by the Ridge regression model to achieve the estimation of the internal motion of liver tissue by the magnetic sensing surrogate signal; then the motion prediction of the estimated internal motion of liver tissue was performed by the artificial neural network (ANN) as the prediction filter; finally, the prediction of the respiratory motion of liver tissue by the magnetic sensing surrogate signal was achieved. Through experimental tests on 16 subject volunteers, the experimental results show that the RMSE of the proposed algorithm for predicting the respiratory motion of liver tissue is 2mm, indicating the potential of this prediction algorithm to achieve the localization of the internal motion position of liver tissue by the human magnetic sensing surrogate signal.