TY - GEN
T1 - Prediction of respiratory motion using wavelet based support vector regression
AU - Dürichen, R.
AU - Wissel, T.
AU - Schweikard, A.
PY - 2012/9/1
Y1 - 2012/9/1
N2 - In order to successfully ablate moving tumors in robotic radiosurgery, it is necessary to compensate the motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in CyberKnife®Synchrony. Due to time delays, errors occur which can be reduced by time series prediction. A new prediction algorithm is presented, which combines á trous wavelet decomposition and support vector regression (wSVR). The algorithm was tested and optimized by grid search on simulated as well as on real patient data set. For these real data, wSVR outperformed a wavelet based least mean square (wLMS) algorithm by >; 13% and standard Support Vector regression (SVR) by >; 7:5%. Using approximate estimates for the optimal parameters wSVR was evaluated on a data set of 20 patients. The overall results suggest that the new approach combines beneficial characteristics in a promising way for accurate motion prediction.
AB - In order to successfully ablate moving tumors in robotic radiosurgery, it is necessary to compensate the motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in CyberKnife®Synchrony. Due to time delays, errors occur which can be reduced by time series prediction. A new prediction algorithm is presented, which combines á trous wavelet decomposition and support vector regression (wSVR). The algorithm was tested and optimized by grid search on simulated as well as on real patient data set. For these real data, wSVR outperformed a wavelet based least mean square (wLMS) algorithm by >; 13% and standard Support Vector regression (SVR) by >; 7:5%. Using approximate estimates for the optimal parameters wSVR was evaluated on a data set of 20 patients. The overall results suggest that the new approach combines beneficial characteristics in a promising way for accurate motion prediction.
UR - https://www.scopus.com/pages/publications/84870699867
U2 - 10.1109/MLSP.2012.6349742
DO - 10.1109/MLSP.2012.6349742
M3 - Conference contribution
SN - 978-146731026-0
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
SP - 1
EP - 6
BT - 2012 IEEE International Workshop on Machine Learning for Signal Processing
PB - IEEE
T2 - 22nd IEEE International Workshop on Machine Learning for Signal Processing
Y2 - 23 September 2012 through 26 September 2012
ER -
SDG 3 – Gesundheit und Wohlergehen
SDG 9 – Industrie, Innovation und Infrastruktur