Speaker
Description
Electrical impedance tomography (EIT) is a medical imaging technique that uses electric currents and potential measurements on the surface of the body to infer the electrical conductivity within the body. To improve the reconstructed image, our models of biological tissues incorporate anisotropic conductivity, the electrophysiology of electrically active tissues, and the physics of ionic solutions. These partial differential equation models are solved numerically using boundary integral equation methods as well as biologically-informed neural networks. We take two parallel approaches to this medical inverse problem: numerical optimization and deep learning. Our approaches are validated against a large experimental dataset collected from our own EIT device. A second medical inverse problem is cardiography. The cells of the heart are electrically active and synchronized, which make measurement of the electrical activity of the heart readily observable in electrocardiograms (EKG). We model the electrical activity of the heart using the mono-domain model with detailed ionic current models and patient-specific heart geometry. We employ our simulation and vast collection of EKG recordings to pursue a variety of applications: a) visualize the shape of the heart as an indication of congestive heart failure; b) visualize the electrical conduction pathway within the heart to understand conduction disorders; and c) study circadian rhythms in the electrical activity of the heart.
