On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting

Wagner F, Thies M, Pfaff L, Aust O, Pechmann S, Weidner D, Maul N, Rohleder M, Gu M, Utz J, Denzinger F, Maier A (2023)

Publication Type: Conference contribution

Publication year: 2023

Journal

Publisher: IEEE Computer Society

Book Volume: 2023-April

Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging

Event location: Cartagena

ISBN: 9781665473583

DOI: 10.1109/ISBI53787.2023.10230511

Abstract

Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low-dose acquisitions. However, considerably less research investigates methods already intervening in the raw detector data due to limited access to suitable projection data or correct reconstruction algorithms. In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data. Our experiments demonstrate that including an additional projection denoising operator improved the overall denoising performance by 82.4-94.1 %/12.5-41.7 % (PSNR/SSIM) on abdomen CT and 1.5-2.9 %/0.4-0.5 % (PSNR/SSIM) on XRM data relative to the low-dose baseline. We make our entire helical CT reconstruction framework publicly available that contains a raw projection rebinning step to render helical projection data suitable for differentiable fan-beam reconstruction operators and end-to-end learning.

Authors with CRIS profile

Involved external institutions

Fraunhofer-Institut für Keramische Technologien und Systeme (IKTS) Germany (DE)

How to cite

APA:

Wagner, F., Thies, M., Pfaff, L., Aust, O., Pechmann, S., Weidner, D.,... Maier, A. (2023). On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting. In Proceedings - International Symposium on Biomedical Imaging. Cartagena, CO: IEEE Computer Society.

MLA:

Wagner, Fabian, et al. "On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting." Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena IEEE Computer Society, 2023.

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