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Abstract #5045

Deep learning brain conductivity mapping using a patch-based 3D U-net

Nils Hampe1,2, Ulrich Katscher1, Cornelius A.T. van den Berg3,4, Khin Khin Tha5, and Stefano Mandija3,4

1Philips Research Laboratories, Hamburg, Germany, 2University of Lubeck, Lubeck, Germany, 3University Medical Center Utrecht, Utrecht, Netherlands, 4Utrecht University, Utrecht, Netherlands, 5Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan

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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