  Subject movement during the magnetic resonance examination is inevitable and
causes not only image artefacts 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, e.g. induced by patient movement, is
important for MR applications that are prone to B0 inhomogeneities. We 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 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-tuned 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. Our 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. We therefore conclude that it 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 magnetic resonance
acquisitions without the use of navigators.
