Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
This work addresses image quality issues in EIT, a medical imaging technique, by enhancing reconstructions, though it is incremental as it applies existing CNN techniques to a specific domain.
The study tackled the problem of blurred images in Electrical Impedance Tomography (EIT) by using Convolutional Neural Networks (CNNs) to post-process direct reconstructions from D-bar methods, resulting in sharp and reliable reconstructions for experimental data without additional transfer training.
The mathematical problem for Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low-pass filtering of linear Fourier data, only using low frequencies in the image recovery process results in blurred images lacking sharp features such as clear organ boundaries. Convolutional Neural Networks provide a powerful framework for post-processing such convolved direct reconstructions. In this study, we demonstrate that these CNN techniques lead to sharp and reliable reconstructions even for the highly nonlinear inverse problem of EIT. The network is trained on data sets of simulated examples and then applied to experimental data without the need to perform an additional transfer training. Results for absolute EIT images are presented using experimental EIT data from the ACT4 and KIT4 EIT systems.