  The thick-slice magnetic resonance (MR) images are often structurally blurred
in coronal and sagittal views, which causes harm to diagnosis and image
post-processing. Deep learning (DL) has shown great potential to re-construct
the high-resolution (HR) thin-slice MR images from those low-resolution (LR)
cases, which we refer to as the slice interpolation task in this work. However,
since it is generally difficult to sample abundant paired LR-HR MR images, the
classical fully supervised DL-based models cannot be effectively trained to get
robust performance. To this end, we propose a novel Two-stage Self-supervised
Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a
two-stage self-supervised learning (SSL) strategy is developed for unsupervised
DL network training. The paired LR-HR images are synthesized along the sagittal
and coronal directions of input LR images for network pretraining in the
first-stage SSL, and then a cyclic in-terpolation procedure based on triplet
axial slices is designed in the second-stage SSL for further refinement. More
training samples with rich contexts along all directions are exploited as
guidance to guarantee the improved in-terpolation performance. Moreover, a new
cycle-consistency constraint is proposed to supervise this cyclic procedure,
which encourages the network to reconstruct more realistic HR images. The
experimental results on a real MRI dataset indicate that TSCNet achieves
superior performance over the conventional and other SSL-based algorithms, and
obtains competitive quali-tative and quantitative results compared with the
fully supervised algorithm.
