This repository contains:

1. The models for EIT image reconstruction which is published in EIT-CDAE and follow-up work.

2. Database constructed for training.

• EIT-image-reconstruction models

  • Dataset-preparation

  • Train

  • Performance

• Author
• License

The architecture of EIT image reconstruction networks, which contains: EIT-CDAE(model A), network to denoise measurement matrix(model B) and U-net for EIT image reconstruction.

There are 2 choices of datasets: using open-source dataset: montreal dataset of EIDORS or generate simulated dataset. Both need to use EIDORS toolkit. To generate the dataset, you need run data_gen.m in dataset file. An instance of images processed is as below:

The CDAE-EIT model was built using Tensorflow, please read detail in

code/cdae.py

The network to denoise measurement matrix(model B) was built using Tensorflow. This model using measurement matrix from open-source dataset with additional noise as input, original data as output. Then the denoised data can be used to reconstruct EIT images. Please read details in

code/matricDN.py

U-net for EIT image reconstruction was built using Tensorflow, details in:

code/U-net.py

• CDAE：

  1. Train and test the CDAE model with constructed database. The reconstructed images are as following:

​ 2. Testing results of the model with open-source database: Montreal database:

• results of the matrix denoise model :

​ (a): Original image; (b) Image with additional noise; (c): Results of the model's output

• U-net

  ​ 1. Results of constructed database

​ 2. Results of open-source database

• Yue Gao
• Yewangqing Lu
• Hui Li
• Boxiao Liu
• Mingyi Chen
• Guoxing Wang
• Yong Lian
• Yongfu Li*

Please cite these papers if you have used part of this work.

GAO Y, LU Y, LI H, et al. EIT-CDAE: a 2-D electrical impedance tomography image reconstruction method based on auto encoder technique[C]. IEEE Biomedical Circuits & Systems Conference, 2019: 1-4.
GAO Y, LIU B, LI H, et al. Live Demonstration: a pulmonary conditions monitor based on electrical impedance tomography measurement[C]. International Symposium on Circuits and Systems, 2019: 1-1.