Noise-Level Adaptive Diffusion Priors for Zero-Shot Low-Field MRI Quality Enhancement.

Low-field MRI (LF-MRI) improves imaging accessibility, but its practical utility is often limited by reduced signal-to-noise ratio, weaker tissue contrast, and variable image quality. These challenges are further compounded by the limited availability of low-field training data across scanners and the fact that raw k-space data are not always accessible in research and clinical workflows. In this study, we adapted a high-field-trained diffusion model for LF-MRI quality enhancement without low-field retraining or fine-tuning. During inference, the pretrained diffusion prior was combined with noise-level adaptive measurement guidance to suppress low-field noise while maintaining consistency with measurement-supported anatomical structures. With phase augmentation, this framework was further extended to magnitude-only DICOM-exported inputs when measured phase information was unavailable. The method was evaluated on 10 locally recruited healthy volunteers scanned at 0.05, 0.3, and 3 T, a public 0.064-T healthy-subject dataset with 10 paired cases, and patient cases acquired at 0.05 and 0.35 T. Compared with raw LF input and representative baselines including BM3D, MiDiffusion, and NAFNet, the proposed method (Nila) improved visual image quality, noise suppression, and tissue contrast, while preserving lesion-region appearance in patient cases. On the 0.3 and 0.064 T datasets with coregistered 3-T references, Nila achieved the best LPIPS and the highest or tied-highest NMI across contrasts, indicating improved similarity to the high-field reference. Multisample posterior inference further produced uncertainty maps that provide an explainable-AI view of spatial output variability, with elevated variance mainly localized to tissue boundaries and ambiguous regions. These results suggest that high-field diffusion priors can serve as practical and reusable tools for LF-MRI enhancement across heterogeneous systems.