Electrical impedance tomography is an effective, non-ionizing, and inexpensive imaging modality for imaging the electric conductivity within an object from a set of boundary data. In practice, reconstructed images from traditional deterministic methods provide poor resolution.
We are interested in solving the exponentially ill-posed electrical impedance tomography (EIT) inverse problem using a newly developed deep neural network algorithmOur deep neural net algorithm was developed using convolutional neural networks for the direct problem and a deep network for the inverse problem to provide higher resolution images.
