Deep Learning Based Local SAR Prediction for Head imaging at 7T: applicability and accuracy for unknown head coils

Motivation: The methods presented for on-line local SAR evaluation require access to geometric design details of the transmit coil which are not always available. Goal(s): Evaluate the generalization capabilities of deep learning-based methods when they are used to assess the local SAR distribution for coils not included in the training data. Approach: We built a diverse synthetic dataset four different coils and trained a neural network: using only samples from each coil, and using samples from all coils except one. Results: Including a reasonably wide variety of coils in the training process enables local SAR assessment without knowing the design details of the coil. Impact: The lack of access to design details of the coil makes it challenging to transition the more advanced local SAR assessment methods into clinical practice. Training with a diverse set of coils could enable local SAR assessment without coil information.