Conductivity and current density image reconstruction using harmonicB_zalgorithm in magnetic resonance electrical impedance tomography

Abstract

Magnetic resonance electrical impedance tomography (MREIT) is to provide cross-sectional images of the conductivity distribution sigma of a subject. While injecting current into the subject, we measure one component Bz of the induced magnetic flux density B = (Bx, By, Bz) using an MRI scanner. Based on the relation between (inverted delta)2 Bz and inverted delta sigma, the harmonic Bz algorithm reconstructs an image of sigma using the measured Bz data from multiple imaging slices. After we obtain sigma, we can reconstruct images of current density distributions for any given current injection method. Following the description of the harmonic Bz algorithm, this paper presents reconstructed conductivity and current density images from computer simulations and phantom experiments using four recessed electrodes injecting six different currents of 26 mA. For experimental results, we used a three-dimensional saline phantom with two polyacrylamide objects inside. We used our 0.3 T (tesla) experimental MRI scanner to measure the induced Bz. Using the harmonic Bz algorithm, we could reconstruct conductivity and current density images with 82 x 82 pixels. The pixel size was 0.6 x 0.6 mm2. The relative L2 errors of the reconstructed images were between 13.8 and 21.5% when the signal-to-noise ratio (SNR) of the corresponding MR magnitude images was about 30. The results suggest that in vitro and in vivo experimental studies with animal subjects are feasible. Further studies are requested to reduce the amount of injection current down to less than 1 mA for human subjects.

Magnetic resonance electrical impedance tomography at 3 tesla field strength

“…Compared to previous results obtained with a 0.3 Tesla MRI scanner (14), the conductivity images presented here are significantly improved-mainly due to the much better SNR of the 3.0 Tesla MRI scanner. However, the harmonic B z algorithm is weak against the random noise in measured B z data, since numerical differentiations tend to amplify the noise.…”

“…A conductivity image reconstruction algorithm (called the harmonic B z algorithm) was recently proposed, which uses the following key identity (13,14):…”

“…In previous MREIT experimental studies (10 -12), the biggest problems were object rotations inside the magnet to acquire the three components of the internal magnetic field data, and low SNR of reconstructed images. However, new MREIT reconstruction algorithms were recently introduced, along with phantom imaging results obtained without any object rotations (13)(14)(15).In this work we present some results of MREIT phantom imaging experiments performed with the harmonic B z algorithm and a 3.0 Tesla MRI system. After briefly introducing the fundamental principle of the conductivity image reconstruction algorithm, we present the experimental results of the conductivity imaging performance evaluation.…”

“…In previous MREIT experimental studies (10 -12), the biggest problems were object rotations inside the magnet to acquire the three components of the internal magnetic field data, and low SNR of reconstructed images. However, new MREIT reconstruction algorithms were recently introduced, along with phantom imaging results obtained without any object rotations (13)(14)(15).…”

Anisotropic WM conductivity reconstruction based on diffusion tensor magnetic resonance imaging: a simulation study

“…The conductivity distribution image can be reconstructed based on the relationship between the conductivity and the measured magnetic flux density combined with the current density. MREIT reconstruction algorithms fall into two categories [10]: those utilizing internal current density [10][11][12][13][14] and those making use of measured magnetic flux density [15][16][17][18][19][20][21][22]. Considering the rotation problem of the object in the MRI system, the latter has the advantage of avoiding the object rotation over the former.…”