PurposeIn typical MRI protocols, time is spent acquiring a field map to calculate the shim settings for best image quality. We propose a fast template‐based field map prediction method that yields near‐optimal shims without measuring the field.MethodsThe template‐based prediction method uses prior knowledge of the B0 distribution in the human brain, based on a large database of field maps acquired from different subjects, together with subject‐specific structural information from a quick localizer scan. The shimming performance of using the template‐based prediction is evaluated in comparison to a range of potential fast shimming methods.ResultsStatic B0 shimming based on predicted field maps performed almost as well as shimming based on individually measured field maps. In experimental evaluations at 7 T, the proposed approach yielded a residual field standard deviation in the brain of on average 59 Hz, compared with 50 Hz using measured field maps and 176 Hz using no subject‐specific shim.ConclusionsThis work demonstrates that shimming based on predicted field maps is feasible. The field map prediction accuracy could potentially be further improved by generating the template from a subset of subjects, based on parameters such as head rotation and body mass index. Magn Reson Med 80:171–180, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Slice-wise shimming can improve field homogeneity, but suffers from large noise propagation in the shim calculation. Here, we propose a robust shim current optimization for higher-order dynamic shim updating, based on Tikhonov regularization with a variable regularization parameter, .Theory and Methods: was selected for each slice separately in a fully automatic procedure based on a combination of boundary constraints and an L-curve search algorithm. Shimming performance was evaluated for second order slicewise shimming of the brain at 7T, by simulation on a database of field maps from 143 subjects, and by direct measurements in 8 subjects.Results: Simulations yielded on average 36% reduction in the shim current norm for just 0.4 Hz increase in residual field SD as compared to unconstrained unregularized optimization. In vivo results yielded on average 34.0 Hz residual field SD as compared to 34.3 Hz with a constrained unregularized optimization, while simultaneously reducing the shim current norm to 2.8 A from 3.9 A. The proposed regularization also reduced the average step in the shim current between slices. Conclusion:Slice-wise variable Tikhonov regularization yielded reduced current norm and current steps to a negligible cost in field inhomogeneity. The method holds promise to increase the robustness, and thereby the utility, of higher-order shim updating. K E Y W O R D Sdynamic B 0 shimming, higher-order shimming, regularization, shim optimization | INTRODUCTIONMost MR acquisitions benefit from a homogeneous background magnetic field, especially imaging techniques relying on fast echo planar imaging (EPI) readouts, such as functional MRI and diffusion-weighted imaging. The B 0 field is, however, distorted by differences in magnetic susceptibility between air and tissue, causing image artifacts, such as signal loss, blurring, and geometric distortion. To counteract the B 0 field inhomogeneity, modern MR