Year: 2024
Type: conference-paper
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.
DOI: 10.1117/12.3008456
Authors: Zejun Wu, Samuel W. Remedios, Blake E. Dewey, Aaron Carass, Jerry L. Prince
Institutions: Johns Hopkins University, Johns Hopkins Medicine
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4391877901