Abstract # 2900 Correcting For Intra-fraction Breathing Motion Based On Offline And Online Analysis Of Respiratory Patterns

Presenter: Xu, Jun

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A model based analysis of the breathing signal was performed to help predict motion associated with respiration. Two adaptive filters, the Kalman filter and linear predictive filter were implemented to predict the online breathing signal. The patient breath signal was acquired using a Varian Real-time Position Management (RPM) with a sample frequency 30 Hz. To remove the noise from the signal, the first-order smoothing filter was used. For the offline analysis, the piecewise cosine function model was used as basic model using a partitioned nonlinear least squares algorithm to perform the model based analysis. For online prediction, the time lag was set to 200 ms. Both the Kalman filter and Linear Predictive filter were applied to evaluate the model’s ability to predict the breathing signal.