A New Magnetic Resonance Electrical Impedance Tomography (MREIT) Algorithm: RSM-MREIT Algorithm with Applications to Estimation of Human Head Conductivity

摘要

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-to-skull 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.