Conventional Electrical Properties Tomography (EPT) suffers from reconstruction artifacts related to assumptions necessary for solving the equations analytically. To circumvent the necessity for these assumptions, in this study a deep learning approach is utilized to approximate the analytically unsolvable equations. For this purpose, a 3D convolutional neural network was trained on simulations and in-vivo data from healthy volunteers and cancer patients. Results demonstrate the potential of this method, as noise-free conductivity maps were obtained without anatomic apriori information in less than 1:30 min per reconstruction.
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