Abstract
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 paper, 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.
Highlights
E LECTRICAL Impedance Tomography (EIT) images traditionally display the tissue-dependent conductivity distribution of a patient in the plane of the attached measurement electrodes allowing, e.g., visualization of heart and lung function as well as injuries [1]–[6]
We propose combining D-bar with Deep Learning, with a Convolutional Neural Network, to ‘learn’ and undo the image blurring resulting in real-time sharp EIT images
We demonstrate the effect of the Deep D-bar method on simulated, as well as experimental, data for absolute EIT imaging
Summary
E LECTRICAL Impedance Tomography (EIT) images traditionally display the tissue-dependent conductivity distribution of a patient in the plane of the attached measurement electrodes allowing, e.g., visualization of heart and lung function as well as injuries [1]–[6]. The resulting images are of high-contrast and data acquisition is done by harmless electrical measurements without the need for contrast agents or ionizing radiation. The image recovery process of forming the EIT image from the current/voltage. Manuscript received February 26, 2018; revised April 4, 2018; accepted April 13, 2018. Date of publication April 27, 2018; date of current version October 1, 2018.
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