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 for Nila is available at https://github.com/Solor-pikachu/Nila.

翻译：通常，基于扩散模型的MRI重建方法在逐步去除人为添加的噪声的同时，施加数据一致性约束以重建底层图像。然而，现实世界的MRI采集由于热涨落已包含固有噪声。这一现象在使用超快速、高分辨率成像序列进行前沿研究时尤为显著，或在低收入和中等收入国家偏好的低场强系统中亦常见。这些常见场景可能导致现有基于扩散模型的重建技术性能欠佳或完全失效。具体而言，随着人为添加的噪声被逐步去除，MRI固有噪声日益凸显，使得实际噪声水平与预定义的去噪调度不一致，从而导致图像重建不准确。为解决此问题，我们提出了一种后验采样策略，并引入了一种新颖的噪声水平自适应数据一致性操作。我们在两个公共数据集和一个场强范围从0.3T到3T的内部临床数据集上进行了大量实验，结果表明，我们的方法超越了当前最先进的MRI重建方法，并对各种噪声水平具有高度鲁棒性。Nila的代码可在https://github.com/Solor-pikachu/Nila获取。