Not applicable, Observational, Artifacts, Computer Applications-General, CT, eHealth, Computer applications, Artificial Intelligence, Artificial Intelligence and Machine Learning
M. Patwari1, R. Gutjahr2, R. Raupach3, A. Maier1; 1Erlangen/DE, 2Nürnberg, Deutschland/DE, 3Forchheim/DE
The purpose of this study was to evaluate the performance of denoising networks and the impact of including perceptual loss. We also studied whether the use of additional slices can help to improve the denoising performance of the network.
3D networks significantly outperform 2D networks in noise removal, however, 2D networks make up for it by better feature preservation after denoising. This indicates that using neighbouring slices for denoising helps in noise removal, however, it does not preserve features.
Using perceptual loss improves feature preservation for both 2D and 3D denoising networks. Perceptual loss also improves denoising peformance in the case of 3D networks. Therefore, we can conclude that including perceptual loss and neighboring slices results in better noise removal while preserving features.