期刊
出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JTEHM.2016.2516005
关键词
Fuzzy deep learning; intra-fractional variation; inter-fractional variation; breathing prediction; tumor tracking
资金
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1054333] Funding Source: National Science Foundation
- NCATS NIH HHS [UL1 TR000058] Funding Source: Medline
Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra-and inter-fractional data variation, called intra-and inter-fraction fuzzy deep learning (IIFDL), where I-DL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the III-DL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the III-DL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of III-DL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据