Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing
This work addresses a practical issue in clinical MRI scanning by enabling arbitrary reduction of slice spacing, potentially improving visualization and diagnosis, though it appears incremental as it builds on implicit neural representation techniques.
The paper tackles the problem of enhancing through-plane resolution in MRI images with varying inter-slice spacing by proposing a Spatial Attention-based Implicit Neural Representation (SA-INR) network, which achieves superior performance on public and clinical datasets compared to existing methods.
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.