AI-based Dual-domain Framework for Gridline Suppression in Digital Radiography

Khamseh S, Wolz F, Scheuplein J, Ergler T, Maier A (2026)

Publication Type: Conference contribution

Publication year: 2026

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer

Pages Range: 337-344

Conference Proceedings Title: Bildverarbeitung für die Medizin 2026

Event location: Lübeck DE

ISBN: 9783658510992

DOI: 10.1007/978-3-658-51100-5_67

Abstract

Anti-scatter grids enhance image contrast in digital radiography but can introduce gridline artifacts when grid and detector sampling frequencies misalign. We propose a dual-domain AI framework that integrates a frozen DINO Vision Transformer with a FiLM-conditioned U-Net, combining global semantic encoding with frequency-aware reconstruction. The DINO embeddings modulate U-Net activations via Feature-wise Linear Modulation, enabling anatomically consistent correction through the joint prediction of a spatial residual and a frequency-domain attenuation mask, which are adaptively fused to suppress gridline artifacts while preserving tone and structural detail. A physics-based synthetic dataset comprising 1,475 clean and 4,425 grid-contaminated radiographs was generated from real detector captures. On 555 test images with available clean references, the model achieved a mean PSNR of 36.9 dB and an SSIM of 0.98, demonstrating high-fidelity and structurally consistent reconstruction across varying grid frequencies and exposure conditions.

Authors with CRIS profile

Joshua Scheuplein Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens Healthineers (Germany) DE Germany (DE)

How to cite

APA:

Khamseh, S., Wolz, F., Scheuplein, J., Ergler, T., & Maier, A. (2026). AI-based Dual-domain Framework for Gridline Suppression in Digital Radiography. In Bildverarbeitung für die Medizin 2026 (pp. 337-344). Lübeck, DE: Springer.

MLA:

Khamseh, Shadi, et al. "AI-based Dual-domain Framework for Gridline Suppression in Digital Radiography." Proceedings of the German Conference on Medical Image Computing, Lübeck Springer, 2026. 337-344.

BibTeX: Download