Abstract #3193

On the usage of deep neural networks as a tensor-to-tensor translation between MR measurements and electrical properties

Ilias Giannakopoulos^1,2, José Serallés², Georgy Guryev^1,2, Luca Daniel², Elfar Adalsteinsson^2,3, Lawrence Wald^4,5,6, Daniel Sodickson^7,8,9, Jacob White², and Riccardo Lattanzi^7,8,9

¹Skoltech Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russian Federation, ²Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Institute for Medical Engineering and Science, Cambridge, MA, United States, ⁴Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ⁵Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁶Harvard-MIT Health Sciences and Technology, Cambridge, MA, United States, ⁷Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, United States, ⁸The Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Department of Radiology, New York University School of Medicine, New York, NY, United States, ⁹Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, United States

Electrical properties (EP) can be retrieved from magnetic resonance measurements. We employed numerical simulations to investigate the use of convolutional neural networks (CNN) as a tensor-to-tensor translation between transmit magnetic field pattern ($$$b_1^+$$$) and EP distribution for simple tissue-mimicking phantoms. Given the volumetric nature of the problem, we chose a 3D UNET and trained the network on $$$10000$$$ data. We investigated on the usage of regularization to account for overfitting and observed that multiple dropouts through the layers of the network yield optimal EP reconstructions for $$$1000$$$ testing data.

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