The inability of Magnetic Resonance Electrical Properties Tomography to accurately reconstruct tissue electrical properties severely limits its clinical use, e.g. as a biomarker in oncology. We demonstrate that the electrical properties reconstruction problem can be casted as a supervised deep learning task. Deep learning based electrical properties reconstructions for simulations and MR measurements (3 Tesla) on phantoms and human brains demonstrate great improvement in the quality of reconstructed electrical properties maps. This could be major step forward to turn electrical properties tomography into a reliable biomarker where pathological conditions can be revealed and characterized by abnormalities in tissue electrical properties.
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