  Magnetic resonance (MR) images collected in 2D clinical protocols typically
have large inter-slice spacing, resulting in high in-plane resolution and
reduced through-plane resolution. Super-resolution technique can enhance the
through-plane resolution of MR images to facilitate downstream visualization
and computer-aided diagnosis. However, most existing works train the
super-resolution network at a fixed scaling factor, which is not friendly to
clinical scenes of varying inter-slice spacing in MR scanning. Inspired by the
recent progress in implicit neural representation, we propose a Spatial
Attention-based Implicit Neural Representation (SA-INR) network for arbitrary
reduction of MR inter-slice spacing. The SA-INR aims to represent an MR image
as a continuous implicit function of 3D coordinates. In this way, the SA-INR
can reconstruct the MR image with arbitrary inter-slice spacing by continuously
sampling the coordinates in 3D space. In particular, a local-aware spatial
attention operation is introduced to model nearby voxels and their affinity
more accurately in a larger receptive field. Meanwhile, to improve the
computational efficiency, a gradient-guided gating mask is proposed for
applying the local-aware spatial attention to selected areas only. We evaluate
our method on the public HCP-1200 dataset and the clinical knee MR dataset to
demonstrate its superiority over other existing methods.
