Abstract

In this talk we discuss modelling and image reconstruction in Lorentz Force Electrical Impedance Tomography (LFEIT) also referred to as Magneto-acousto-electric tomography. LFEIT is a hybrid modality for electrical conductivity imaging underpinned by a nexus of physical phenomena occurring when acoustic waves propagate through electrically conductive media subjected to a magnetic field. When using focused ultrasound radiation, a pressure field focuses near a point causing small, local displacement of the tissue. Under the influence of a magnetic field, this motion induces a Lorentz current source at the focal point. The magnitude of this current scales to the amount of free charges released by the moving tissue, hence the conductivity of the medium at the point of focus can be inferred, by measuring the current or its potential field at the surface. These measurements are then used in solving an inverse problem for the electrical conductivity. I will review some recent results that suggest that LFEIT has significant advantages over the diffusion-governed EIT in terms of its robustness to measurement noise and spatial resolution.

Biography

Dr. Nicholas Polydorides was educated in the UK and held research positions at School of Maths at the University of Manchester, the Lab for Information and Decision Systems at MIT and the Energy, Environment and Water Research Centre of the Cyprus Institute. In Edinburgh Dr. Polydorides leads the Agile Tomography Group that specialises in low-frequency electromagnetic simulation and tomographic image reconstruction, as well as chemical species tomography from spectroscopic measurements of light in the near infrared regime.

Dr. Polydorides’ research is in the realm of applied inverse problems and this usually entails, in some proportion: mathematical modelling, signal processing, statistical estimation and optimisation algorithms. As of 2016 Dr. Polydorides is also a faculty fellow at newly established Alan Turing Institute, which perhaps also qualifies me as a data scientist. His research is relevant to applications of electromagnetic imaging in geophysical exploration, industrial process tomography, biomedical imaging and non-destructive testing of materials and structures. In particular, Dr. Polydorides is interested in computational approaches suitable for large-scale PDE models for static and low-frequency electromagnetic fields and algorithms that process these models along with measurements in the quest to image the electromagnetic properties of a domain of interest.