Electrical conductivity and permittivity maps of brain tissues derived from water content based on T1‐weighted acquisition

• Published in: Magnetic resonance in medicine Vol. 77; no. 3; pp. 1094 - 1103
• Main Authors: Michel, Eric, Hernandez, Daniel, Lee, Soo Yeol
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.03.2017
• Subjects: electrical conductivity; electrical properties tomography (EPT); Maxwell's mixture theory; permittivity; tissue water content
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.26193

Purpose To develop an electrical properties tomography (EPT) technique that can provide in vivo electrical conductivity and permittivity images of biological tissue without performing complex‐valued radiofrequency field measurements. Theory and Methods Electrical conductivity and permittivity images are modeled as a monotonic function of tissues' water content (W) under the principle of Maxwell's mixture theory. Water content maps are estimated from two spin‐echo images having different repetition times (TRs). For the modeling functions, physically measured parameters (electrical properties, water content, and T1) of brain cerebrospinal fluid (CSF), gray matter, and white matter are used as landmark literature references. The formulations are validated by a developed electrolyte‐protein phantom and by human brain studies at 3 Tesla (T). Results The electrical properties (EPs) of the phantom estimated by the proposed method match well with the values measured on the bench. The conductivity and permittivity maps from all experiments show uncompromised spatial resolution without boundary artifacts and higher contrast when compared with water content maps. Conclusions Human brain and phantom EP images suggest that water content is a dominating factor in determining the electrical properties of tissues. Despite possible literature inaccuracies, the proposed method offers EP maps that can provide complementary information to current approaches, to facilitate EPT scans in clinical applications. Magn Reson Med 77:1094–1103, 2017. © 2016 International Society for Magnetic Resonance in Medicine