An image reconstruction method based on machine learning for dual-energy subtraction radiography

2008 ◽  
Author(s):  
Yoshiro Kitamura ◽  
Masahiko Yamada ◽  
Wataru Ito
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Dual Energy ◽  
Reconstruction Method ◽  
Image Reconstruction Method ◽  
Energy Subtraction

Image reconstruction method of dual energy CT for multilayer spherical shell with great density difference

2015 ◽  
Vol 27 (4) ◽  
pp. 44002
Author(s):  
夏惊涛 Xia Jingtao ◽  
王群书 Wang Qunshu ◽  
马继明 Ma Jiming ◽  
李斌康 Li Binkang ◽  
宋朝晖 Song Zhaohui ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Spherical Shell ◽  
Dual Energy ◽  
Density Difference ◽  
Dual Energy Ct ◽  
Reconstruction Method ◽  
Great Density ◽  
Image Reconstruction Method

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.

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