Respiratory Motion Prediction Using Deep Convolutional Long Short‑Term Memory Network
سال انتشار: 1399
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 83
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شناسه ملی سند علمی:
JR_JMSI-10-2_001
تاریخ نمایه سازی: 28 تیر 1402
چکیده مقاله:
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 (۴D 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 ۳۲۹۵ 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 ۹ × ۱۰^−۳ and ۰.۹۴۳, 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 ۴D CT images.
کلیدواژه ها:
Convolutional long short‑term memory ، deep neural network ، lung motion ، radiotherapy ، respiratory motion prediction
نویسندگان
Shahabedin Nabavi
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
Monireh Abdoos
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
Mohsen Ebrahimi Moghaddam
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
Mohammad Mohammadi
Department of Medical Physics, Royal Adelaide Hospital- Department of Medical Physics, School of Physical Sciences, The University of Adelaide, Adelaide, Australia