Tailored dielectric shimming in MRI using machine learning - a feasibility study

Motivation: Inhomogeneities of the MRI transmit field cause image shading and hinder diagnosis. In dielectric shimming, pads of high permittivity are used to recover signal in low intensity areas, but full-wave calculation of the resulting fields is too slow for real-time use at the scanner. Goal(s): We study feasibility of using AI to rapidly predict the transmit field with dielectric pads. Approach: An AI pipeline is trained using a small simulated data set for proof of concept. Results: We obtain a structural similarity of 97% with a mean squared error of 0.02%, demonstrating feasibility and the potential for a real-time implementation in the future. Impact: This work improves image shading and diagnostics. Dielectric shimming in particular and rapid calculation of electromagnetic fields in general especially apply to ultra-high field strengths such as 7T, 9.4T and 10.5T, where significant inhomogeneity is hindering proper evaluation.