Robust RF Shimming With Minibatched Magnitude Least Squares Optimization

We present a magnitude-least-squares RF shimming algorithm which uses interleaved noisy and locally converging updates to escape local minima and find low-cost shim solutions. We introduce noise in the algorithm by performing updates using a small minibatch of the available B1+ measurements. The method is validated using in-vivo head B1+ maps from a 7T scanner. An optimal minibatch size is found which consistently produces low-power and low-RMSE solutions across subjects and through head slices. This shim design method may be employed to more robustly correct for B1+ inhomogeneities at ultra-high field strengths than is possible with conventional methods.