Tasharofi F, Fan F, Qahqaie M, Thies M, Maier A (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 15972 LNCS
Pages Range: 192-201
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Daejeon, KOR
ISBN: 9783032051684
DOI: 10.1007/978-3-032-05169-1_19
Metal artifacts, caused by high-density metallic implants in computed tomography (CT) imaging, severely degrade image quality, complicating diagnosis and treatment planning. While existing deep learning algorithms have achieved notable success in Metal Artifact Reduction (MAR), they often struggle to suppress artifacts while preserving structural details. To address this challenge, we propose FIND-Net (Fourier-Integrated Network with Dictionary Kernels), a novel MAR framework that integrates frequency and spatial domain processing to achieve superior artifact suppression and structural preservation. FIND-Net incorporates Fast Fourier Convolution (FFC) layers and trainable Gaussian filtering, treating MAR as a hybrid task operating in both spatial and frequency domains. This approach enhances global contextual understanding and frequency selectivity, effectively reducing artifacts while maintaining anatomical structures. Experiments on synthetic datasets show that FIND-Net achieves statistically significant improvements over state-of-the-art MAR methods, with a 3.07% MAE reduction, 0.18% SSIM increase, and 0.90% PSNR improvement, confirming robustness across varying artifact complexities. Furthermore, evaluations on real-world clinical CT scans confirm FIND-Net’s ability to minimize modifications to clean anatomical regions while effectively suppressing metal-induced distortions. These findings highlight FIND-Net’s potential for advancing MAR performance, offering superior structural preservation and improved clinical applicability. Code is available at (https://github.com/Farid-Tasharofi/FIND-Net).
APA:
Tasharofi, F., Fan, F., Qahqaie, M., Thies, M., & Maier, A. (2026). FIND-Net – Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction. In James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Jinah Park, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim (Eds.), Lecture Notes in Computer Science (pp. 192-201). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
MLA:
Tasharofi, Farid, et al. "FIND-Net – Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction." Proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, Daejeon, KOR Ed. James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Jinah Park, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim, Springer Science and Business Media Deutschland GmbH, 2026. 192-201.
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