Prog Med Phys.  2024 Dec;35(4):73-88. 10.14316/pmp.2024.35.4.73.

Principle, Development, and Application of Electrical Conductivity Mapping Using Magnetic Resonance Imaging

• Jahng GH
• Lee MB
• Kwon OI

• KMID: 2564146
• DOI: http://doi.org/10.14316/pmp.2024.35.4.73

Abstract

Magnetic resonance imaging (MRI)-related techniques can provide information related to the electrical properties of the body. Understanding the electrical properties of human tissues is crucial for developing diagnostic tools and therapeutic approaches for various medical conditions. This study reviewed the principles, development, and application of electrical conductivity mapping using MRI. To review the magnetic resonance electrical properties tomography (MREPT)-based conductivity mapping technique and its application to brain imaging, first, we explain the definition and fundamental principles of electrical conductivity, some factors that influence changes in ionic conductivity, and the background of mapping cellular conductivities. Second, we explain the concepts and applications of magnetic resonance electrical impedance tomography (MREIT) and MREPT. Third, we describe our recent technical developments and their clinical applications. Finally, we explain the benefits, impacts, and challenges of MRI-based conductivity in clinical practice. MRI techniques, such as MREIT and MREPT, enabled the measurement of conductivity-related properties within the body. MREIT assessed low-frequency conductivity by applying a lowfrequency external current, whereas MREPT captured high-frequency conductivity (at the Larmor frequency) without applying an external current. In MREIT, the subject’s safety should be ensured because electrical current is applied, particularly around sensitive areas, such as the brain, or in subjects with implanted electronic devices. Our previous studies have highlighted the potential of conductivity indices as biomarkers for Alzheimer’s disease. MREPT is usually applied to humans rather than MREIT. MREPT holds promise as a noninvasive tool for characterizing tissue properties and understanding pathological conditions.

Keyword

MRI; B1 phase; Conductivity; Brain application

Figure

• 

  Fig. 1 Graphical explanation of the MREIT method. In MREIT, electrodes are attached to the subject’s surface at specified locations, such as brain areas without hair. The magnetic resonance imaging phase is used to obtain the Bz Field, which is the component of the magnetic field induced by the applied current. The Bz component is located along the direction of the main magnetic field. After calculating the Bz component, the current density within the subject is calculated by applying Ampere’s Law and/or the Biot–Savart Law. Because the applied current is known, this step allows the reconstruction of the current paths in the tissue. Finally, using mathematical algorithms (e.g., J-substitution algorithm), conductivity images are reconstructed based on the measured magnetic flux density and applied current density. MREIT, magnetic resonance electrical impedance tomography.

• 

  Fig. 2 Summary of the technical development to map the HFC and low-frequency conductivity using MREPT and MC-SMT images. In MREPT, (a) a standard magnetic resonance imaging scan is performed using a sequence that is sensitive to phase information, such as a turbo spin-echo sequence or balanced fast field-echo sequence. Furthermore, (b) diffusion tensor images with three b-values and multiple gradient directions are acquired to obtain microstructure information. The spatial distribution of the radiofrequency field (B1) is calculated from the phase images. (c) The HFC is calculated with a double derivative of the B1 field by applying a regularization. (d) The MC-SMT is used to map intrinsic diffusion coefficient, intra-neurite volume fraction, and extra-neurite mean diffusivity. (e) The low-frequency conductivity map can be calculated with the recovered HFC and MC-SMT maps. (f) The extra-neurite tensor maps are obtained using MC-SMT maps. (g) The low-frequency conductivity tensor map can be calculated using (c) and (f). HFC, high-frequency conductivity; MREPT, magnetic resonance electrical properties tomography; MC-SMT, multicompartment spherical mean technique.

• 

  Fig. 3 Representative maps are obtained from two imaging slices acquired from one young participant. Maps are shown as the high-frequency conductivity (HFC), intrinsic diffusion coefficient (Dint), intra-neurite volume fraction (νint), extra-neurite diffusivity (Dext), and low-frequency conductivity tensor.

• 

  Fig. 4 3D T1-weighted image (3DT1) and the corresponding segmented brain tissues of gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid (CSF) volume and a high-frequency conductivity (HFC) map acquired from one cognitively normal (CN) older participant and a patient with Alzheimer’s disease (AD).

• 

  Fig. 5 T1-weighted (T1W) image and conductivity maps of the high-frequency conductivity (HFC) and the corresponding extra-neurite conductivity (EC) and intra-neurite conductivity (IC) acquired from one cognitively normal (CN) older participant and a patient with Alzheimer’s disease (AD).

Reference

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