S076 - Regularization by Denoising Diffusion Process for MRI Reconstruction
Batu Ozturkler, Morteza Mardani, Arash Vahdat, Jan Kautz, John M. Pauly
Diffusion models have recently delivered state-of-the-art performance for MRI reconstruction with improved robustness. However, these models still fail when there is a large distribution shift, and their long inference times impede their clinical utility. In this paper, we present regularization by denoising diffusion processes for MRI reconstruction (RED-diff). RED-diff formulates sampling as stochastic optimization, and outperforms diffusion baselines in PSNR/SSIM with 3x faster inference while using the same amount of memory.
Schedule: Tuesday, July 11: Posters — 10:30–12:00 & 15:00–16:00
Poster location: T46