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 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.

中文翻译：

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基于空间注意力的隐式神经表示，用于任意减小 MRI 切片间距

在 2D 临床方案中收集的磁共振 (MR) 图像通常具有较大的层间距，导致面内分辨率较高，但平面分辨率较低。超分辨率技术可以增强MR图像的平面分辨率，以促进下游可视化和计算机辅助诊断。然而，大多数现有工作以固定比例因子训练超分辨率网络，这对于磁共振扫描中不同层间距的临床场景并不友好。受隐式神经表示最新进展的启发，我们提出了一种用于任意减少 MR 切片间距的网络。 SA-INR 旨在将 MR 图像表示为 3D 坐标的连续隐式函数。这样，SA-INR可以通过连续采样3D空间中的坐标来重建任意层间距的MR图像。特别是，引入了局部感知空间注意操作，以在更大的感受野中更准确地对附近的体素及其亲和力进行建模。同时，为了提高计算效率，提出了一种梯度引导门控掩模，用于仅将局部感知空间注意力应用于选定区域。我们在公共 HCP-1200 数据集和临床膝关节 MR 数据集上评估我们的方法，以证明其相对于其他现有方法的优越性。