Decomposition of high-frequency electrical conductivity into extracellular and intracellular compartments based on two-compartment model using low-to-high multi-b diffusion MRI

• Published in: Biomedical engineering online Vol. 20; no. 1; p. 29
• Main Authors: Lee, Mun Bae, Kim, Hyung Joong, Kwon, Oh In
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.03.2021; BioMed Central; BMC
• Subjects: Aqueous solutions; Brain - diagnostic imaging; Brain - physiology; Brain Mapping; Carrier density; Cell body; Cell membranes; Cerebrospinal Fluid; Compartments; Decision trees; Decomposition; Decomposition (Mathematics); Diffusion; Diffusion Magnetic Resonance Imaging; Electric Conductivity; Electric Impedance; Electrical conductivity; Electrical properties; Electrical resistivity; Electrical stimuli; Electrolytes; Electrophysiology; High-frequency conductivity decomposition; Humans; Image Processing, Computer-Assisted - methods; Low-frequency conductivity tensor; Machine learning; Magnetic properties; Magnetic resonance electrical property tomography; Magnetic resonance imaging; Membranes; Methods; Mobility; Model testing; Multi-b diffusion weighted imaging; Neuroimaging; Normal Distribution; Partial differential equations; Phantoms, Imaging; Physiological research; Random forest; Reproducibility of Results; Scanners; Standard deviation; Tissues; Tomography; Water chemistry; South Korea; Random forest; Low-frequency conductivity tensor; Multi-b diffusion weighted imaging; High-frequency conductivity decomposition; Magnetic resonance electrical property tomography
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-021-00869-5

As an object's electrical passive property, the electrical conductivity is proportional to the mobility and concentration of charged carriers that reflect the brain micro-structures. The measured multi-b diffusion-weighted imaging (Mb-DWI) data by controlling the degree of applied diffusion weights can quantify the apparent mobility of water molecules within biological tissues. Without any external electrical stimulation, magnetic resonance electrical properties tomography (MREPT) techniques have successfully recovered the conductivity distribution at a Larmor-frequency. This work provides a non-invasive method to decompose the high-frequency conductivity into the extracellular medium conductivity based on a two-compartment model using Mb-DWI. To separate the intra- and extracellular micro-structures from the recovered high-frequency conductivity, we include higher b-values DWI and apply the random decision forests to stably determine the micro-structural diffusion parameters. To demonstrate the proposed method, we conducted phantom and human experiments by comparing the results of reconstructed conductivity of extracellular medium and the conductivity in the intra-neurite and intra-cell body. The phantom and human experiments verify that the proposed method can recover the extracellular electrical properties from the high-frequency conductivity using a routine protocol sequence of MRI scan. We have proposed a method to decompose the electrical properties in the extracellular, intra-neurite, and soma compartments from the high-frequency conductivity map, reconstructed by solving the electro-magnetic equation with measured B1 phase signals.