[1]邓煌森,刘捷,闫炼,等.基于深度卷积自编码器的微波成像方法及其医学应用潜力[J].中国医学物理学杂志,2025,42(2):184-189.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.007]
　DENG Huangsen,LIU Jie,YAN Lian,et al.Microwave imaging method based on deep convolutional autoencoder and its potential for medical application[J].Chinese Journal of Medical Physics,2025,42(2):184-189.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.007]
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基于深度卷积自编码器的微波成像方法及其医学应用潜力()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第2期
页码:
184-189
栏目:
医学影像物理
出版日期:
2025-01-20

文章信息/Info

Title:
Microwave imaging method based on deep convolutional autoencoder and its potential for medical application
文章编号:
1005-202X（2025）02-0184-06
作者:
邓煌森; 刘捷; 闫炼; 朱光正; 宁旭; 秦明新; 陈明生
陆军军医大学生物医学工程与影像医学系， 重庆 400038
Author(s):
DENG Huangsen;  LIU Jie;  YAN Lian;  ZHU Guangzheng;  NING Xu;  QIN Mingxin;  CHEN Mingsheng
School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing 400038, China
关键词:
脑卒中; 微波成像; 深度学习; 卷积自编码器
Keywords:
Keywords: stroke microwave imaging deep learning convolutional autoencoder
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2025.02.007
文献标志码:
A
摘要:
开发一个基于深度学习的微波成像模型，将散射电场直接映射为目标物体介电特性分布图像，并探索其在医学应用的潜在价值。采用二维时域有限差分法进行数值模拟来获取散射电场数据集；构建基于深度卷积自编码器成像模型，对两类目标物体进行成像研究；使用相对误差进行成像结果的定量评估，并分析成像模型对不同卒中类型的区分能力。结果表明基于深度卷积自编码器的成像网络在处理两种数值模型时均展现出色的成像性能。对于简单物体，该模型能准确定位并初步重建物体形状，平均相对误差为0.301 2；对于卒中模型，能较好地重建卒中区域的位置和形状，初步重建其他脑组织，平均相对误差为0.077 8。基于深度卷积自编码器的微波成像网络对快速准确重建图像很有前景，脑卒中检测的数值示例表明该方法在生物医学成像领域具有显著的应用潜力。
Abstract:
A deep learning based microwave imaging model which can directly map the scattered electric field to the dielectric property distribution image of the target object is developed, and its potential for medical applications is explored. The two-dimensional time-domain finite difference method is used for numerical simulation to obtain a dataset of scattered electric fields a deep convolutional autoencoder based imaging model is constructed to perform imaging studies on two types of target objects the imaging results are quantitatively evaluated using relative error, and the models ability to distinguish different types of strokes is also analyzed. The results show that the imaging network based on deep convolutional autoencoder exhibits excellent imaging performance when processing both numerical models. For simple objects, the model can accurately locate and preliminarily reconstruct the shape of the object, with an average relative error of 0.301 2, while for the stroke models, it can effectively reconstruct the location and shape of the stroke area, and preliminarily reconstruct other brain tissues, with an average relative error of 0.077 8. The microwave imaging network based on deep convolutional autoencoder has great promise for fast and accurate image reconstruction, and the numerical example of stroke detection demonstrates its significant application potential in biomedical imaging.

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备注/Memo

备注/Memo:
【收稿日期】2024-07-22 【基金项目】国家自然科学基金（62171444） 【作者简介】邓煌森，硕士，研究方向：脑卒中微波成像和电磁检测，E-mail: Chris_324@163.com 【通信作者】陈明生，博士，副教授，研究方向：智能脑损伤电磁检测，E-mail: chenms83@163.com
更新日期/Last Update: 2025-01-22