Ion mobility imaging and contrast mechanism of apparent conductivity in MREIT

Chauhan

IEEE Trans. Med. Imaging

et al. 2015

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Electrical conductivities of biological tissues show frequency-dependent behaviors, and these values at different frequencies may provide clinically useful diagnostic information. MR-based tissue property mapping techniques such as magnetic resonance electrical impedance tomography (MREIT) and magnetic resonance electrical property tomography (MREPT) are widely used and provide unique conductivity contrast information over different frequency ranges. Recently, a new method for data acquisition and reconstruction for low- and high-frequency conductivity images from a single MR scan was proposed. In this study, we applied this simultaneous dual-frequency range conductivity mapping MR method to evaluate its utility in a designed phantom and two in vivo animal disease models. Magnetic flux density and B(1)(+) phase map for dual-frequency conductivity images were acquired using a modified spin-echo pulse sequence. Low-frequency conductivity was reconstructed from MREIT data by the projected current density method, while high-frequency conductivity was reconstructed from MREPT data by B(1)(+) mapping. Two different conductivity phantoms comprising varying ion concentrations separated by insulating films with or without holes were used to study the contrast mechanism of the frequency-dependent conductivities related to ion concentration and mobility. Canine brain abscess and ischemia were used as in vivo models to evaluate the capability of the proposed method to identify new electrical properties-based contrast at two different frequencies. The simultaneous dual-frequency range conductivity mapping MR method provides unique contrast information related to the concentration and mobility of ions inside tissues. This method has potential to monitor dynamic changes of the state of disease.

Section: B Animal Brain Imagingmentioning

confidence: 91%

Section: A Phantom Preparationmentioning

confidence: 99%

Frequency-Dependent Conductivity Contrast for Tissue Characterization Using a Dual-Frequency Range Conductivity Mapping Magnetic Resonance Method

Chauhan

IEEE Trans. Med. Imaging

et al. 2015

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“…We may also use it to design an MREIT experiment [18].. We plan to use the simulator to study effects of numerous electrode designs and configurations. We will analyze simulated quantities to understand internal current flows in various parts of the human body.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional MREIT simulator of static bioelectromagnetism and MRI

et al. 2011

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Purpose Magnetic resonance electrical impedance tomography (MREIT) aims to produce high-resolution cross-sectional images of a conductivity distribution inside the human body. We perform conductivity image reconstructions based on a relation between the conductivity distribution and induced magnetic flux density distributions subject to externally injected currents. This induced magnetic flux density is measured in MREIT using an MRI scanner. To facilitate MREIT research, we need a numerical simulator including static bioelectromagnetism and MRI data collection process. In this paper, we describe the development of a threedimensional MREIT simulator (MREITSim). Methods We describe various features of MREITSim including geometry modeling, meshing, finite element modeling and numerical computations of magnetic flux density and k-space MR data. We demonstrate the underlying bioelectromagnetic phenomena and MR data collection process using phantom models of without and with anomaly. We illustrate effects of noise in MR data and echo time on magnetic flux density computations. Results We demonstrate numerical computations of current density and magnetic flux density distributions for current injections orthogonal to z-direction, the direction of the main magnetic field of an MRI scanner. The k-space MREIT data generation procedure is illustrated using a phantom model with an insulating anomaly. Conclusions The simulator functions as a virtual MREIT scanner and provides quantitative numerical results of intended experimental studies. We suggest the simulator as a basic research tool for future MREIT studies of its theory, algorithm, experimental techniques and pulse sequence design.

“…4(a)) was conductive for the x-direction current flow. 37 For the y-direction current injection case, the bundle of straws (right hand side in Fig. 4(a)) behaved as an insulator.…”

Section: B Phantom Imaging Experimentsmentioning

confidence: 99%

Reconstruction of apparent orthotropic conductivity tensor image using magnetic resonance electrical impedance tomography

Sajib

Journal of Applied Physics

Jeong

et al. 2015

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Magnetic resonance electrical impedance tomography visualizes current density and/or conductivity distributions inside an electrically conductive object. Injecting currents into the imaging object along at least two different directions, induced magnetic flux density data can be measured using a magnetic resonance imaging scanner. Without rotating the object inside the scanner, we can measure only one component of the magnetic flux density denoted as Bz. Since the biological tissues such as skeletal muscle and brain white matter show strong anisotropic properties, the reconstruction of anisotropic conductivity tensor is indispensable for the accurate observations in the biological systems. In this paper, we propose a direct method to reconstruct an axial apparent orthotropic conductivity tensor by using multiple Bz data subject to multiple injection currents. To investigate the anisotropic conductivity properties, we first recover the internal current density from the measured Bz data. From the recovered internal current density and the curl-free condition of the electric field, we derive an over-determined matrix system for determining the internal absolute orthotropic conductivity tensor. The over-determined matrix system is designed to use a combination of two loops around each pixel. Numerical simulations and phantom experimental results demonstrate that the proposed algorithm stably determines the orthotropic conductivity tensor.