Respiratory Motion Prediction Based on Mazimum Posterior Probability
For the radiotherapy, the tumor inside thorax or abdomen keep varying with respiration motion. Current technologies, e.g., respiratory gating and beam tracking, face great challenges in predicting the respiratory tumor motion. Whereas respiratory motion is changeful, traditional prediction model such as Linear Model, Kalman Filter, and so on, can not imitate the motion accurately. In this article, the probabilistic algorithm, combined with the state inference, is proposed in order to predict the respiration signal during treatment. The respiratory objects of eleven patients were employed in our work to validate the proposed method. The experimental results were satisfying in comparing with traditional methods, e.g., the method successfully dealed with various local variations in respiratory objects, and predicted the respiration with lower error and higher correctness rate of state inference, so much as the signals with different time latency.
radiation therapy respiratory motion prediction tumor-tracking mazimum posterior probability
Jun Yang Zhengbo Zhang Shoujun Zhou Hongnan Yin
Medical affairs office Chinese PLA 458 Hospital Guangzhou,China,510602 Department of Biomedical Engineering Chinese PLA General Hospital Beijing,China,100853 SHENZHEN Hyper Tech.Ltd.Shenzhen,China,518057 The Second Hospital of Heilongjiang Haerbin,China,150010
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）