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Ghasemi, Zahra, Samadi Miandoab, Payam. (1402). Feasibility Study of Convolutional Long Short Term Memory Network for Pulmonary Movement Prediction in CT Images. سامانه مدیریت نشریات علمی, 14(1), 55-66. doi: 10.31661/jbpe.v0i0.2105-1339
Zahra Ghasemi; Payam Samadi Miandoab. "Feasibility Study of Convolutional Long Short Term Memory Network for Pulmonary Movement Prediction in CT Images". سامانه مدیریت نشریات علمی, 14, 1, 1402, 55-66. doi: 10.31661/jbpe.v0i0.2105-1339
Ghasemi, Zahra, Samadi Miandoab, Payam. (1402). 'Feasibility Study of Convolutional Long Short Term Memory Network for Pulmonary Movement Prediction in CT Images', سامانه مدیریت نشریات علمی, 14(1), pp. 55-66. doi: 10.31661/jbpe.v0i0.2105-1339
Ghasemi, Zahra, Samadi Miandoab, Payam. Feasibility Study of Convolutional Long Short Term Memory Network for Pulmonary Movement Prediction in CT Images. سامانه مدیریت نشریات علمی, 1402; 14(1): 55-66. doi: 10.31661/jbpe.v0i0.2105-1339

Feasibility Study of Convolutional Long Short Term Memory Network for Pulmonary Movement Prediction in CT Images

Journal of Biomedical Physics and Engineering
دوره 14، شماره 1، اردیبهشت 2024، صفحه 55-66    اصل مقاله (2.2 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2105-1339
نویسندگان
Zahra Ghasemi* 1؛ Payam Samadi Miandoab2
1Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran
2Department of Medical Radiation Engineering, Amirkabir University of Technology, Tehran, Iran
چکیده
Background: During X-ray imaging, pulmonary movements can cause many image artifacts. To tackle this issue, several studies, including mathematical algorithms and 2D-3D image registration methods, have been presented. Recently, the application of deep artificial neural networks has been considered for image generation and prediction.
Objective: In this study, a convolutional long short-term memory (ConvLSTM) neural network is used to predict spatiotemporal 4DCT images.
Material and Methods: In this analytical analysis study, two ConvLSTM structures, consisting of stacked ConvLSTM models along with the hyperparameter optimizer algorithm and a new design of the ConvLSTM model are proposed. The hyperparameter optimizer algorithm in the conventional ConvLSTM includes the number of layers, number of filters, kernel size, epoch number, optimizer, and learning rate. The two ConvLSTM structures were also evaluated through six experiments based on Root Mean Square Error (RMSE) and structural similarity index (SSIM).
Results: Comparing the two networks demonstrates that the new design of the ConvLSTM network is faster, more accurate, and more reliable in comparison to the tuned-stacked ConvLSTM model. For all patients, the estimated RMSE and SSIM were 3.17 and 0.988, respectively, and a significant improvement can be observed in comparison to the previous studies. 
Conclusion: Overall, the results of the new design of the ConvLSTM network show excellent performances in terms of RMSE and SSIM. Also, the generated CT images with the new design of the ConvLSTM model show a good consistency with the corresponding references regarding registration accuracy and robustness.
کلیدواژه‌ها
Radiotherapy؛ Deep Learning؛ Convolutional Long Short Term Memory؛ Pulmonary Movement؛ Prediction Model؛ 4DCT؛ Diagnostic Imaging؛ Hyperparameter Optimization
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