Abstract

Electromagnetic induction imaging (EMI) allows mapping of the conductivity of target objects and, when combined with appropriate algorithms, the generation of full 3D tomographic images. Despite its tremendous potential, and the wealth of possible applications, the use of EMI has essentially been limited to eddy current testing for monitoring of corrosion and welding in metallic structures. The present work reviews the factors hindering the progress of electromagnetic induction imaging and highlights how the use of atomic magnetometers overcame some of them, opening the path to real world applications of EMI. Perspectives for further developments are discussed.

Highlights

  • Electromagnetic induction imaging (EMI) allows mapping of the electromagnetic properties of an object

  • This article is aimed at reviewing the motivations which led to the development of EMI with atomic magnetometers, the key enabling role they played in overcoming important limitations of EMI, and the progress so far

  • The excess of fluid produces an anomaly in the conductivity of the brain tissue, which can in principle be detected via electromagnetic induction imaging techniques [4,10]

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Summary

Introduction

Electromagnetic induction imaging (EMI) allows mapping of the electromagnetic properties of an object. It is not limited to two-dimensional maps and, when combined with appropriate inversion algorithms, it can produce full three-dimensional images. To highlight such tomographic capabilities, it is often referred to as magnetic induction tomography (MIT) [1]. This article is aimed at reviewing the motivations which led to the development of EMI with atomic magnetometers, the key enabling role they played in overcoming important limitations of EMI, and the progress so far.

Principles of Electromagnetic Induction Imaging
Security
Surveillance
Biomedical Imaging
Head Injuries
Cancer
Atrial Fibrillation
Motivations
First Proof-Of-Concept of EMI with Atomic Magnetometers
Development of EMI with AMs and the Quest for Ultimate Sensitivity
Alghorithms for Image Reconstruction
Findings
Discussion and Conclusions
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