  Electrical properties (EPs) of tissues, conductivity and permittivity, are
modulated by the ionic and water content, which change in presence of
pathologies. Information on tissues EPs can be used e.g. as an endogenous
biomarker in oncology. MR-Electrical Properties Tomography (MR-EPT) aims to
reconstruct tissue EPs by solving an electromagnetic inverse problem relating
MR measurements of the transmit radiofrequency RF field to the EPs. However,
MR-EPT reconstructions highly suffer from noise in the RF field maps, which
limits the clinical applicability. Instead of employing electromagnetic models
posing strict requirements on the measured quantities, we propose a data driven
approach where the inverse transformation is learned by means of a neural
network. Supervised training of a conditional generative adversarial neural
network was performed using simulated realistic RF field maps and realistic
human head dielectric models. Deep learning EPT (DL-EPT) reconstructions are
presented for in-silica MR data and MR measurements at 3 Tesla on phantoms and
human brains. DL-EPT shows high quality EP maps, demonstrating good accuracy
and greatly improved precision compared to conventional MR-EPT. Moreover,
DL-EPT allows permittivity reconstructions at 3 Tesla, which is not possible
with state-of-art MR-EPT techniques. The supervised learning-based approach
leverages the strength of tailored electromagnetic simulations, allowing
inclusion of a priori information (e.g. coil setup) and circumvention of
inaccessible MR electromagnetic quantities. Since DL-EPT is highly
noise-robust, the requirements for MRI data acquisitions can be relaxed,
allowing faster acquisitions and higher resolutions. We believe that DL-EPT
greatly improves the quality and applicability of EPT opening a new window for
an endogenous biomarker in MRI diagnostics that reflects differences in ionic
tissue content.
