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在射频废除中对联电场和温度场的实时预测:一种物理集成的神经网络方法

Tianqi Lu¹, Jincheng Zou¹, Shiqing Zhao¹

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

International journal for numerical methods in biomedical engineering

|October 23, 2025

概括

一个新的物理集成的神经网络模型在射频消光 (RFA) 过程中快速预测电场和温度场. 这种人工智能方法可以实时监控精确,个性化的瘤治疗,克服传统模拟的计算限制.

科学领域:

生物医学工程生物医学工程
人工智能在医学中的应用
计算物理计算物理

背景情况:

射频除 (RFA) 是固体瘤的关键最小侵入性治疗方法.
在RFA期间实时监测热电效应对于处理精度至关重要.
现有的模拟方法是计算密集型,阻碍实时临床应用.

研究的目的:

开发一个物理集成的神经网络模型,用于在RFA期间实时预测合的电场和温度场.
为了克服传统RFA模拟的计算局限性.
通过快速反,实现精确,个性化的RFA治疗.

主要方法:

开发了一种混合深度学习模型,将DeepONet用于电力潜力和ConvLSTM用于温度预测.
该模型使用热电合有限元法 (FEM) 模拟的数据进行训练.
使用生物模仿幻影实验验验证了模型性能.

主要成果:

DeepONet模型实现了高精度,MAE为0.0241和MRE为1.44%.
对于关键温度区域,ConvLSTM模型表现出强的表现,MAE为0.0286,MRE为3.46%,Dice得分为0.9334 .
开发的模型可以在1秒内预测RFA过程,比传统方法快得多.

结论:

物理集成的神经网络模型提供了快速而准确的预测RFA热电效应.
这种人工智能驱动的方法可以在RFA过程中实现实时监控和精确的温度控制.
未来与控制算法的集成可以增强3D温度场采集和治疗个性化.

关键词:

深度学习是一种深度学习.数字双胞胎数字双胞胎是什么意思有限元素方法的有限元素方法.无线电频率剥离 (RFA) 是一种方法.热电合器的热电合器

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