Improved Adaptive Mean Tracking Method for Respiratory Drifting

Respiratory gating technology for radiation therapy has become a hot research problem, which can ensure the healthy tissue involving minimal damage. One of the key problems of the respiratory gating technology is mean tracking method for respiratory signals drifting, to ensure the accuracy of gated area on radiation switch. Based on Kanatani hyperaccuracy ellipse fitting method, and the generalized eigenvalue decomposition problem we proposed an improved adaptive mean tracking method for respiratory signals drifting. Using our proposed algorithm, the respiratory signal can be estimated by ellipse model in statespace. In this paper, we can achieve the coordinates of the ellipse center, which corresponds to the mean of the respiratory signal which we need to estimate. It is robust to the lost data or pseudo data. In the experiment, we demonstrated that antinoise performance of our algorithm is brilliant in the estimation of the small amount data, due to using the Kanatani’s proposed method. It has also fixed the problem of ellipse fitting method in using only the generalized eigenvalue decomposition to find the corresponding eigenvectors of the largest eigenvalue.