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Oh, S.H., Lee, B.I., Woo, E.J., Lee, S.Y., Kim, T.S., Kwon, O., et al. (2005) Electrical conductivity images of biological tissue phantom in MREIT. Physiological Measurement, 26(2), S279-S288.

has been cited by the following article:

• TITLE: Reconstruction of conductivity distribution of brain tissue from two components magnetic flux density

  AUTHORS: Wenlong Xu, Dandan Yan

  KEYWORDS: Magnetic Resonance Electrical Impedance Tomography; Magnetic Flux Density Measurement; Current Density; Conductivity Distribution

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.3 No.7, July 15, 2010

  ABSTRACT: In this paper the recent Magnetic resonance electrical impedance imaging (MREIT) technique is used to image non-invasively the three-dimensional continuous conductivity distribution of the head tissues. With the feasibility of the human head being rotated twice in the magnetic resonance imaging (MRI) system, a continuous conductivity reconstruction MREIT algorithm based on two components of the measured magnetic flux density is introduced. The reconstructed conductivity image could be obtained through solving iter- atively a non-linear matrix equation. According to the present algorithm of using two magnetic flux den- sity components, numerical simulations were per- formed on a concentric three-sphere and realistic human head model (consisting of the scalp, skull and brain) with the uniform and non-uniform isotropic target conductivity distributions. Based on the algorithm, the reconstruction of scalp and brain conductivity ratios could be figured out even under the condition that only one current is injected into the brain. The present results show that the three-dimensional continuous conductivity reconstruction method with two magnetic flux density components for the realistic head could get better results than the method with only one magnetic flux density component. Given the skull conductivity ratio, the relative errors of scalp and brain conductivity values were reduced to less than 1% with the uniform conductivity distribution and less than 6.5% with the non-uniform distribution for different noise levels. Furthermore, the algorithm also shows fast convergence and improved robustness against noise.