Pose-dependent field reconstruction enabled by prospective motion navigation and randomized sampling

Head motion is accompanied by a multitude of second order motion effects like changing coil sensitivity maps, background field inhomogeneities, and susceptibility-induced fields. While scan geometries and shims can be corrected in real-time, the scanner has no means to counteract changes of the coil sensitivity maps or the susceptibility-induced fields requiring data-driven retrospective motion correction algorithms for these issues. Randomized sampling can further be exploited to improve the problem conditioning of the parameter estimations on temporal sub-segments of the scan. In this work, we evaluate the combination prospective motion navigation with randomized sampling in a pose-dependent field correction algorithm.