Filter2Noise: a framework for interpretable and zero-shot low-dose CT image denoising

Sun Y, Schneider LS, Mei S, Wang J, Hu G, Gu M, Ye C, Wagner F, Song L, Bayer S, Maier A (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Book Volume: 13

Article Number: 024004

Journal Issue: 2

DOI: 10.1117/1.JMI.13.2.024004

Abstract

Purpose: Deep learning has achieved remarkable progress in low-dose computed tomography (LDCT) denoising; however, radiologists struggle to trust black-box models they cannot verify or control. Zero-shot methods eliminate training data requirements but fail on computed tomography’s (CT) spatially correlated noise. We demonstrate that a transparent mathematical operator, when made content-adaptive, can match deep learning performance while remaining fully interpretable. Approach: We introduce Filter2Noise (F2N), which replaces conventional deep networks with an attention-guided bilateral filter that adapts to local anatomy. A lightweight attention module (3.6k parameters) predicts optimal filtering strategies for each image region by analyzing tissue type, texture, and noise characteristics. To enable robust learning from a single noisy image with correlated noise, we develop Euclidean local shuffle, which strategically disrupts noise correlations while preserving anatomical structure, and a multi-scale self-supervised loss that enforces consistency across resolutions. Results: On the Mayo Clinic LDCT Grand Challenge, F2N achieves 39.76 dB peak signal-to-noise ratio, outperforming the next-best zero-shot method by 1.88 dB, while using 360× fewer parameters (3.6k versus 1.3M). Clinical validation on photon-counting CT demonstrates that F2N elevates low-dose images to full-dose quality (no statistically significant difference in contrast-to-noise ratio, p=0.10). The learned filtering strategy is fully visualizable: parameter maps reveal content-aware behavior. Radiologists can interactively adjust these parameters post-training to refine denoising in diagnostically critical regions. Conclusions: F2N reconciles competitive performance with complete interpretability and user control, providing radiologists with a verifiable tool that works across scanners and protocols without retraining.

Authors with CRIS profile

Involved external institutions

Peking Union Medical College Hospital (PUMCH) / Beijing Xiehe Hospital China (CN) Siemens Healthineers (Germany) Germany (DE)

How to cite

APA:

Sun, Y., Schneider, L.-S., Mei, S., Wang, J., Hu, G., Gu, M.,... Maier, A. (2026). Filter2Noise: a framework for interpretable and zero-shot low-dose CT image denoising. Journal of Medical Imaging, 13(2). https://doi.org/10.1117/1.JMI.13.2.024004

MLA:

Sun, Yipeng, et al. "Filter2Noise: a framework for interpretable and zero-shot low-dose CT image denoising." Journal of Medical Imaging 13.2 (2026).

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