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.

翻译：基于扩散模型的磁共振成像重建方法通常通过逐步去除人工添加的噪声并施加数据一致性约束来重建底层图像。然而，实际临床MRI采集过程中，由于热涨落效应，原始数据已包含固有噪声。这种现象在使用超快速高分辨率成像序列进行前沿研究时尤为显著，或是采用中低收入国家常用的低场强系统时。这些常见场景可能导致现有基于扩散模型的重建技术出现性能次优甚至完全失效。具体而言，当人工添加的噪声被逐步去除时，固有MRI噪声会愈发突出，导致实际噪声水平与预设去噪调度不一致，进而造成图像重建误差。针对这一问题，我们提出了一种后验采样策略，其中引入了创新的噪声水平自适应数据一致性（Nila-DC）操作。在覆盖0.3T至3T场强的两个公开数据集及一个内部临床数据集上的大量实验表明，本方法全面超越当前最先进的MRI重建方法，并对不同噪声水平具有强鲁棒性。代码将在评审后公开。