AniRes2D: anisotropic residual-enhanced diffusion for 2D MR super-resolution

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

doi:10.1117/12.3008456

2 April 2024 AniRes2D: anisotropic residual-enhanced diffusion for 2D MR super-resolution

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

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 1293029 (2024) https://doi.org/10.1117/12.3008456
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zejun Wu, Samuel W. Remedios, Blake E. Dewey, Aaron Carass, and Jerry L. Prince "AniRes2D: anisotropic residual-enhanced diffusion for 2D MR super-resolution", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 1293029 (2 April 2024); https://doi.org/10.1117/12.3008456

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