  To super-resolve the through-plane direction of a multi-slice 2D magnetic
resonance (MR) image, its slice selection profile can be used as the
degeneration model from high resolution (HR) to low resolution (LR) to create
paired data when training a supervised algorithm. Existing super-resolution
algorithms make assumptions about the slice selection profile since it is not
readily known for a given image. In this work, we estimate a slice selection
profile given a specific image by learning to match its internal patch
distributions. Specifically, we assume that after applying the correct slice
selection profile, the image patch distribution along HR in-plane directions
should match the distribution along the LR through-plane direction. Therefore,
we incorporate the estimation of a slice selection profile as part of learning
a generator in a generative adversarial network (GAN). In this way, the slice
selection profile can be learned without any external data. Our algorithm was
tested using simulations from isotropic MR images, incorporated in a
through-plane super-resolution algorithm to demonstrate its benefits, and also
used as a tool to measure image resolution. Our code is at
https://github.com/shuohan/espreso2.
