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Improving respiratory signal prediction with a deep neural network and simple changes to the input and output data format
https://repo.qst.go.jp/records/2001127
https://repo.qst.go.jp/records/20011273dc929af-45be-4fb3-8be1-35c39dd9ef71
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||
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| 公開日 | 2024-04-11 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Improving respiratory signal prediction with a deep neural network and simple changes to the input and output data format | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 著者 |
Hirai Ryusuke
× Hirai Ryusuke
× Mori Shinichiro
× Suyari Hiroki
× Ishikawa Hitoshi
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Objective. To improve respiratory gating accuracy and radiation treatment throughput, we developed a generalized model based on a deep neural network (DNN) for predicting any given patient’s respiratory motion. Approach. Our model uses long short-term memory (LSTM) based on a recurrent neural network (RNN), and improves upon common techniques. The first improvement is that the data input is not a one-dimensional sequence, but two-dimensional block data. This shortens the input sequence length, reducing computation time. Second, the output is not a scalar, but a sequence prediction. This increases the amount of available data, allowing improved prediction accuracy. For training and evaluation of our model, 434 sets of real-time position management (RPM) data were retrospectively collected from clinical studies. The data were separated in a ratio of 4:1, with the larger set used for training models and the remaining set used for testing. We measured the accuracy of respiratory signal prediction and amplitude-based gating with prediction windows equaling 133, 333, and 533 msec. This new model was compared with the original LSTM and a non-recurrent DNN model. Main results. The mean absolute errors (MAEs) with the prediction window at 133, 333 and 533 msec were 0.036, 0.084, 0.119 with our model; 0.049, 0.14, 0.246 with the original LSTM-based model; and 0.041, 0.119, 0.16 with the non-recurrent DNN model, respectively. The computation time were 0.66 msec with our model; 0.63 msec the original LSTM-based model; 1.60 msec the non-recurrent DNN model, respectively. The accuracies of amplitude-based gating with the same prediction window settings and a duty cycle of approximately 50% were 98.3%, 95.8% and 92.7% with our model, 97.6%, 93.9% and 87.2% with the original LSTM-based model; and 97.9%, 94.3% and 89.5% with the non- recurrent DNN model, respectively. Significance. Our RNN algorithm for respiratory signal prediction successfully estimated tumor positions. We believe it will be useful in respiratory signal prediction technology. |
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| 書誌情報 |
PHYSICS IN MEDICINE AND BIOLOGY 巻 69, p. 085023, 発行日 2024-04 |
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| 出版者 | ||||||||||||||
| 出版者 | IOP Publishing | |||||||||||||
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| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 1361-6560 | |||||||||||||
| DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1088/1361-6560/ad2b92 | |||||||||||||
