Electrical Conductivity Imaging Using Gradient Bz Decomposition Algorithm in Magnetic Resonance Electrical Impedance Tomography (MREIT)

Chunjae Park; Ohin Kwon; Eung Je Woo; Jin Keun Seo

doi:10.1109/TMI.2004.824228

Electrical Conductivity Imaging Using Gradient B_z Decomposition Algorithm in Magnetic Resonance Electrical Impedance Tomography (MREIT)

Chunjae Park, Ohin Kwon, Eung Je Woo, Jin Keun Seo

Department of Computational Science and Engineering

Research output: Contribution to journal › Article › peer-review

83 Citations (Scopus)

Abstract

In magnetic resonance electrical impedance tomography (MREIT), we try to visualize cross-sectional conductivity (or resistivity) images of a subject. We inject electrical currents into the subject through surface electrodes and measure the z component B_z of the induced internal magnetic flux density using an MRI scanner. Here, z is the direction of the main magnetic field of the MRI scanner. We formulate the conductivity image reconstruction problem in MREIT from a careful analysis of the relationship between the injection current and the induced magnetic flux density B_z. Based on the novel mathematical formulation, we propose the gradient B_z decomposition algorithm to reconstruct conductivity images. This new algorithm needs to differentiate B_z only once in contrast to the previously developed harmonic B_z algorithm where the numerical computation of ∇²B_z is required. The new algorithm, therefore, has the important advantage of much improved noise tolerance. Numerical simulations with added random noise of realistic amounts show the feasibility of the algorithm in practical applications and also its robustness against measurement noise.

Original language	English
Pages (from-to)	388-394
Number of pages	7
Journal	IEEE Transactions on Medical Imaging
Volume	23
Issue number	3
DOIs	https://doi.org/10.1109/TMI.2004.824228
Publication status	Published - 2004 Mar

Bibliographical note

Funding Information:
Manuscript received June 14, 2003; revised November 17, 2003. This work was supported by the Korea Science and Engineering Foundation under Grant R11-2002-103. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was J. Newell. Asterisk indicates corresponding author.

All Science Journal Classification (ASJC) codes

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TMI.2004.824228

Cite this

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title = "Electrical Conductivity Imaging Using Gradient Bz Decomposition Algorithm in Magnetic Resonance Electrical Impedance Tomography (MREIT)",

abstract = "In magnetic resonance electrical impedance tomography (MREIT), we try to visualize cross-sectional conductivity (or resistivity) images of a subject. We inject electrical currents into the subject through surface electrodes and measure the z component Bz of the induced internal magnetic flux density using an MRI scanner. Here, z is the direction of the main magnetic field of the MRI scanner. We formulate the conductivity image reconstruction problem in MREIT from a careful analysis of the relationship between the injection current and the induced magnetic flux density Bz. Based on the novel mathematical formulation, we propose the gradient Bz decomposition algorithm to reconstruct conductivity images. This new algorithm needs to differentiate Bz only once in contrast to the previously developed harmonic Bz algorithm where the numerical computation of ∇2Bz is required. The new algorithm, therefore, has the important advantage of much improved noise tolerance. Numerical simulations with added random noise of realistic amounts show the feasibility of the algorithm in practical applications and also its robustness against measurement noise.",

keywords = "Conductivity image, Gradient B decomposition algorithm, MREIT, Magnetic flux density",

author = "Chunjae Park and Ohin Kwon and Woo, {Eung Je} and Seo, {Jin Keun}",

note = "Funding Information: Manuscript received June 14, 2003; revised November 17, 2003. This work was supported by the Korea Science and Engineering Foundation under Grant R11-2002-103. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was J. Newell. Asterisk indicates corresponding author.",

year = "2004",

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AU - Park, Chunjae

AU - Kwon, Ohin

AU - Woo, Eung Je

AU - Seo, Jin Keun

N1 - Funding Information: Manuscript received June 14, 2003; revised November 17, 2003. This work was supported by the Korea Science and Engineering Foundation under Grant R11-2002-103. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was J. Newell. Asterisk indicates corresponding author.

PY - 2004/3

Y1 - 2004/3

N2 - In magnetic resonance electrical impedance tomography (MREIT), we try to visualize cross-sectional conductivity (or resistivity) images of a subject. We inject electrical currents into the subject through surface electrodes and measure the z component Bz of the induced internal magnetic flux density using an MRI scanner. Here, z is the direction of the main magnetic field of the MRI scanner. We formulate the conductivity image reconstruction problem in MREIT from a careful analysis of the relationship between the injection current and the induced magnetic flux density Bz. Based on the novel mathematical formulation, we propose the gradient Bz decomposition algorithm to reconstruct conductivity images. This new algorithm needs to differentiate Bz only once in contrast to the previously developed harmonic Bz algorithm where the numerical computation of ∇2Bz is required. The new algorithm, therefore, has the important advantage of much improved noise tolerance. Numerical simulations with added random noise of realistic amounts show the feasibility of the algorithm in practical applications and also its robustness against measurement noise.

AB - In magnetic resonance electrical impedance tomography (MREIT), we try to visualize cross-sectional conductivity (or resistivity) images of a subject. We inject electrical currents into the subject through surface electrodes and measure the z component Bz of the induced internal magnetic flux density using an MRI scanner. Here, z is the direction of the main magnetic field of the MRI scanner. We formulate the conductivity image reconstruction problem in MREIT from a careful analysis of the relationship between the injection current and the induced magnetic flux density Bz. Based on the novel mathematical formulation, we propose the gradient Bz decomposition algorithm to reconstruct conductivity images. This new algorithm needs to differentiate Bz only once in contrast to the previously developed harmonic Bz algorithm where the numerical computation of ∇2Bz is required. The new algorithm, therefore, has the important advantage of much improved noise tolerance. Numerical simulations with added random noise of realistic amounts show the feasibility of the algorithm in practical applications and also its robustness against measurement noise.

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