Microwave imaging method based on deep convolutional autoencoder and its potential for medical application
10.3969/j.issn.1005-202X.2025.02.007
- VernacularTitle: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
- Author:
Huangsen DENG
1
;
Jie LIU
1
;
Lian YAN
1
;
Guangzheng ZHU
1
;
Xu NING
1
;
Mingxin QIN
1
;
Mingsheng CHEN
1
Author Information
1. 陆军军医大学生物医学工程与影像医学系,重庆 400038
- Publication Type:Journal Article
- Keywords:
stroke;
microwave imaging;
deep learning;
convolutional autoencoder
- From:
Chinese Journal of Medical Physics
2025;42(2):184-189
- CountryChina
- Language:Chinese
-
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 model's 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 0.3012,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.
