AbstractPurposeSubject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B₀), which is a prerequisite for high quality data. Thus, characterization of changes to B₀, for example induced by patient movement, is important for MR applications that are prone to B₀ inhomogeneities.MethodsWe propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real‐time correction. A 3D U‐net was trained on in vivo gradient‐echo brain 7T MRI data. The input consisted of B₀ maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid‐body transformation on the initial anatomical image). The output consisted of B₀ maps at the new head positions. We further fine‐trained the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U‐net with these data.ResultsOur approach was compared to established dynamic B₀ field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator‐equivalent method and proposed method.ConclusionIt is feasible to predict B₀ maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of MR acquisitions without the use of navigators.