Magnetic-Resonance-Based Electrical Properties Tomography: A Review

• Published in: IEEE reviews in biomedical engineering Vol. 7; pp. 87 - 96
• Main Authors: Zhang, Xiaotong, Liu, Jiaen, He, Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; B_{1} mapping; Bioimpedance; Biomedical measurement; Brain - anatomy & histology; Brain - physiology; Electric Impedance; electrical properties tomography (EPT); Humans; Image reconstruction; Magnetic field measurement; Magnetic fields; Magnetic resonance imaging; magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; Medical imaging; NMR; Nuclear magnetic resonance; R&D; Radio frequency; Research & development; Specific absorption rate; specific absorption rate (SAR); Tomography; Tomography - methods
• DOI: 10.1109/RBME.2013.2297206
• ISSN: 1937-3333; 1941-1189

Frequency-dependent electrical properties (EPs; conductivity and permittivity) of biological tissues provide important diagnostic information (e.g., tumor characterization), and also play an important role in quantifying radiofrequency (RF) coil induced specific absorption rate (SAR), which is a major safety concern in high- and ultrahigh-field magnetic resonance imaging (MRI) applications. Cross-sectional imaging of EPs has been pursued for decades. Recently introduced electrical properties tomography (EPT) approaches utilize the measurable RF magnetic field induced by the RF coil in an MRI system to quantitatively reconstruct the EP distribution in vivo and noninvasively with a spatial resolution of a few millimeters or less. This paper reviews the EPT approach from its basic theory in electromagnetism to the state-of-the-art research outcomes. Emphasizing on the imaging reconstruction methods rather than experimentation techniques, we review the developed imaging algorithms, validation results in physical phantoms and biological tissues, as well as their applications in in vivo tumor detection and subject-specific SAR prediction. Challenges for future research are also discussed.