التفاصيل البيبلوغرافية
| العنوان: |
Algorithm for Predicting Respiratory Motion of Liver Tissue Based on Short-Term Respiratory Monitoring. |
| المؤلفون: |
Feng H; College of Energy and Electrical Engineering, Hohai University, Nanjing Jiangsu, China., Zhou ZY; Suzhou Institute of Bioengineering Technology, Chinese Academy of Sciences, Suzhou Jiangsu, China., Dai YK; Suzhou Institute of Bioengineering Technology, Chinese Academy of Sciences, Suzhou Jiangsu, China. |
| المصدر: |
Studies in health technology and informatics [Stud Health Technol Inform] 2023 Nov 23; Vol. 308, pp. 549-555. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform |
| أسماء مطبوعة: |
Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991- |
| مواضيع طبية MeSH: |
Respiration* , Liver*/diagnostic imaging, Humans ; Motion ; Algorithms ; Neural Networks, Computer |
| مستخلص: |
In this study, an algorithm for predicting respiratory motion of liver tissue based on the combination of subject-specific external surrogate signals and 2D ultrasound image sequences was investigated to achieve better respiratory monitoring in clinical procedures. To achieve non-invasiveness in clinical procedures, an EM position tracker and a Doppler ultrasound diagnostic system were used as data collectors. Firstly, the mapping relationship between the magnetic sensing surrogate signal and the internal motion of liver tissue was learned by the Ridge regression model to achieve the estimation of the internal motion of liver tissue by the magnetic sensing surrogate signal; then the motion prediction of the estimated internal motion of liver tissue was performed by the artificial neural network (ANN) as the prediction filter; finally, the prediction of the respiratory motion of liver tissue by the magnetic sensing surrogate signal was achieved. Through experimental tests on 16 subject volunteers, the experimental results show that the RMSE of the proposed algorithm for predicting the respiratory motion of liver tissue is 2mm, indicating the potential of this prediction algorithm to achieve the localization of the internal motion position of liver tissue by the human magnetic sensing surrogate signal. |
| فهرسة مساهمة: |
Keywords: Breathing Exercise; Machine Learning; Predictive Filters; Ultrasound |
| تواريخ الأحداث: |
Date Created: 20231126 Date Completed: 20231128 Latest Revision: 20231128 |
| رمز التحديث: |
20231215 |
| DOI: |
10.3233/SHTI230883 |
| PMID: |
38007782 |
| قاعدة البيانات: |
MEDLINE |
