Deans, Cameron; (2018) Electromagnetic Induction Imaging with Atomic Magnetometers. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Electromagnetic induction imaging (EMI) is a technique for non-invasively mapping the passive electromagnetic properties of materials. It involves the active probing of samples with a radio-frequency magnetic field and recording the details of the magnetic field produced by the induced eddy current response. The performance of an EMI system is ultimately determined by the choice of magnetic field sensor used in the measurement. The sensor’s sensitivity, range of operation frequency, and sensing volume are all crucial characteristics when considering the imaging platform’s capabilities. Atomic magnetometers (AMs) – based on the coherent precession of a polarised alkali atomic vapour – currently rate amongst the most sensitive devices for magnetic field measurements. Radio-frequency atomic magnetometers (RF-AMs) are ultra-sensitive detectors of oscillating magnetic fields across a broad range of frequencies. As such, they are ideally suited to EMI applications. This work presents the development of EMI systems based on RF-AMs. The imaging performance and a wide range of applications are experimentally demonstrated. The continuous development of a single-channel rubidium RF-AM is described. The final device operates in unshielded environments and near room temperature with a measured sensitivity of 55 fT/√Hz, a photon-shot noise limit of 10 fT/√Hz, and a linewidth of 36 Hz. Tunability of the device is proven by consistent, narrow-linewidth operation across the kHz – MHz band – operating in magnetic fields significantly greater than previous AM designs. The sensor was developed with a small effective sensor volume, which increases the spatial resolution of the imaging. High-resolution EMI is performed across a broad range of materials. This spans the first EMI images with an RF-AM at 6x107 S/m to low-conductivity, non-metallic samples at 500 S/m. Typically, sample volumes are of a few cm3 and with an imaging resolution around 1 mm. These numbers make EMI with AMs (EMI-AM) suitable for numerous applications. Techniques – including multi-frequency image analysis – are employed to discriminate sample properties. Further work developed novel image reconstruction approaches – based on machine learning – to maximise the amount of information that can be extracted from EMI images. Finally, the potential of biomedical imaging is discussed and its feasibility verified by simulating the application of EMI-AM to imaging the conductivity of the heart.

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