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Magnetic resonance (MR) images collected in 2D scanning protocols typicallyhave large inter-slice spacing, resulting in high in-plane resolution butreduced through-plane resolution. Super-resolution techniques can reduce theinter-slice spacing of 2D scanned MR images, facilitating the downstream visualexperience and computer-aided diagnosis. However, most existingsuper-resolution methods are trained at a fixed scaling ratio, which isinconvenient in clinical settings where MR scanning may have varyinginter-slice spacings. To solve this issue, we propose Hierarchical FeatureConditional Diffusion (HiFi-Diff)} for arbitrary reduction of MR inter-slicespacing. Given two adjacent MR slices and the relative positional offset,HiFi-Diff can iteratively convert a Gaussian noise map into any desiredin-between MR slice. Furthermore, to enable fine-grained conditioning, theHierarchical Feature Extraction (HiFE) module is proposed to hierarchicallyextract conditional features and conduct element-wise modulation. Ourexperimental results on the publicly available HCP-1200 dataset demonstrate thehigh-fidelity super-resolution capability of HiFi-Diff and its efficacy inenhancing downstream segmentation performance.

Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion