Distance self-attention and long short-term memory model for predicting X-ray tube arcing in CT equipment
10.13929/j.issn.1003-3289.2025.04.031
- VernacularTitle:距离自注意力与长短期记忆模型预测CT设备X线管打火
- Author:
Haopeng ZHOU
1
;
Yuyao TANG
;
Changxi WANG
;
Kang LI
;
Zhenlin LI
Author Information
1. 四川大学电气工程学院,四川成都 610065;四川大学华西医院生物医学大数据研究院,四川成都 610041
- Publication Type:Journal Article
- Keywords:
deep learning;
tomography,X-ray computed;
self-attention
- From:
Chinese Journal of Medical Imaging Technology
2025;41(4):659-665
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a distance self-attention(DSA)and long short-term memory(LSTM)model and observe its value for predicting X-ray tube arcing in CT equipment.Methods CT equipment status data of internet of medical things were collected and preprocessed,then DSA-LSTM model based on model attention(MA)module and nonlinear attenuation distance factor was constructed,and its value for predicting X-ray tube arcing in CT equipment was analyzed compared with other models.Results Compared with other models,DSA-LSTM model had better comprehensive efficiency for predicting X-ray tube arcing in CT equipment.MA module and nonlinear attenuation distance factor could improve the predictive efficiency of DSA-LSTM model,and all included features contributed to the performance of DSA-LSTM model in a certain extent.Conclusion DSA-LSTM model could effectively predict X-ray tube arcing in CT equipment.
