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Magnetic Resonance Electrical Impedance Tomography

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Electrical Properties of Tissues

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1380))

Abstract

Magnetic Resonance Electrical Impedance Tomography (MREIT) is a high-resolution bioimpedance imaging technique that has developed over a period beginning in the early 1990s to measure low-frequency (<1 kHz) tissue electrical properties. Low-frequency electrical properties are particularly important because they provide valuable information on cell structures and ionic composition of tissues, which may be very useful for diagnostic purposes. MREIT uses one component of the magnetic flux density data induced due to an exogenous-current administration, measured using an MRI machine, to reconstruct isotropic or anisotropic electrical property distributions. The MREIT technique typically requires two linearly independent current administrations to reconstruct conductivity uniquely. Since its invention, researchers have explored its potential for measuring electrical conductivity in regions such as the brain and muscle tissue. It has also been investigated in disease models, for example, cerebral ischemia and early tumor detection. In this chapter, we aim to provide a solid foundation of the different MREIT image reconstruction algorithms, including both isotropic and anisotropic conductivity reconstruction approaches. We will also explore the newly developed diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) method, a practical method for anisotropic tissue property imaging, at the end of the chapter.

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Notes

  1. 1.

    The term pseudocurrent was first introduced by Ma et al. [39].

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Authors and Affiliations

  1. School of Biological Health System Engineering, Arizona State University, Tempe, AZ, USA

    Saurav Z. K. Sajib & Rosalind Sadleir

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  1. Saurav Z. K. Sajib
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  2. Rosalind Sadleir
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Correspondence to Rosalind Sadleir .

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Editors and Affiliations

  1. School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA

    Rosalind Sadleir

  2. School of Engineering, Macquarie University, Wallumattagal Campus, Macquarie Park, NSW, Australia

    Atul Singh Minhas

Appendices

Appendix 1

See Fig. 7.10.

Fig. 7.10
figure 10

Flow diagram for conductivity tensor reconstruction process using the non-iterative DT-MREIT algorithm [30]. The top row displays the measured diffusion tensor and reconstructed projected current density induced due to noncollinear current flow inside the imaging object which are used as an input to the algorithm. The bottom-left figure shows the reconstructed scale factor image obtained after solving the equation (7.60) described in Sect. 7.6.1. The reconstructed conductivity tensor components obtained from the scale factor and measured diffusion tensor are displayed in the middle column of the bottom row. The conductivity tensor is displayed at the bottom-right panel. Conductivity tensor of each voxel is represented by tri-axial ellipsoids. The radii of each ellipsoid are proportional to the eigenvalues, and their axes are oriented along the directions of the eigenvectors. The colors of the ellipsoid shown in the top-middle panel indicate the orientation of the principle eigenvector.

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Appendix 2

See Fig. 7.11.

Fig. 7.11
figure 11

Flow diagram for conductivity tensor reconstruction process using iterative DT-MREIT algorithm described in Jeong et al. [17]. The top row displays the measured diffusion tensor and reconstructed projected current density induced due to noncollinear current flow inside the imaging object. These images were used as an input to the algorithm. A global scale factor of η0 = 0.4 S ⋅s/mm3 was used to obtain the initial \({ \mathbb C}^0\) distribution. The Laplace Eq. (7.4a) subject to the same boundary conditions was also solved to calculate the ∇u distribution. The JP, \({ \mathbb D}\) and ∇u information was used to update the scale factor and the corresponding conductivity tensor (7.67). The process was repeated until the solution converged. The bottom-right image displays the reconstructed scale factor and conductivity tensor at iteration number (n) 16.

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Sajib, S.Z.K., Sadleir, R. (2022). Magnetic Resonance Electrical Impedance Tomography. In: Sadleir, R., Minhas, A.S. (eds) Electrical Properties of Tissues. Advances in Experimental Medicine and Biology, vol 1380. Springer, Cham. https://doi.org/10.1007/978-3-031-03873-0_7

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