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

79 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

All Science Journal Classification (ASJC) codes

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

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