Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising

Fabian Wagner, Mareike Thies, Laura Pfaff, Noah Maul, Sabrina Pechmann, Mingxuan Gu, Jonas Utz, Oliver Aust, Daniela Weidner, Georgiana Neag, Stefan Uderhardt, Jang Hwan Choi, Andreas Maier

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured image contrasts to train a denoising model. We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss. The trained denoising operator achieves convincing quantitative and qualitative results, outperforming state-of-the-art self-supervised methods by 4.7–11.0%/4.8–7.3% (PSNR/SSIM) on brain MRI data and by 43.6–50.5%/57.1–77.1% (PSNR/SSIM) on dual-energy CT X-ray microscopy data with respect to the noisy baseline. Our experiments on different real measured data sets indicate that Noise2Contrast training generalizes to other multi-contrast imaging modalities.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings
EditorsAlejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031340475
StatePublished - 2023
Event28th International Conference on Information Processing in Medical Imaging, IPMI 2023 - San Carlos de Bariloche, Argentina
Duration: 18 Jun 202323 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13939 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Information Processing in Medical Imaging, IPMI 2023
CitySan Carlos de Bariloche

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Contrast Fusion
  • Known Operator Learning
  • Self-Supervised Denoising


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