Electrical Property Mapping using Vision Transformers and Canny Edge Detection

Motivation: To estimate tissue electrical properties (EP) non-invasively for specific absorption rate management and as biomarkers for pathology characterization. Goal(s): To train neural networks for mapping transmit magnetic fields (B1+) onto EP. Approach: We developed a 3D vision transformer that takes the B1+ and an edge mask based on Canny filtering of the MR image as the inputs. The targets were the EP of the object. We trained on simulated tissue mimicking objects and fine-tuned on realistic head models. Results: Our network successfully reconstructed the EP in a phantom experiment, and detected a synthetic cyst in a realistic head model in simulation. Impact: We propose a supervised learning approach using vision transformers and Canny edge detection to perform electrical property (EP) mapping. The network successfully reconstructs the EP using experimentally measured fields and is a promising first step towards clinically-usable in-vivo EP reconstructions.