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Electrodeless conductivity tensor imaging (CTI) using MRI: basic theory and animal experiments

  • Saurav Z.K. Sajib
  • , Oh In Kwon
  • , Hyung Joong Kim
  • , Eung Je Woo

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

The electrical conductivity is a passive material property primarily determined by concentrations of charge carriers and their mobility. The macroscopic conductivity of a biological tissue at low frequency may exhibit anisotropy related with its structural directionality. When expressed as a tensor and properly quantified, the conductivity tensor can provide diagnostic information of numerous diseases. Imaging conductivity distributions inside the human body requires probing it by externally injecting conduction currents or inducing eddy currents. At low frequency, the Faraday induction is negligible and it has been necessary in most practical cases to inject currents through surface electrodes. Here we report a novel method to reconstruct conductivity tensor images using an MRI scanner without current injection. This electrodeless method of conductivity tensor imaging (CTI) utilizes B1 mapping to recover a high-frequency isotropic conductivity image which is influenced by contents in both extracellular and intracellular spaces. Multi-b diffusion weighted imaging is then utilized to extract the effects of the extracellular space and incorporate its directional structural property. Implementing the novel CTI method in a clinical MRI scanner, we reconstructed in vivo conductivity tensor images of canine brains. Depending on the details of the implementation, it may produce conductivity contrast images for conductivity weighted imaging (CWI). Clinical applications of CTI and CWI may include imaging of tumor, ischemia, inflammation, cirrhosis, and other diseases. CTI can provide patient-specific models for source imaging, transcranial dc stimulation, deep brain stimulation, and electroporation.

Original languageEnglish
Pages (from-to)273-282
Number of pages10
JournalBiomedical Engineering Letters
Volume8
Issue number3
DOIs
Publication statusPublished - 1 Aug 2018

Bibliographical note

Publisher Copyright:
© 2018, Korean Society of Medical and Biological Engineering and Springer-Verlag GmbH Germany, part of Springer Nature.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Anisotropy
  • Conductivity tensor imaging (CTI)
  • Diffusion weighted imaging
  • Magnetic resonance electrical properties tomography

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