  Deep learning approaches to accelerated MRI take a matrix of sampled
Fourier-space lines as input and produce a spatial image as output. In this
work we show that by careful choice of the offset used in the sampling
procedure, the symmetries in k-space can be better exploited, producing higher
quality reconstructions than given by standard equally-spaced samples or
randomized samples motivated by compressed sensing.
