Rethinking Real-World MRI Denoising: Learning from Physical Noise

Magnetic resonance imaging (MRI) inherently suffers fromnoise, which limits downstream medical analyses. In MRI, noise-freeimages are unobtainable; therefore, existing denoising approaches for-mulate surrogate training objectives, compromising between preservingdetail and concealing noise, causing domain shifts or incomplete de-noising. To enable denoiser training directly on unmodified, noisy im-ages, we exploit repeated acquisitions. This naturally constitutes a phys-ical Noise2Noise (pN2N) setting. For unrepeated data, we introducea diffusion-based re-noiser that synthesizes noisy image pairs, extend-ing pN2N to Renoise2Noise (ReN2N). Furthermore, we demonstratethat ReN2N improves generalization to unseen datasets. Additionally,we propose to combine pN2N or ReN2N with optional guidance fromco-acquired contrast, yielding four versions of our novel denoising frame-work: YADO (You Accurately Denoise real Observations). Across 14 testconditions, YADO consistently outperforms 17 state-of-the-art baselines,matching the quality of physically noise-suppressed images obtained viabrute-force averaging of independent acquisitions. YADO thus estab-lishes practical denoising for real-world acquisition settings.