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

Research output: Contribution to journalArticle

78 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 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.

Original languageEnglish
Pages (from-to)388-394
Number of pages7
JournalIEEE Transactions on Medical Imaging
Volume23
Issue number3
DOIs
Publication statusPublished - 2004 Mar 1

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Electric Conductivity
Acoustic impedance
Magnetic resonance
Electric Impedance
Tomography
Magnetic Resonance Spectroscopy
Decomposition
Imaging techniques
Magnetic flux
Magnetic resonance imaging
Noise
Computer-Assisted Image Processing
Magnetic Fields
Image reconstruction
Electrodes
Magnetic fields
Injections
Computer simulation

All Science Journal Classification (ASJC) codes

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

Cite this

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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.",
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Electrical Conductivity Imaging Using Gradient Bz Decomposition Algorithm in Magnetic Resonance Electrical Impedance Tomography (MREIT). / Park, Chunjae; Kwon, Ohin; Woo, Eung Je; Seo, Jin Keun.

In: IEEE Transactions on Medical Imaging, Vol. 23, No. 3, 01.03.2004, p. 388-394.

Research output: Contribution to journalArticle

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