Fast magnetic resonance electrical impedance tomography with highly undersampled data

Yizhuang Song, Habib Ammari, Jin Keun Seo

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper describes the mathematical grounds for a highly undersampled magnetic resonance electrical impedance tomography (MREIT) method, with the aim of visualizing the dynamic changes in electrical tissue properties that occur in response to physiological activity. MREIT with fully sampled magnetic resonance (MR) data (satisfying the Nyquist criterion) has been shown to be capable of high-resolution conductivity imaging in numerical simulations and in animal experiments. However, when the data are undersampled (violating the Nyquist criterion for reducing data acquisition time), it is difficult to extract the component of magnetic flux density that is induced by boundary injection currents, and it is the data from this component that are used in performing the standard MREIT algorithm. Here, we show that it is possible to localize small conductivity perturbations using highly undersampled MR data. We perform various numerical simulations to support our theoretical results.

Original languageEnglish
Pages (from-to)558-577
Number of pages20
JournalSIAM Journal on Imaging Sciences
Volume10
Issue number2
DOIs
Publication statusPublished - 2017 Jan 1

Fingerprint

Electrical Impedance Tomography
Acoustic impedance
Magnetic Resonance
Magnetic resonance
Tomography
Conductivity
Numerical Simulation
Computer simulation
Magnetic flux
Data Acquisition
Animals
Data acquisition
Injection
High Resolution
Imaging
Tissue
Perturbation
Imaging techniques
Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Applied Mathematics

Cite this

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Fast magnetic resonance electrical impedance tomography with highly undersampled data. / Song, Yizhuang; Ammari, Habib; Seo, Jin Keun.

In: SIAM Journal on Imaging Sciences, Vol. 10, No. 2, 01.01.2017, p. 558-577.

Research output: Contribution to journalArticle

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