  Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to
obtain but hinder automated processing. We propose to use denoising diffusion
probabilistic models (DDPMs) to super-resolve these 2D-acquired LR MR slices.
This paper introduces AniRes2D, a novel approach combining DDPM with a residual
prediction for 2D super-resolution (SR). Results demonstrate that AniRes2D
outperforms several other DDPM-based models in quantitative metrics, visual
quality, and out-of-domain evaluation. We use a trained AniRes2D to
super-resolve 3D volumes slice by slice, where comparative quantitative results
and reduced skull aliasing are achieved compared to a recent state-of-the-art
self-supervised 3D super-resolution method. Furthermore, we explored the use of
noise conditioning augmentation (NCA) as an alternative augmentation technique
for DDPM-based SR models, but it was found to reduce performance. Our findings
contribute valuable insights to the application of DDPMs for SR of anisotropic
MR images.
