Denoising diffusion models offer a promising approach to accelerating
magnetic resonance imaging (MRI) and producing diagnostic-level images in an
unsupervised manner. However, our study demonstrates that even tiny worst-case
potential perturbations transferred from a surrogate model can cause these
models to generate fake tissue structures that may mislead clinicians. The
transferability of such worst-case perturbations indicates that the robustness
of image reconstruction may be compromised due to MR system imperfections or
other sources of noise. Moreover, at larger perturbation strengths, diffusion
models exhibit Gaussian noise-like artifacts that are distinct from those
observed in supervised models and are more challenging to detect. Our results
highlight the vulnerability of current state-of-the-art diffusion-based
reconstruction models to possible worst-case perturbations and underscore the
need for further research to improve their robustness and reliability in
clinical settings.

通过研究发现，即使微小的最坏情况扰动由替代模型传递而来，去噪扩散模型仍然能够生成可能误导临床医生的假组织结构，这种扰动的可转移性表明由于磁共振系统的不完善或其他噪声源，图像重构的鲁棒性可能会受到损害。此外，在更大的扰动强度下，扩散模型会产生与监督模型中观察到的不同且更具挑战性的类似高斯噪声的伪影，这强调了当前最先进的基于扩散的重构模型对可能的最坏情况扰动的脆弱性，并强调了在临床环境中改善其鲁棒性和可靠性的进一步研究的必要性。