Magnetic resonance electrical impedance tomography (MREIT)

Jin Keun Seo, Eung Je Woo

Research output: Contribution to journalReview article

95 Citations (Scopus)

Abstract

Magnetic resonance electrical impedance tomography (MREIT) is a recently developed medical imaging modality visualizing conductivity images of an electrically conducting object. MREIT was motivated by the well-known ill-posedness of the image reconstruction problem of electrical impedance tomography (EIT). Numerous experiences have shown that practically measurable data sets in an EIT system are insufficient for a robust reconstruction of a high-resolution static conductivity image due to its ill-posed nature and the influences of errors in forward modeling. To overcome the inherent ill-posed characteristics of EIT, the MREIT system was proposed in the early 1990s to use the internal data of magnetic flux density B = (Bx, By, Bz), which is induced by an externally injected current. MREIT uses an MRI scanner as a tool to measure the z-component Bz of the magnetic flux density, where z is the axial magnetization direction of the MRI scanner. In 2001, a constructive Bz-based MREIT algorithm called the harmonic Bz algorithm was developed and its numerical simulations showed that high-resolution conductivity image reconstructions are possible. This novel algorithm is based on the key observation that the Laplacian ΔBz probes changes in the log of the conductivity distribution along any equipotential curve having its tangent to the vector field J × (0, 0, 1), where J = (Jx, Jy, Jz) is the induced current density vector. Since then, imaging techniques in MREIT have advanced rapidly and have nowr eached the stage of in vivo animal and human experiments. This paper reviews MREIT from its mathematical framework to the most recent human experiment outcomes.

Original languageEnglish
Pages (from-to)40-68
Number of pages29
JournalSIAM Review
Volume53
Issue number1
DOIs
Publication statusPublished - 2011

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computational Mathematics
  • Applied Mathematics

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