Conductivity imaging with low level current injection using transversal J-substitution algorithm in MREIT.

An aim of magnetic resonance electrical impedance tomography (MREIT) is to visualize the internal current density and conductivity of the electrically imaged object by injecting current through electrodes attached to it. Due to a limited amount of injection current, one of the most important factors in MREIT is how to control the noise contained in the measured magnetic flux density data. This paper describes a new iterative algorithm called the transversal J-substitution algorithm which is robust to measured noise. As a result, the proposed transversal J-substitution algorithm considerably improves the quality of the reconstructed conductivity image under a low injection current. The relation between the reconstructed contrast of conductivity and the measured noise in the magnetic flux density is analyzed. We show that the contrast of first update of the conductivity with a homogeneous initial guess using the proposed algorithm has sufficient distinguishability to detect the anomaly. Results from numerical simulations demonstrate that the transversal J-substitution algorithm is robust to the noise. For practical implementations of MREIT, we tested real experiments in an agarose gel phantom using low current injection with amplitudes 1 mA and 5 mA to reconstruct the interior conductivity distribution.