A new magnetic resonance electrical impedance tomography (MREIT) algorithm: the RSM-MREIT algorithm with applications to estimation of human head conductivity

Abstract

We have developed a new Magnetic Resonance Electrical Impedance Tomography (MREIT) algorithm, RSM-MREIT algorithm, for noninvasive imaging of electrical conductivity distribution using only one component of magnetic flux density. The proposed RSM-MREIT algorithm uses Response Surface Methodology (RSM) algorithm for optimizing the conductivity distribution through minimizing the errors between the measured and calculated magnetic flux density. A series of computer simulations have been conducted to assess the performance of the proposed RSM-MREIT algorithm to estimate electrical conductivity values of the scalp, the skull, and the brain tissue, in a three-shell piece-wise homogeneous head model. Computer simulation studies were conducted in both a spherical and realistic geometry head model with a single variable (the brain-toskull conductivity ratio) and three-variables (the conductivity of the brain, the skull, and the scalp). The relative error between the target and estimated head conductivity values were less than 12% for both the single-variable and three-variable simulations. These promising simulation results demonstrate the feasibility of the proposed RSM-MREIT algorithm in estimating electrical conductivity values in a piece-wise homogeneous head model of the human head, and suggest that the RSM-MREIT algorithm merits further investigation.

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

“…Some research [6,[28][29][30] showed that the realistic head model with anisotropic and inhomogeneous conductivity could improve the accuracy of the source location in EEG/MEG analysis. Presently, the MREIT algorithms [20,21] were used in human head only under the condition of the piece-wise homogeneous conductivities of the tissues. Some other MREIT algorithms were not suitable to reconstruct the conductivities of the complex head tissues.…”

“…In order to avoid the singularity occurring when r = r ' , B(r) is treated as a node variable and J(r ' ) is used at the centre of each finite element in Eq.3. The comparison between the analytical solution and the numerical solution by FEM method was performed [20,21] to indicate the feasibility of solving the forward problem using the FEM.…”

“…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.…”

“…For the head tissue conductivity, the relatively novel and concise Radial Basis Function (RBF) and Response Surface Methodology (RSM) MREIT algorithms [20,21] have been proposed to focus on the piece-wise homogeneous head tissue conductivity reconstruction. Therefore, RBF-MREIT approach was extended to realize the estimation of anisotropic WM conductivity distribution of the head tissues in this paper.…”

Level-set-based reconstruction algorithm for EIT lung images: first clinical results

“…The impedance distribution is then estimated based on the measured voltages and medium geometry. Some of typical applications of these techniques are for geophysical imaging [1,2,3], process monitoring [4,5], and functional imaging of the body [6,7].…”