Cross-sectional imaging of conductivity and permittivity distributions inside the human body has been actively investigated in impedance imaging areas such as electrical impedance tomography (EIT) and magnetic induction tomography (MIT). Since the conductivity and permittivity values exhibit frequency-dependent changes, it is worthwhile to perform spectroscopic imaging from almost dc to hundreds of MHz. To probe the human body, we may inject current using surface electrodes or induce current using external coils. In EIT and MIT, measured data are only available on the boundary or exterior of the body unless we invasively place sensors inside the body. Their image reconstruction problems are nonlinear and ill-posed to result in images with a relatively low spatial resolution. Noting that an MRI scanner can noninvasively measure magnetic fields inside the human body, electrical tissue property imaging methods using MRI have lately been proposed. Magnetic resonance EIT (MREIT) performs conductivity imaging at dc or below 1kHz by externally injecting current into the human body and measuring induced internal magnetic flux density data using an MRI scanner. Magnetic resonance electrical property tomography (MREPT) produces both conductivity and permittivity images at the Larmor frequency of an MRI scanner based on B1-mapping techniques. Since internal data are only available in MREIT and MREPT, we may formulate well-posed inverse problems for image reconstructions. To develop related imaging techniques, we should clearly understand the basic principles of MREIT and MREPT, which are based on coupled physics of bioelectromagnetism and MRI as well as associated mathematical methods. In this paper, we describe the physical principles of MREIT and MREPT in a unified way and associate measurable quantities with the conductivity and permittivity. Clarifying the key relations among them, we examine existing image reconstruction algorithms to reveal their capabilities and limitations. We discuss technical issues in MREIT and MREPT and suggest future research directions to improve the quality of cross-sectional images of the electrical tissue properties.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Signal Processing
- Mathematical Physics
- Computer Science Applications
- Applied Mathematics