Adaptive filtering to predict lung tumor motion during free breathing

Abstract

Breathing-induced tumor motion during radiation therapy can be compensated either by gating or correcting the pointing of the radiation beam, but these techniques involve time delays in the corrective response. We have analyzed the accuracy of adaptive filter algorithms in predicting tumor positions with sufficient lead time to compensate for these systematic delays. Tumor and chest motion during respiration has been recorded fluoroscopically for lung cancer patients, using gold fiducials implanted in the tumors to enhance visibility. The motions been analyzed for predictability up to 1.0 second in advance using tapped delay line, Kalman filter, and neural network filter algorithms. Breathing patterns are not stationary in time. Both internal tumor and external chest movement can show amplitude and period modulations during a 30 second interval. Tapped delay line and other stationary filters cannot compensate for the changes and consequently have poor predictability. The predictive accuracy of adaptive filters has little dependence on the type of algorithm, but depends mainly on the frequency of updating and deteriorates rapidly when predicting more than 0.2 seconds in advance of the breathing signal. Longer-period (e.g., 30 seconds) variability in breathing requires frequent adaptation of the filter parameters.