In general, diffusion model-based MRI reconstruction methods incrementally
remove artificially added noise while imposing data consistency to reconstruct
the underlying images. However, real-world MRI acquisitions already contain
inherent noise due to thermal fluctuations. This phenomenon is particularly
notable when using ultra-fast, high-resolution imaging sequences for advanced
research, or using low-field systems favored by low- and middle-income
countries. These common scenarios can lead to sub-optimal performance or
complete failure of existing diffusion model-based reconstruction techniques.
Specifically, as the artificially added noise is gradually removed, the
inherent MRI noise becomes increasingly pronounced, making the actual noise
level inconsistent with the predefined denoising schedule and consequently
inaccurate image reconstruction. To tackle this problem, we propose a posterior
sampling strategy with a novel NoIse Level Adaptive Data Consistency (Nila-DC)
operation. Extensive experiments are conducted on two public datasets and an
in-house clinical dataset with field strength ranging from 0.3T to 3T, showing
that our method surpasses the state-of-the-art MRI reconstruction methods, and
is highly robust against various noise levels. The code will be released after
review.

通过采用新的 NoIse Level Adaptive Data Consistency (Nila-DC) 操作，我们提出了一种后验采样策略来解决扩散模型 MRI 重建技术在存在固有噪声的情况下表现不佳的问题，实验证明我们的方法在各种噪声水平下超越了目前最先进的 MRI 重建方法，并且表现出很高的鲁棒性。