基于神经网络的聚焦超声激励焦点温度控制

Xilun Liu, M. Almekkawy
{"title":"基于神经网络的聚焦超声激励焦点温度控制","authors":"Xilun Liu, M. Almekkawy","doi":"10.1109/LAUS53676.2021.9639179","DOIUrl":null,"url":null,"abstract":"High intensity focused ultrasound (HIFU), as a viable thermal tissue ablation approach, has recently increased its popularity. During the ablation process, the temperature rapidly increases and reaches above physiological normal temperature. The temperature versus time history is given by solving the bio-heat transfer equation (BHTE) with the initial condition and boundary conditions. Instead of using the traditional mesh-based algorithm, we introduce a physics-informed neural network (PINN) to solve the inverse control problem. In this work, unknown magnitudes of the deposition power can be estimated during the training process to ensure that the magnitude values can raise up to a predetermined temperature value at the target region.","PeriodicalId":156639,"journal":{"name":"2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Temperature Control of the Focal Point of Focused Ultrasound Excitation Using Neural Network Approach\",\"authors\":\"Xilun Liu, M. Almekkawy\",\"doi\":\"10.1109/LAUS53676.2021.9639179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High intensity focused ultrasound (HIFU), as a viable thermal tissue ablation approach, has recently increased its popularity. During the ablation process, the temperature rapidly increases and reaches above physiological normal temperature. The temperature versus time history is given by solving the bio-heat transfer equation (BHTE) with the initial condition and boundary conditions. Instead of using the traditional mesh-based algorithm, we introduce a physics-informed neural network (PINN) to solve the inverse control problem. In this work, unknown magnitudes of the deposition power can be estimated during the training process to ensure that the magnitude values can raise up to a predetermined temperature value at the target region.\",\"PeriodicalId\":156639,\"journal\":{\"name\":\"2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LAUS53676.2021.9639179\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LAUS53676.2021.9639179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

高强度聚焦超声(HIFU)作为一种可行的热组织消融方法,近年来越来越受到人们的欢迎。在消融过程中,温度迅速升高,达到生理正常温度以上。通过求解具有初始条件和边界条件的生物传热方程(BHTE),给出了温度随时间的变化历程。在传统的基于网格的控制算法中,我们引入了一种物理信息神经网络(PINN)来解决逆控制问题。在这项工作中,可以在训练过程中估计沉积功率的未知幅度,以确保该幅度值可以在目标区域上升到预定的温度值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temperature Control of the Focal Point of Focused Ultrasound Excitation Using Neural Network Approach
High intensity focused ultrasound (HIFU), as a viable thermal tissue ablation approach, has recently increased its popularity. During the ablation process, the temperature rapidly increases and reaches above physiological normal temperature. The temperature versus time history is given by solving the bio-heat transfer equation (BHTE) with the initial condition and boundary conditions. Instead of using the traditional mesh-based algorithm, we introduce a physics-informed neural network (PINN) to solve the inverse control problem. In this work, unknown magnitudes of the deposition power can be estimated during the training process to ensure that the magnitude values can raise up to a predetermined temperature value at the target region.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信