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| Home > In process > Rethinking Real-World MRI Denoising: Learning from Physical Noise |
| Contribution to a conference proceedings/Contribution to a book | DZNE-2026-01059 |
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2026
Springer Nature Switzerland
Cham
ISBN: 978-3-032-37434-9 (print), 978-3-032-37435-6 (electronic)
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Please use a persistent id in citations: doi:10.1007/978-3-032-37435-6_27
Abstract: Magnetic resonance imaging (MRI) inherently suffers from noise, which limits downstream medical analyses. In MRI, noise-free images are unobtainable; therefore, existing denoising approaches formulate surrogate training objectives, compromising between preserving detail and concealing noise, causing domain shifts or incomplete denoising. To enable denoiser training directly on unmodified, noisy images, we exploit repeated acquisitions. This naturally constitutes a physical Noise2Noise (pN2N) setting. For unrepeated data, we introduce a diffusion-based re-noiser that synthesizes noisy image pairs, extending pN2N to Renoise2Noise (ReN2N). Furthermore, we demonstrate that ReN2N improves generalization to unseen datasets. Additionally, we propose to combine pN2N or ReN2N with optional guidance from co-acquired contrast, yielding four versions of our novel denoising framework: YADO (You Accurately Denoise real Observations). Across 14 test conditions, YADO consistently outperforms 17 state-of-the-art baselines, matching the quality of physically noise-suppressed images obtained via brute-force averaging of independent acquisitions. YADO thus establishes practical denoising for real-world acquisition settings.
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