Abstract
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 language | English |
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Title of host publication | Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings |
Editors | Alejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 771-782 |
Number of pages | 12 |
ISBN (Print) | 9783031340475 |
DOIs | |
State | Published - 2023 |
Event | 28th International Conference on Information Processing in Medical Imaging, IPMI 2023 - San Carlos de Bariloche, Argentina Duration: 18 Jun 2023 → 23 Jun 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13939 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 28th International Conference on Information Processing in Medical Imaging, IPMI 2023 |
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Country/Territory | Argentina |
City | San Carlos de Bariloche |
Period | 18/06/23 → 23/06/23 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Contrast Fusion
- Known Operator Learning
- Self-Supervised Denoising