Journal of Medical Signals & Sensors

ORIGINAL ARTICLE
Year
: 2020  |  Volume : 10  |  Issue : 2  |  Page : 69--75

Respiratory motion prediction using deep convolutional long short-term memory network


Shahabedin Nabavi1, Monireh Abdoos1, Mohsen Ebrahimi Moghaddam1, Mohammad Mohammadi2 
1 Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
2 Department of Medical Physics, Royal Adelaide Hospital; Department of Medical Physics, School of Physical Sciences, The University of Adelaide, Adelaide, Australia

Correspondence Address:
Shahabedin Nabavi
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran
Iran

Background: Pulmonary movements during radiation therapy can cause damage to healthy tissues. It is necessary to adapt treatment planning based on tumor motion to avoid damage to healthy tissues. A range of approaches has been proposed to monitor the issue. A treatment planning based on fourdimensional computed tomography (4D CT) images can be addressed as one of the most achievable options. Although several methods proposed to predict pulmonary movements based on mathematical algorithms, the use of deep artificial neural networks has recently been considered. Methods: In the current study, convolutional long shortterm memory networks are applied to predict and generate images throughout the breathing cycle. A total of 3295 CT images of six patients in three different views was considered as reference images. The proposed method was evaluated in six experiments based on a leaveonepatientout method similar to crossvalidation. Results: The weighted average results of the experiments in terms of the rootmeansquared error and structural similarity index measure are 9 × 10^−3 and 0.943, respectively. Conclusion: Utilizing the proposed method, because of its generative nature, which results in the generation of CT images during the breathing cycle, improves the radiotherapy treatment planning in the lack of access to 4D CT images.


How to cite this article:
Nabavi S, Abdoos M, Moghaddam ME, Mohammadi M. Respiratory motion prediction using deep convolutional long short-term memory network.J Med Signals Sens 2020;10:69-75


How to cite this URL:
Nabavi S, Abdoos M, Moghaddam ME, Mohammadi M. Respiratory motion prediction using deep convolutional long short-term memory network. J Med Signals Sens [serial online] 2020 [cited 2020 Sep 29 ];10:69-75
Available from: http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2020;volume=10;issue=2;spage=69;epage=75;aulast=Nabavi;type=0