An Optimized Forward Problem Solver for the Complete Characterization of the Electromagnetic Properties of Biological Tissues in Magnetic Induction Tomography

A new method for solving the magnetic induction tomography forward problem is presented. It is based on the combination of an OcTree type multi-level adaptive orthogonal mesh generation algorithm with a conformal finite integration technique-like formulation. The results obtained were compared with both analytical solutions and the response from a previously validated method. The presented method was proven to be capable of dealing with both complex electrical conductivity and magnetic permeability, giving a response with a low relative error, even when fairly coarse grids are used, thus reducing the overall system size and consequently the time for the attainment of the solution. This method is shown to be sensitive enough to be useful for image reconstruction in biological applications, where very small conductivities and relative permeabilities are usually found.

[1]  A. C. Serra,et al.  Improvements in the measurement system of a biological Magnetic Induction Tomographical experimental setup , 2010 .

[2]  A V Korjenevsky,et al.  Development of the system for visualization of electric conductivity distribution in human brain and its activity by the magnetic induction tomography (MIT) method , 2010 .

[3]  Manuchehr Soleimani,et al.  Absolute Conductivity Reconstruction in Magnetic Induction Tomography Using a Nonlinear Method , 2006, IEEE Transactions on Medical Imaging.

[4]  Stefan Heldmann,et al.  An octree multigrid method for quasi-static Maxwell's equations with highly discontinuous coefficients , 2007, J. Comput. Phys..

[5]  Rolf Schuhmann,et al.  解説 Discrete Electromagnetism by the Finite Integration Technique , 2002 .

[6]  O. Bíró.  Edge element formulations of eddy current problems , 1999 .

[7]  Paolo Mauriello,et al.  Principles of probability tomography for natural‐source electromagnetic induction fields , 1999 .

[8]  M. Soleimani,et al.  Image reconstruction in three-dimensional magnetostatic permeability tomography , 2005, IEEE Transactions on Magnetics.

[9]  Anthony J. Peyton,et al.  Development of Electromagnetic Tomography (EMT) for Industrial Applications. Part 1: Sensor Design and Instrumentation , 1999 .

[10]  Thomas Weiland,et al.  Magnetic field simulation using conformal FIT formulations , 2002 .

[11]  Hiroyuki Fukutomi,et al.  Fast Signal Predictions of Noised Signals in Eddy Current Testing. , 1999 .

[12]  M. Clemens,et al.  Numerical algorithms for the FDiTD and FDFD simulation of slowly varying electromagnetic fields , 1999 .

[13]  T. Weiland.  Time Domain Electromagnetic Field Computation with Finite Difference Methods , 1996 .

[14]  Nuno B Bras,et al.  A Fast Forward Problem Solver for the Reconstruction of Biological Maps in Magnetic Induction Tomography , 2010, IEEE Transactions on Magnetics.

[15]  A Korjenevsky,et al.  Magnetic induction tomography: experimental realization. , 2000, Physiological measurement.

[16]  H Scharfetter,et al.  Measurement of liver iron overload by magnetic induction using a planar gradiometer: preliminary human results. , 2004, Physiological measurement.

[17]  H Griffiths,et al.  Magnetic Induction Tomography: A Measuring System for Biological Tissues , 1999, Annals of the New York Academy of Sciences.