Authors:
Narges Ghasemi
1
;
2
;
Shahabedin Nabavi
1
;
Mohsen Ebrahimi Moghaddam
1
and
Yasser Shekofteh
1
Affiliations:
1
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
;
2
Department of Computer Science, Viterbi School of Engineering, University of Southern California, U.S.A.
Keyword(s):
Convolutional Long Short-Term Memory, Deep Neural Network, Lung Motion, Radiotherapy, Respiratory Motion Prediction.
Abstract:
One of the challenges of treating lung tumors in radiation therapy is the patient’s respiratory movements during the treatment, which lead to tumor motion. The goal of respiratory motion prediction is to predict the movements of lung tissues and lung tumors during the breathing cycle. Predicting respiratory movements allows radiation to be directed only at the tumor, minimizing exposure to healthy tissue and reducing the risk of side effects. Using 4D CT images, we can find the next position of the lung tumor and make a 4D radiation therapy plan. As obtaining 4D CT scans is harmful to the patient due to radiation, the aim of this study is to construct a 4D CT during a respiratory cycle using only a 3D image. In this paper, a Chaotic Convolutional Long Short-Term Memory network is proposed, which utilizes chaotic features in respiratory signals to predict pulmonary movements more accurately. The innovation of this method is paying attention to chaotic features of respiratory signals,
which leads to better interpretability of the presented model. The obtained results show that the proposed method has a higher learning speed and better performance compared to previous models, which generate 4D CT scans.
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