  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.
