Electrical Resistance Tomography Image Reconstruction With Densely Connected Convolutional Neural Network

2021 ◽  
Vol 70 ◽  
pp. 1-11
Author(s):  
Feng Li ◽  
Chao Tan ◽  
Feng Dong
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Electrical Resistance ◽  
Electrical Resistance Tomography ◽  
Tomography Image

Ultrasound transmission tomography image reconstruction with a fully convolutional neural network

2020 ◽  
Vol 65 (23) ◽  
pp. 235021
Author(s):  
Wenzhao Zhao ◽  
Hongjian Wang ◽  
Hartmut Gemmeke ◽  
Koen W A van Dongen ◽  
Torsten Hopp ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Transmission Tomography ◽  
Tomography Image

scholarly journals Electrical Impedance Tomography Image Reconstruction using Convolutional Neural Network with Periodic Padding

2021 ◽  
Vol 54 (15) ◽  
pp. 418-423
Author(s):  
Guilherme C. Duran ◽  
André K. Sato ◽  
Edson K. Ueda ◽  
Rogério Y. Takimoto ◽  
Thiago C. Martins ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Tomography Image ◽  
Electrical Impedance Tomography Image

scholarly journals Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1058
Author(s):  
Zhuoran Chen ◽  
Gege Ma ◽  
Yandan Jiang ◽  
Baoliang Wang ◽  
Manuchehr Soleimani
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Supervised Learning ◽  
Electrical Resistance ◽  
Learning Algorithm ◽  
Electrical Resistance Tomography ◽  
Two Phase ◽  
Regularization Algorithm ◽  
Capacitively Coupled ◽  
Tv Regularization

A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse image reconstruction was realized by a CNN-based supervised learning algorithm, as well as the well-known total variation (TV) regularization algorithm for comparison. The 2D, monochrome, 2500-pixel image was divided into 625 clusters, and each cluster was used individually to train its own CNN to solve the 16 classes classification problem. Inherent regularization for the assumption of binary materials enabled us to use a classification algorithm with CNN. The iterative TV regularization algorithm achieved a close state of the two-phase material reconstruction by its sparsity-based assumption. The supervised learning algorithm established the mathematical model that mapped the simulated resistance measurement to the pixel patterns of the clusters. The training process was carried out only using simulated measurement data, but simulated and experimental tests were both conducted to investigate the feasibility of applying a multi-layer CNN for CCERT imaging. The performance of the CNN algorithm on the simulated data is demonstrated, and the comparison between the results created by the TV-based algorithm and the proposed CNN algorithm with the real-world data is also provided.

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