Dual-domain fusion network for metal artifact reduction in CT

Jiayi Wu; Yuan Li; Zhe Wang; Huamin Wang; Maurizio S. Tonetti; Guohua Cao

doi:10.1117/12.3006099

1 April 2024 Dual-domain fusion network for metal artifact reduction in CT

Jiayi Wu, Yuan Li, Zhe Wang, Huamin Wang, Maurizio S. Tonetti, Guohua Cao

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292523 (2024) https://doi.org/10.1117/12.3006099
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Metal artifacts in CT images severely degrade image quality and decrease the diagnostic value of CT examinations. In this study, a dual-domain deep learning neural network framework that fuses information from both the sinogram and image domains is proposed. Our approach leverages the powers from the imaging-physics-driven model and the data-driven approach. It involves a sinogram improvement module, an image enhancement module, and an image fusion module, and promotes mutual learning from training data in both the sinogram domain and image domain. The experimental results show that our method can successfully reduce metal artifacts while preserving the tissue structures in the regions near the metal implants.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiayi Wu, Yuan Li, Zhe Wang, Huamin Wang, Maurizio S. Tonetti, and Guohua Cao "Dual-domain fusion network for metal artifact reduction in CT", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292523 (1 April 2024); https://doi.org/10.1117/12.3006099

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