  Magnetic resonance-electrical properties tomography (MR-EPT) is a technique
used to estimate the conductivity and permittivity of tissues from MR
measurements of the transmit magnetic field. Different reconstruction methods
are available, however all these methods present several limitations which
hamper the clinical applicability. Standard Helmholtz based MR-EPT methods are
severely affected by noise. Iterative reconstruction methods such as contrast
source inversion-EPT (CSI-EPT) are typically time consuming and are dependent
on their initialization. Deep learning (DL) based methods require a large
amount of training data before sufficient generalization can be achieved. Here,
we investigate the benefits achievable using a hybrid approach, i.e. using
MR-EPT or DL-EPT as initialization guesses for standard 3D CSI-EPT. Using
realistic electromagnetic simulations at 3 T and 7 T, the accuracy and
precision of hybrid CSI reconstructions are compared to standard 3D CSI-EPT
reconstructions. Our results indicate that a hybrid method consisting of an
initial DL-EPT reconstruction followed by a 3D CSI-EPT reconstruction would be
beneficial. DL-EPT combined with standard 3D CSI-EPT exploits the power of data
driven DL-based EPT reconstructions while the subsequent CSI-EPT facilitates a
better generalization by providing data consistency.
