@inproceedings{
ozturkler2023regularization,
title={Regularization by Denoising Diffusion Process for {MRI} Reconstruction},
author={Batu Ozturkler and Morteza Mardani and Arash Vahdat and Jan Kautz and John M. Pauly},
booktitle={NeurIPS 2023 Workshop on Deep Learning and Inverse Problems},
year={2023},
url={https://openreview.net/forum?id=NRGZmGbteB}
}
Diffusion models have recently delivered state-of-the-art performance for MRI reconstruction with improved robustness. However, these models fail when there is a large distribution shift, and their long inference times impede their clinical utility. Recently, regularization by denoising diffusion process (RED-diff) was introduced for solving general inverse problems. RED-diff uses a variational sampler based on a measurement consistency loss and a score matching regularization. In this paper, we extend RED-diff to MRI reconstruction. RED-diff formulates MRI reconstruction as stochastic optimization, and outperforms diffusion baselines in PSNR/SSIM with $3 \times$ faster inference while using the same amount of memory. The code is publicly available at https://github.com/NVlabs/SMRD.
Regularization by Denoising Diffusion Process for MRI Reconstruction
Batu Ozturkler Morteza Mardani Arash Vahdat Jan Kautz John M. Pauly