Rapid diagnosis and continuous monitoring of intracerebral hemorrhage with magnetic induction tomography based on stacked autoencoder

2021 ◽  
Vol 92 (8) ◽  
pp. 084707
Author(s):  
Ruijuan Chen ◽  
Yixiang Song ◽  
Juan Huang ◽  
Jinhai Wang ◽  
Hongsheng Sun ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Magnetic Induction ◽  
Continuous Monitoring ◽  
Rapid Diagnosis ◽  
Stacked Autoencoder ◽  
Magnetic Induction Tomography

Inspection of defects in CFRP by improved magnetic induction tomography

2019 ◽  
Vol 61 (3) ◽  
pp. 255-259
Author(s):  
Lipan Zhang ◽  
Qifeng Meng ◽  
Kai Song ◽  
Ming Gao ◽  
Zhiyuan Cheng
Keyword(s):  
Magnetic Induction ◽  
Magnetic Induction Tomography

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

Sign in / Sign up

Export Citation Format

Share Document