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These challenges are further compounded by the limited availability of low\u2010field training data across scanners and the fact that raw k\u2010space data are not always accessible in research and clinical workflows. In this study, we adapted a high\u2010field\u2010trained diffusion model for LF\u2010MRI quality enhancement without low\u2010field retraining or fine\u2010tuning. During inference, the pretrained diffusion prior was combined with noise\u2010level adaptive measurement guidance to suppress low\u2010field noise while maintaining consistency with measurement\u2010supported anatomical structures. With phase augmentation, this framework was further extended to magnitude\u2010only DICOM\u2010exported 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\u2009T, a public 0.064\u2010T healthy\u2010subject dataset with 10 paired cases, and patient cases acquired at 0.05 and 0.35\u2009T. 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\u2010region appearance in patient cases. On the 0.3 and 0.064\u2009T datasets with coregistered 3\u2010T references, Nila achieved the best LPIPS and the highest or tied\u2010highest NMI across contrasts, indicating improved similarity to the high\u2010field reference. Multisample posterior inference further produced uncertainty maps that provide an explainable\u2010AI view of spatial output variability, with elevated variance mainly localized to tissue boundaries and ambiguous regions. These results suggest that high\u2010field diffusion priors can serve as practical and reusable tools for LF\u2010MRI enhancement across heterogeneous systems.<\/jats:p>","DOI":"10.1002\/nbm.70373","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T14:12:06Z","timestamp":1786716726000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noise\u2010Level Adaptive Diffusion Priors for Zero\u2010Shot Low\u2010Field MRI Quality Enhancement"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5832-5895","authenticated-orcid":false,"given":"Jiacai","family":"Cai","sequence":"first","affiliation":[{"name":"College of Health Science and Environmental Engineering Shenzhen Technology University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6094-129X","authenticated-orcid":false,"given":"Shoujin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Health Science and Environmental Engineering Shenzhen Technology University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4925-6910","authenticated-orcid":false,"given":"Zihao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Health Science and Environmental Engineering Shenzhen Technology University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Health Science and Environmental Engineering Shenzhen Technology University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1656-9336","authenticated-orcid":false,"given":"Huaishui","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Health Science and Environmental Engineering Shenzhen Technology University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1072-5983","authenticated-orcid":false,"given":"Min","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science Zhejiang University  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingfang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Shanghai Soundwise Technology Co. 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