AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution
This addresses the challenge of improving automated processing for fast-acquired but low-resolution MR images, though it appears incremental as it builds on existing DDPM methods with residual prediction.
The paper tackled the problem of super-resolving anisotropic low-resolution 2D MR images using denoising diffusion probabilistic models (DDPMs), resulting in AniRes2D outperforming other DDPM-based models in metrics like quantitative scores and visual quality, and achieving reduced skull aliasing compared to a state-of-the-art 3D method.
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.