  Magnetic resonance images (MRI) acquired with low through-plane resolution
compromise time and cost. The poor resolution in one orientation is
insufficient to meet the requirement of high resolution for early diagnosis of
brain disease and morphometric study. The common Single image super-resolution
(SISR) solutions face two main challenges: (1) local detailed and global
anatomical structural information combination; and (2) large-scale restoration
when applied for reconstructing thick-slice MRI into high-resolution (HR)
iso-tropic data. To address these problems, we propose a novel two-stage
network for brain MRI SR named TransMRSR based on the convolutional blocks to
extract local information and transformer blocks to capture long-range
dependencies. TransMRSR consists of three modules: the shallow local feature
extraction, the deep non-local feature capture, and the HR image
reconstruction. We perform a generative task to encapsulate diverse priors into
a generative network (GAN), which is the decoder sub-module of the deep
non-local feature capture part, in the first stage. The pre-trained GAN is used
for the second stage of SR task. We further eliminate the potential latent
space shift caused by the two-stage training strategy through the
self-distilled truncation trick. The extensive experiments show that our method
achieves superior performance to other SSIR methods on both public and private
datasets. Code is released at https://github.com/goddesshs/TransMRSR.git .

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning