Metal-conscious Embedding for CBCT Projection Inpainting

Fan F, Wang Y, Ritschl L, Biniazan R, Beister M, Kreher BW, Huang Y, Kappler S, Maier A (2023)

Publication Type: Conference contribution

Publication year: 2023

Publisher: IEEE

Conference Proceedings Title: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

Event location: Cartagena

DOI: 10.1109/ISBI53787.2023.10230412


The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection in-painting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced.

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Fan, F., Wang, Y., Ritschl, L., Biniazan, R., Beister, M., Kreher, B.W.,... Maier, A. (2023). Metal-conscious Embedding for CBCT Projection Inpainting. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). Cartagena: IEEE.


Fan, Fuxin, et al. "Metal-conscious Embedding for CBCT Projection Inpainting." Proceedings of the IEEE International Symposium on Biomedical Imaging, Cartagena IEEE, 2023.

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