Electromagnetic Properties and the Basis for CDI, MREIT, and EPT.

The electromagnetic properties of body tissues depend on numerous factors, the most important of which are ionic concentrations and, particularly in the low-frequency regime, membrane density and geometry. In this chapter, the characteristics of these properties and their spectra are introduced. The properties measured by different types of MR-based methods are described.

Magnetic Resonance Imaging Basics.

In this chapter, we will discuss the basic principles of signal generation and image formation in magnetic resonance imaging (MRI). We will start with a description of nuclear magnetic resonance (NMR) phenomenon and then gradually arrive at the mathematical expressions for MRI signal in spatial domain and k-space domain. Then we describe the image reconstruction methods typically used in MRI, the signal-to-noise ratio calculation methods in MRI, and common MR image formats. A key focus of the contents of this chapter is on the formation of phase images in MRI. We do not intend to provide a comprehensive overview of MRI. Instead, the contents are intended for readers interested in performing research in electromagnetic properties mapping using MRI. Nevertheless, considering the generality of the contents, any reader interested in developing a quick understanding of the physical and mathematical background of MRI can find this chapter helpful.

Magnetic Resonance Electrical Properties Tomography (MREPT).

This chapter explains the magnetic resonance electrical impedance tomography (MREPT) technique used to image electrical properties at high frequencies. The chapter describes the MREPT data acquisition methods, current state-of-the-art image reconstruction algorithms, and experiments with phantoms, animals, and humans.

High-frequency electrical properties tomography at 9.4T as a novel contrast mechanism for brain tumors.

PURPOSE: To establish high-frequency magnetic resonance electrical properties tomography (MREPT) as a novel contrast mechanism for the assessment of glioblastomas using a rat brain tumor model. METHODS: Six F98 intracranial tumor bearing rats were imaged longitudinally 8, 11 and 14 days after tumor cell inoculation. Conductivity and mean diffusivity maps were generated using MREPT and Diffusion Tensor Imaging. These maps were co-registered with T2 -weighted images and volumes of interests (VOIs) were segmented from the normal brain, ventricles, edema, viable tumor, tumor rim, and tumor core regions. Longitudinal changes in conductivity and mean diffusivity (MD) values were compared in these regions. A correlation analysis was also performed between conductivity and mean diffusivity values. RESULTS: The conductivity of ventricles, edematous area and tumor regions (tumor rim, viable tumor, tumor core) was significantly higher (P < .01) compared to the contralateral cortex. The conductivity of the tumor increased over time while MD from the tumor did not change. A marginal positive correlation was noted between conductivity and MD values for tumor rim and viable tumor, whereas this correlation was negative for the tumor core. CONCLUSION: We demonstrate a novel contrast mechanism based on ionic concentration and mobility, which may aid in providing complementary information to water diffusion in probing the microenvironment of brain tumors.

Measurement of blood-brain barrier permeability using dynamic contrast-enhanced magnetic resonance imaging with reduced scan time.

PURPOSE: To investigate the feasibility of measuring the subtle disruption of blood-brain barrier (BBB) using DCE-MRI with a scan duration shorter than 10 min. METHODS: The extended Patlak-model (EPM) was introduced to include the effect of plasma flow (Fp ) in the estimation of vascular permeability-surface area product (PS). Numerical simulation studies were carried out to investigate how the reduction in scan time affects the accuracy in estimating contrast kinetic parameters. DCE-MRI studies of the rat brain were conducted with Fisher rats to confirm the results from the simulation. Intracranial F98 glioblastoma models were used to assess areas with different levels of permeability. In the normal brain tissues, the Patlak model (PM) and EPM were compared, whereas the 2-compartment-exchange-model (TCM) and EPM were assessed in the peri-tumor and the tumor regions. RESULTS: The simulation study results demonstrated that scan time reduction could lead to larger bias in PS estimated by PM (>2000%) than by EPM (<47%), especially when Fp is low. When Fp was high as in the gray matter, the bias in PM-PS (>900%) were larger than that in EPM-PS (<42%). The animal study also showed similar results, where the PM parameters were more sensitive to the scan duration than the EPM parameters. It was also demonstrated that, in the peri-tumor region, the EPM parameters showed less change by scan duration than the TCM parameters. CONCLUSION: The results of this study suggest that EPM can be used to measure PS with a scan duration of 10 min or less.