Deep learning (DL) techniques have been extensively employed in magnetic
resonance imaging (MRI) reconstruction, delivering notable performance
enhancements over traditional non-DL methods. Nonetheless, recent studies have
identified vulnerabilities in these models during testing, namely, their
susceptibility to (\textit{i}) worst-case measurement perturbations and to
(\textit{ii}) variations in training/testing settings like acceleration factors
and k-space sampling locations. This paper addresses the robustness challenges
by leveraging diffusion models. In particular, we present a robustification
strategy that improves the resilience of DL-based MRI reconstruction methods by
utilizing pretrained diffusion models as noise purifiers. In contrast to
conventional robustification methods for DL-based MRI reconstruction, such as
adversarial training (AT), our proposed approach eliminates the need to tackle
a minimax optimization problem. It only necessitates fine-tuning on purified
examples. Our experimental results highlight the efficacy of our approach in
mitigating the aforementioned instabilities when compared to leading
robustification approaches for deep MRI reconstruction, including AT and
randomized smoothing