Spatial attention-based implicit neural representation for arbitrary reduction of MRI slice spacing

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.