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Journal ArticleDOI

Imaging Electrical Impedance From Acoustic Measurements by Means of Magnetoacoustic Tomography With Magnetic Induction (MAT-MI)

Xu Li, +2 more
- 22 Jan 2007 - 
- Vol. 54, Iss: 2, pp 323-330
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TLDR
The present computer simulation results suggest that MAT-MI can reconstruct conductivity images of biological tissue with high spatial resolution and high contrast and in the present simulation study, a two-layer spherical model is used.
Abstract
We have conducted computer simulation and experimental studies on magnetoacoustic-tomography with magnetic induction (MAT-MI) for electrical impedance imaging. In MAT-MI, the object to be imaged is placed in a static magnetic field, while pulsed magnetic stimulation is applied in order to induce eddy current in the object. In the static magnetic field, the Lorentz force acts upon the eddy current and causes acoustic vibrations in the object. The propagated acoustic wave is then measured around the object to reconstruct the electrical impedance distribution. In the present simulation study, a two-layer spherical model is used. Parameters of the model such as sample size, conductivity values, strength of the static and pulsed magnetic field, are set to simulate features of biological tissue samples and feasible experimental constraints. In the forward simulation, the electrical potential and current density are solved using Poisson's equation, and the acoustic pressure is calculated as the forward solution. The electrical impedance distribution is then reconstructed from the simulated pressure distribution surrounding the sample. The present computer simulation results suggest that MAT-MI can reconstruct conductivity images of biological tissue with high spatial resolution and high contrast. The feasibility of MAT-MI in providing high spatial resolution images containing impedance-related information has also been demonstrated in a phantom experiment

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Citations
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Journal ArticleDOI

Electromagnetic Imaging Methods for Nondestructive Evaluation Applications

TL;DR: This review provides a comprehensive summary of research works on electromagnetic imaging methods for NDE applications, followed by the summary and discussions on future directions.
Journal ArticleDOI

Imaging Electric Properties of Biological Tissues by RF Field Mapping in MRI

TL;DR: A dual-excitation algorithm is proposed, which uses two sets of measured B1 data to noninvasively reconstruct the EPs of biological tissues, and provides superior performance without the need for iterative computations.
Journal ArticleDOI

Gradient-based electrical properties tomography (gEPT): A robust method for mapping electrical properties of biological tissues in vivo using magnetic resonance imaging.

TL;DR: To develop high‐resolution EPT methods and investigate a gradient‐based EPT approach that aims to reconstruct the electrical properties of an imaged sample from experimentally measured B1 maps with improved boundary reconstruction and robustness against measurement noise.
Journal ArticleDOI

Magnetic-Resonance-Based Electrical Properties Tomography: A Review

TL;DR: The EPT approach from its basic theory in electromagnetism to the state-of-the-art research outcomes is reviewed and validation results in physical phantoms and biological tissues, as well as their applications in in vivo tumor detection and subject-specific SAR prediction are discussed.
Journal ArticleDOI

Magneto-acousto-electrical tomography: a potential method for imaging current density and electrical impedance

TL;DR: This is the first time a high-spatial-resolution image of current density is presented using MAET and a mathematical formula whereby the lead field current density may be utilized to reconstruct the distribution of the electrical impedance in a piecewise smooth object is offered.
References
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Book

Classical Electrodynamics

PatentDOI

Magnetic induction tomography

TL;DR: In this article, a magnetic induction tomography (MIT) apparatus comprises an excitation signal generator (70), a primary excitation coil (50), an active reference source (175), and a signal distribution network (115).
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