Sun Y, Schneider LS, Mei S, Ye C, Gu M, Wagner F, Bayer S, Maier A (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 34-39
Conference Proceedings Title: Informatik aktuell
ISBN: 9783658510992
DOI: 10.1007/978-3-658-51100-5_7
Four-dimensional CT (4D-CT) tracks tumor motion throughout the breathing cycle for radiation therapy planning, but dose reduction per phase introduces spatio-temporal noise compromising tumor delineation. Existing learning-based denoising methods are either clinically impractical (requiring paired data) or lack interpretability (black-box networks). We present Filter2Noise-4D (F2N-4D), a zero-shot interpretable framework employing content-adaptive bilateral filtering that exploits spatio-temporal information from neighboring slices. Self-supervised training uses interpolation of neighboring slices to construct training pairs. With only 1.8k parameters, F2N-4D achieves competitive performance while maintaining transparency.
APA:
Sun, Y., Schneider, L.-S., Mei, S., Ye, C., Gu, M., Wagner, F.,... Maier, A. (2026). Interpretable Framework for Zero-shot 4D Low-dose CT Denoising: Filter2Noise-4D. In Heinz Handels, Katharina Breininger, Thomas Deserno, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 34-39). Lübeck, DE: Springer Science and Business Media Deutschland GmbH.
MLA:
Sun, Yipeng, et al. "Interpretable Framework for Zero-shot 4D Low-dose CT Denoising: Filter2Noise-4D." Proceedings of the Bildverarbeitung für die Medizin Workshop, BVM 2026, Lübeck Ed. Heinz Handels, Katharina Breininger, Thomas Deserno, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2026. 34-39.
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