In vivo high-resolutionconductivity imaging of the human leg using MREIT: The first human experiment

Hyung Joong Kim, Young Tae Kim, Atul S. Minhas, Woo Chul Jeong, Eung Je Woo, Jin Keun Seo, O. Jung Kwon

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)


We present the first in vivo cross-sectional conductivity image of the human leg with 1.7 mm pixel size using the magnetic resonance electrical impedance tomography (MREIT) technique. After a review of its experimental protocol by an Institutional Review Board (IRB), we performed MREIT imaging experiments of four human subjects using a 3 T MRI scanner. Adopting thin and flexible carbonhydrogel electrodes with a large surface area and good contact, we could inject as much as 9 mA current in a form of 15 ms pulse into the leg without producing a painful sensation and motion artifact. Sequentially injecting two imaging currents in two different directions, we collected induced magnetic flux density data inside the leg. Scaled conductivity images reconstructed by using the single-step harmonic $B-{z}$ algorithm well distinguished different parts of the subcutaneous adipose tissue, muscle, crural fascia, intermuscular septum and bone inside the leg. We could observe spurious noise spikes in the outer layer of the bone primarily due to the MR signal void phenomenon there. Around the fat, the chemical shift of about two pixels occurred obscuring the boundary of the fat region. Future work should include a fat correction method incorporated in the MREIT pulse sequence and improvements in radio-frequency coils and image reconstruction algorithms. Further human imaging experiments are planned and being conducted to produce conductivity images from different parts of the human body.

Original languageEnglish
Article number4814693
Pages (from-to)1681-1687
Number of pages7
JournalIEEE Transactions on Medical Imaging
Issue number11
Publication statusPublished - 2009 Nov

Bibliographical note

Funding Information:
Manuscript received January 23, 2009; revised March 04, 2009. First published April 14, 2009; current version published October 28, 2009. This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (R11-2002-103). Asterisk indicates corresponding author.

All Science Journal Classification (ASJC) codes

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


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