Can I read AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution on EtoBox?

AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution by Wu, Zejun; Remedios, Samuel W.; Dewey, Blake E.; Carass, Aaron; Prince, Jerry L. is a scholarly article available to read on EtoBox.

What is AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution about?

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.

Author

Wu, Zejun; Remedios, Samuel W.; Dewey, Blake E.; Carass, Aaron; Prince, Jerry L.

Published

2023

Language

EN