  Diffusion models have recently gained popularity for accelerated MRI
reconstruction due to their high sample quality. They can effectively serve as
rich data priors while incorporating the forward model flexibly at inference
time, and they have been shown to be more robust than unrolled methods under
distribution shifts. However, diffusion models require careful tuning of
inference hyperparameters on a validation set and are still sensitive to
distribution shifts during testing. To address these challenges, we introduce
SURE-based MRI Reconstruction with Diffusion models (SMRD), a method that
performs test-time hyperparameter tuning to enhance robustness during testing.
SMRD uses Stein's Unbiased Risk Estimator (SURE) to estimate the mean squared
error of the reconstruction during testing. SURE is then used to automatically
tune the inference hyperparameters and to set an early stopping criterion
without the need for validation tuning. To the best of our knowledge, SMRD is
the first to incorporate SURE into the sampling stage of diffusion models for
automatic hyperparameter selection. SMRD outperforms diffusion model baselines
on various measurement noise levels, acceleration factors, and anatomies,
achieving a PSNR improvement of up to 6 dB under measurement noise. The code is
publicly available at https://github.com/NVlabs/SMRD .
