AbstractPurposeWe developed a 3D vision transformer‐based neural network to reconstruct electrical properties (EP) from magnetic resonance measurements.Theory and MethodsOur network uses the magnitude of the transmit magnetic field of a birdcage coil, the associated transceive phase, and a Canny edge mask that identifies the object boundaries as inputs to compute the EP maps. We trained our network on a dataset of 10 000 synthetic tissue‐mimicking phantoms and fine‐tuned it on a dataset of 11 000 realistic head models. We assessed performance in‐distribution simulated data and out‐of‐distribution head models, with and without synthetic lesions. We further evaluated our network in experiments for an inhomogeneous phantom and a volunteer.ResultsThe conductivity and permittivity maps had an average peak normalized absolute error (PNAE) of 1.3% and 1.7% for the synthetic phantoms, respectively. For the realistic heads, the average PNAE for the conductivity and permittivity was 1.8% and 2.7%, respectively. The location of synthetic lesions was accurately identified, with reconstructed conductivity and permittivity values within 15% and 25% of the ground‐truth, respectively. The conductivity and permittivity for the phantom experiment yielded 2.7% and 2.1% average PNAEs with respect to probe‐measured values, respectively. The in vivo EP reconstruction truthfully preserved the subject's anatomy with average values over the entire head similar to the expected literature values.ConclusionWe introduced a new learning‐based approach for reconstructing EP from MR measurements obtained with a birdcage coil, marking an important step towards the development of clinically‐usable in vivo EP reconstruction protocols.