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Image reconstruction for magnetic induction tomography
Manuchehr Soleimani1, Karen Jersey-Willuhn
1INOTECH Med. Syst., Carlsbad, CA 92009, USA. m.soleimani@inotechmedsys.com
Summary
This study presents the first non-linear magnetic induction tomography (MIT) reconstruction. The method accurately reconstructs electric conductivity distribution using a regularized Gauss-Newton approach for inverse problems.
Area of Science:
- Biomedical Engineering
- Electrical Engineering
- Applied Physics
Background:
- Magnetic induction tomography (MIT) is an imaging technique used to visualize conductivity distributions.
- Image reconstruction in MIT is a challenging non-linear, ill-conditioned inverse problem.
- Iterative methods are commonly employed for conductivity distribution reconstruction.
Purpose of the Study:
- To develop and present the first non-linear reconstruction scheme for magnetic induction tomography.
- To reconstruct the electric conductivity distribution using an advanced iterative method.
Main Methods:
- Utilized an iterative approach for image reconstruction.
- Employed a non-linear regularized Gauss-Newton method.
- Calculated both the forward solution and the Jacobian matrix during reconstruction.
Main Results:
- Successfully reconstructed the electric conductivity distribution.
- Demonstrated the efficacy of the non-linear Gauss-Newton method for MIT.
- Established a novel non-linear scheme for MIT reconstruction.
Conclusions:
- The non-linear regularized Gauss-Newton method is effective for magnetic induction tomography image reconstruction.
- This work represents a significant advancement as the first MIT reconstruction utilizing a non-linear scheme.
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