Conductivity Imaging

Conductivity imaging aims to reconstruct the spatial distribution of electrical conductivity within a medium from measurements of electrical potentials or currents. Current research heavily utilizes deep learning, employing architectures like U-nets and fully connected feedforward networks, often in conjunction with established methods such as the D-bar method, to improve image quality, resolution, and computational efficiency. These advancements are driven by applications in diverse fields, including medical imaging (e.g., detecting cardiac scar tissue) and material characterization (e.g., analyzing inkjet-printed components), where accurate conductivity maps provide crucial insights for diagnosis and manufacturing. The focus is on developing robust and accurate algorithms that can handle noisy data and high-dimensional problems.