Wavelet domain-based deep residual learning for metal artifact reduction in computed tomography

Seungwan Lee; Seonghee Kang; Youngeun Choi; Chanrok Park

doi:10.1117/12.3005636

1 April 2024 Wavelet domain-based deep residual learning for metal artifact reduction in computed tomography

Seungwan Lee, Seonghee Kang, Youngeun Choi, Chanrok Park

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129251Z (2024) https://doi.org/10.1117/12.3005636
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In this study, we proposed a wavelet domain-based deep residual learning strategy for reducing metal artifacts in computed tomography (CT) images. A fully-connected neural network (FCN) was constructed for learning the end-to-end non-linear mapping between the images including metal artifacts and the residual images. Training CT images were transformed into subband images using the 2D wavelet transformation for providing the high-frequency features during network training. The residual learning was implemented by using the subband images. The performance of the proposed technique was compared to that of the O-MAR algorithm. The results showed that metal artifacts were sufficiently suppressed by the proposed technique, and the proposed technique reduced the NRMSE by 12.34% and improved the SSIM by 0.84% compared to the O-MAR algorithm. In conclusion, the proposed model is able to efficiently reduce metal artifacts in CT images and has the superior performance compared with the commercial algorithm.

Citation Download Citation

Seungwan Lee, Seonghee Kang, Youngeun Choi, and Chanrok Park "Wavelet domain-based deep residual learning for metal artifact reduction in computed tomography", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129251Z (1 April 2024); https://doi.org/10.1117/12.3005636

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