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

Wagner F, Thies M, Pfaff L, Maul N, Pechmann S, Gu M, Utz J, Aust O, Weidner D, Neag G, Uderhardt S, Choi JH, Maier A (2023)

Publication Type: Conference contribution

Publication year: 2023

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13939 LNCS

Pages Range: 771-782

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: San Carlos de Bariloche, ARG

ISBN: 9783031340475

DOI: 10.1007/978-3-031-34048-2_59

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.

Authors with CRIS profile

Involved external institutions

Fraunhofer-Institut für Keramische Technologien und Systeme (IKTS) Germany (DE) Ewha Womans University / 이화여자대학교 Korea, Republic of (KR)

How to cite

APA:

Wagner, F., Thies, M., Pfaff, L., Maul, N., Pechmann, S., Gu, M.,... Maier, A. (2023). Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising. In Alejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 771-782). San Carlos de Bariloche, ARG: Springer Science and Business Media Deutschland GmbH.

MLA:

Wagner, Fabian, et al. "Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising." Proceedings of the 28th International Conference on Information Processing in Medical Imaging, IPMI 2023, San Carlos de Bariloche, ARG Ed. Alejandro Frangi, Marleen de Bruijne, Demian Wassermann, Nassir Navab, Springer Science and Business Media Deutschland GmbH, 2023. 771-782.

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